US20190068466A1 - Technologies for auto-discovery of fault domains - Google Patents

Technologies for auto-discovery of fault domains Download PDF

Info

Publication number
US20190068466A1
US20190068466A1 US15/850,325 US201715850325A US2019068466A1 US 20190068466 A1 US20190068466 A1 US 20190068466A1 US 201715850325 A US201715850325 A US 201715850325A US 2019068466 A1 US2019068466 A1 US 2019068466A1
Authority
US
United States
Prior art keywords
sled
compute device
data
fault domain
sleds
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US15/850,325
Other languages
English (en)
Inventor
Anjaneya Reddy Chagam Reddy
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Intel Corp
Original Assignee
Intel Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Intel Corp filed Critical Intel Corp
Priority to US15/850,325 priority Critical patent/US20190068466A1/en
Assigned to INTEL CORPORATION reassignment INTEL CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: CHAGAM REDDY, ANJANEYA REDDY
Publication of US20190068466A1 publication Critical patent/US20190068466A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/16—Constructional details or arrangements
    • G06F1/18—Packaging or power distribution
    • G06F1/183—Internal mounting support structures, e.g. for printed circuit boards, internal connecting means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J15/00—Gripping heads and other end effectors
    • B25J15/0014—Gripping heads and other end effectors having fork, comb or plate shaped means for engaging the lower surface on a object to be transported
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/16—Constructional details or arrangements
    • G06F1/18—Packaging or power distribution
    • G06F1/189—Power distribution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/16—Constructional details or arrangements
    • G06F1/20—Cooling means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/30—Means for acting in the event of power-supply failure or interruption, e.g. power-supply fluctuations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3442—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for planning or managing the needed capacity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3466—Performance evaluation by tracing or monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F12/00—Accessing, addressing or allocating within memory systems or architectures
    • G06F12/02—Addressing or allocation; Relocation
    • G06F12/06—Addressing a physical block of locations, e.g. base addressing, module addressing, memory dedication
    • G06F12/0615—Address space extension
    • G06F12/0623—Address space extension for memory modules
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
    • G06F13/14—Handling requests for interconnection or transfer
    • G06F13/16—Handling requests for interconnection or transfer for access to memory bus
    • G06F13/1605—Handling requests for interconnection or transfer for access to memory bus based on arbitration
    • G06F13/1652—Handling requests for interconnection or transfer for access to memory bus based on arbitration in a multiprocessor architecture
    • G06F13/1657—Access to multiple memories
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
    • G06F13/14—Handling requests for interconnection or transfer
    • G06F13/16—Handling requests for interconnection or transfer for access to memory bus
    • G06F13/1668—Details of memory controller
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
    • G06F13/14—Handling requests for interconnection or transfer
    • G06F13/20—Handling requests for interconnection or transfer for access to input/output bus
    • G06F13/28—Handling requests for interconnection or transfer for access to input/output bus using burst mode transfer, e.g. direct memory access DMA, cycle steal
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
    • G06F13/14—Handling requests for interconnection or transfer
    • G06F13/20—Handling requests for interconnection or transfer for access to input/output bus
    • G06F13/28—Handling requests for interconnection or transfer for access to input/output bus using burst mode transfer, e.g. direct memory access DMA, cycle steal
    • G06F13/30—Handling requests for interconnection or transfer for access to input/output bus using burst mode transfer, e.g. direct memory access DMA, cycle steal with priority control
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
    • G06F13/38—Information transfer, e.g. on bus
    • G06F13/42—Bus transfer protocol, e.g. handshake; Synchronisation
    • G06F13/4204—Bus transfer protocol, e.g. handshake; Synchronisation on a parallel bus
    • G06F13/4221—Bus transfer protocol, e.g. handshake; Synchronisation on a parallel bus being an input/output bus, e.g. ISA bus, EISA bus, PCI bus, SCSI bus
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
    • G06F15/161—Computing infrastructure, e.g. computer clusters, blade chassis or hardware partitioning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/76—Architectures of general purpose stored program computers
    • G06F15/78—Architectures of general purpose stored program computers comprising a single central processing unit
    • G06F15/7807—System on chip, i.e. computer system on a single chip; System in package, i.e. computer system on one or more chips in a single package
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/76—Architectures of general purpose stored program computers
    • G06F15/78—Architectures of general purpose stored program computers comprising a single central processing unit
    • G06F15/7867—Architectures of general purpose stored program computers comprising a single central processing unit with reconfigurable architecture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5083—Techniques for rebalancing the load in a distributed system
    • G06F9/5088—Techniques for rebalancing the load in a distributed system involving task migration
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0813—Configuration setting characterised by the conditions triggering a change of settings
    • H04L41/0816—Configuration setting characterised by the conditions triggering a change of settings the condition being an adaptation, e.g. in response to network events
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0896—Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5003—Managing SLA; Interaction between SLA and QoS
    • H04L41/5019—Ensuring fulfilment of SLA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5003—Managing SLA; Interaction between SLA and QoS
    • H04L41/5019—Ensuring fulfilment of SLA
    • H04L41/5025—Ensuring fulfilment of SLA by proactively reacting to service quality change, e.g. by reconfiguration after service quality degradation or upgrade
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/06—Generation of reports
    • H04L43/065—Generation of reports related to network devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0876—Network utilisation, e.g. volume of load or congestion level
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/25—Flow control; Congestion control with rate being modified by the source upon detecting a change of network conditions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/70—Admission control; Resource allocation
    • H04L47/76—Admission control; Resource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions
    • H04L47/762—Admission control; Resource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions triggered by the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/70—Admission control; Resource allocation
    • H04L47/83—Admission control; Resource allocation based on usage prediction
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L49/00—Packet switching elements
    • H04L49/40—Constructional details, e.g. power supply, mechanical construction or backplane
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004—Server selection for load balancing
    • H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/16—Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q1/00—Details of selecting apparatus or arrangements
    • H04Q1/02—Constructional details
    • H04Q1/10—Exchange station construction
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00—Constructional details common to different types of electric apparatus
    • H05K7/14—Mounting supporting structure in casing or on frame or rack
    • H05K7/1485—Servers; Data center rooms, e.g. 19-inch computer racks
    • H05K7/1488—Cabinets therefor, e.g. chassis or racks or mechanical interfaces between blades and support structures
    • H05K7/1489—Cabinets therefor, e.g. chassis or racks or mechanical interfaces between blades and support structures characterized by the mounting of blades therein, e.g. brackets, rails, trays
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00—Constructional details common to different types of electric apparatus
    • H05K7/14—Mounting supporting structure in casing or on frame or rack
    • H05K7/1485—Servers; Data center rooms, e.g. 19-inch computer racks
    • H05K7/1498—Resource management, Optimisation arrangements, e.g. configuration, identification, tracking, physical location
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00—Constructional details common to different types of electric apparatus
    • H05K7/18—Construction of rack or frame
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00—Constructional details common to different types of electric apparatus
    • H05K7/20—Modifications to facilitate cooling, ventilating, or heating
    • H05K7/20009—Modifications to facilitate cooling, ventilating, or heating using a gaseous coolant in electronic enclosures
    • H05K7/20209—Thermal management, e.g. fan control
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00—Constructional details common to different types of electric apparatus
    • H05K7/20—Modifications to facilitate cooling, ventilating, or heating
    • H05K7/20709—Modifications to facilitate cooling, ventilating, or heating for server racks or cabinets; for data centers, e.g. 19-inch computer racks
    • H05K7/20718—Forced ventilation of a gaseous coolant
    • H05K7/20736—Forced ventilation of a gaseous coolant within cabinets for removing heat from server blades
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F13/00—Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
    • G06F13/38—Information transfer, e.g. on bus
    • G06F13/40—Bus structure
    • G06F13/4004—Coupling between buses
    • G06F13/4022—Coupling between buses using switching circuits, e.g. switching matrix, connection or expansion network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/10—Protecting distributed programs or content, e.g. vending or licensing of copyrighted material ; Digital rights management [DRM]
    • G06F21/105—Arrangements for software license management or administration, e.g. for managing licenses at corporate level
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2200/00—Indexing scheme relating to G06F1/04 - G06F1/32
    • G06F2200/20—Indexing scheme relating to G06F1/20
    • G06F2200/201—Cooling arrangements using cooling fluid
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/86—Event-based monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/885—Monitoring specific for caches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/485—Task life-cycle, e.g. stopping, restarting, resuming execution
    • G06F9/4856—Task life-cycle, e.g. stopping, restarting, resuming execution resumption being on a different machine, e.g. task migration, virtual machine migration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/04—Network management architectures or arrangements
    • H04L41/044—Network management architectures or arrangements comprising hierarchical management structures
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/16—Threshold monitoring
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks

Definitions

  • a cloud computing facility, data center, server farm, or any similar large-scale computer architecture often includes a large number of computer devices. These computer devices may be organized in various ways. For example, based on connectivity or usage, these computer devices will often be organized into groups by network type, application type, or the like. Additionally, computer devices have specific power requirements and are connected to one or more power source devices. These power source devices may draw power from various sources and may not all be interconnected. As a result, groups of power source devices (sometimes referred to as power zones) may be separate from other groups of power source devices. In the event of a power outage or other power loss incident, the impact on one power source device may affect multiple connected computer devices.
  • Known methods of maintaining an inventory of connections between computer devices, network devices, and power source devices include manual inventory of the computer devices that are connected to each power source device. This manual inventory may also be entered into a distributed data processing framework that performs data replication and other data management services for the computer devices.
  • FIG. 1 is a diagram of a conceptual overview of a data center in which one or more techniques described herein may be implemented according to various embodiments;
  • FIG. 2 is a diagram of an example embodiment of a logical configuration of a rack of the data center of FIG. 1 ;
  • FIG. 3 is a diagram of an example embodiment of another data center in which one or more techniques described herein may be implemented according to various embodiments;
  • FIG. 4 is a diagram of another example embodiment of a data center in which one or more techniques described herein may be implemented according to various embodiments;
  • FIG. 5 is a diagram of a connectivity scheme representative of link-layer connectivity that may be established among various sleds of the data centers of FIGS. 1, 3, and 4 ;
  • FIG. 6 is a diagram of a rack architecture that may be representative of an architecture of any particular one of the racks depicted in FIGS. 1-4 according to some embodiments;
  • FIG. 7 is a diagram of an example embodiment of a sled that may be used with the rack architecture of FIG. 6 ;
  • FIG. 8 is a diagram of an example embodiment of a rack architecture to provide support for sleds featuring expansion capabilities
  • FIG. 9 is a diagram of an example embodiment of a rack implemented according to the rack architecture of FIG. 8 ;
  • FIG. 10 is a diagram of an example embodiment of a sled designed for use in conjunction with the rack of FIG. 9 ;
  • FIG. 11 is a diagram of an example embodiment of a data center in which one or more techniques described herein may be implemented according to various embodiments;
  • FIG. 12 is a simplified block diagram of at least one embodiment of a compute device for automatic discovery of fault domains
  • FIG. 13 is a simplified block diagram of at least one embodiment of an environment established by the compute device of FIG. 12 ;
  • FIG. 14 is a simplified flow diagram of at least one embodiment of a method for automatic discovery of fault domains that may be executed by a sled shown in FIG. 16 ;
  • FIGS. 15 and 16 are a simplified flow diagram of at least one embodiment of a method for automatic discovery of fault domains that may be executed by the compute device of FIGS. 12-13 ;
  • FIG. 17 is a diagram of a datacenter or computing network that includes one or more sleds and other compute devices, any one of which may perform a method for automatic discovery of fault domains as shown in FIGS. 15 and 16 .
  • references in the specification to “one embodiment,” “an embodiment,” “an illustrative embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include that particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
  • items included in a list in the form of “at least one A, B, and C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).
  • items listed in the form of “at least one of A, B, or C” can mean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).
  • the disclosed embodiments may be implemented, in some cases, in hardware, firmware, software, or any combination thereof.
  • the disclosed embodiments may also be implemented as instructions carried by or stored on a transitory or non-transitory machine-readable (e.g., computer-readable) storage medium, which may be read and executed by one or more processors.
  • a machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disc, or other media device).
  • FIG. 1 illustrates a conceptual overview of a data center 100 that may generally be representative of a data center or other type of computing network in/for which one or more techniques described herein may be implemented according to various embodiments.
  • data center 100 may generally contain a plurality of racks, each of which may house computing equipment comprising a respective set of physical resources.
  • data center 100 contains four racks 102 A to 102 D, which house computing equipment comprising respective sets of physical resources 105 A to 105 D.
  • a collective set of physical resources 106 of data center 100 includes the various sets of physical resources 105 A to 105 D that are distributed among racks 102 A to 102 D.
  • Physical resources 106 may include resources of multiple types, such as—for example—processors, co-processors, accelerators, field-programmable gate arrays (FPGAs), memory, and storage. The embodiments are not limited to these examples.
  • the illustrative data center 100 differs from typical data centers in many ways.
  • the circuit boards (“sleds”) on which components such as CPUs, memory, and other components are placed are designed for increased thermal performance
  • the sleds are shallower than typical boards. In other words, the sleds are shorter from the front to the back, where cooling fans are located. This decreases the length of the path that air must to travel across the components on the board.
  • the components on the sled are spaced further apart than in typical circuit boards, and the components are arranged to reduce or eliminate shadowing (i.e., one component in the air flow path of another component).
  • processing components such as the processors are located on a top side of a sled while near memory, such as dual inline memory modules (DIMMs), are located on a bottom side of the sled.
  • DIMMs dual inline memory modules
  • the components may operate at higher frequencies and power levels than in typical systems, thereby increasing performance
  • the sleds are configured to blindly mate with power and data communication cables in each rack 102 A, 102 B, 102 C, 102 D, enhancing their ability to be quickly removed, upgraded, reinstalled, and/or replaced.
  • individual components located on the sleds such as processors, accelerators, memory, and data storage drives, are configured to be easily upgraded due to their increased spacing from each other.
  • the components additionally include hardware attestation features to prove their authenticity.
  • the data center 100 utilizes a single network architecture (“fabric”) that supports multiple other network architectures including Ethernet and Omni-Path.
  • the sleds in the illustrative embodiment, are coupled to switches via optical fibers, which provide higher bandwidth and lower latency than typical twisted pair cabling (e.g., Category 5, Category 5e, Category 6, etc.).
  • the data center 100 may, in use, pool resources, such as memory, accelerators (e.g., graphics accelerators, FPGAs, application-specific integrated circuits (ASICs), etc.), and data storage drives that are physically disaggregated, and provide them to compute resources (e.g., processors) on an as needed basis, enabling the compute resources to access the pooled resources as if they were local.
  • the illustrative data center 100 additionally receives usage information for the various resources, predicts resource usage for different types of workloads based on past resource usage, and dynamically reallocates the resources based on this information.
  • the racks 102 A, 102 B, 102 C, 102 D of the data center 100 may include physical design features that facilitate the automation of a variety of types of maintenance tasks.
  • data center 100 may be implemented using racks that are designed to be robotically-accessed, and to accept and house robotically-manipulatable resource sleds.
  • the racks 102 A, 102 B, 102 C, 102 D include integrated power sources that receive a greater voltage than is typical for power sources. The increased voltage enables the power sources to provide additional power to the components on each sled, enabling the components to operate at higher than typical frequencies.
  • FIG. 2 illustrates an exemplary logical configuration of a rack 202 of the data center 100 .
  • rack 202 may generally house a plurality of sleds, each of which may comprise a respective set of physical resources.
  • rack 202 houses sleds 204 - 1 to 204 - 4 comprising respective sets of physical resources 205 - 1 to 205 - 4 , each of which constitutes a portion of the collective set of physical resources 206 comprised in rack 202 .
  • rack 202 is representative of—for example—rack 102 A
  • physical resources 206 may correspond to the physical resources 105 A comprised in rack 102 A.
  • physical resources 105 A may thus be made up of the respective sets of physical resources, including physical storage resources 205 - 1 , physical accelerator resources 205 - 2 , physical memory resources 205 - 3 , and physical compute resources 205 - 5 comprised in the sleds 204 - 1 to 204 - 4 of rack 202 .
  • the embodiments are not limited to this example.
  • Each sled may contain a pool of each of the various types of physical resources (e.g., compute, memory, accelerator, storage).
  • robotically accessible and robotically manipulatable sleds comprising disaggregated resources, each type of resource can be upgraded independently of each other and at their own optimized refresh rate.
  • FIG. 3 illustrates an example of a data center 300 that may generally be representative of one in/for which one or more techniques described herein may be implemented according to various embodiments.
  • data center 300 comprises racks 302 - 1 to 302 - 32 .
  • the racks of data center 300 may be arranged in such fashion as to define and/or accommodate various access pathways.
  • the racks of data center 300 may be arranged in such fashion as to define and/or accommodate access pathways 311 A, 311 B, 311 C, and 311 D.
  • the presence of such access pathways may generally enable automated maintenance equipment, such as robotic maintenance equipment, to physically access the computing equipment housed in the various racks of data center 300 and perform automated maintenance tasks (e.g., replace a failed sled, upgrade a sled).
  • automated maintenance equipment such as robotic maintenance equipment
  • the dimensions of access pathways 311 A, 311 B, 311 C, and 311 D, the dimensions of racks 302 - 1 to 302 - 32 , and/or one or more other aspects of the physical layout of data center 300 may be selected to facilitate such automated operations. The embodiments are not limited in this context.
  • FIG. 4 illustrates an example of a data center 400 that may generally be representative of one in/for which one or more techniques described herein may be implemented according to various embodiments.
  • data center 400 may feature an optical fabric 412 .
  • Optical fabric 412 may generally comprise a combination of optical signaling media (such as optical cabling) and optical switching infrastructure via which any particular sled in data center 400 can send signals to (and receive signals from) each of the other sleds in data center 400 .
  • the signaling connectivity that optical fabric 412 provides to any given sled may include connectivity both to other sleds in a same rack and sleds in other racks. In the particular non-limiting example depicted in FIG.
  • data center 400 includes four racks 402 A to 402 D.
  • Racks 402 A to 402 D house respective pairs of sleds 404 A- 1 and 404 A- 2 , 404 B- 1 and 404 B- 2 , 404 C- 1 and 404 C- 2 , and 404 D- 1 and 404 D- 2 .
  • data center 400 comprises a total of eight sleds. Via optical fabric 412 , each such sled may possess signaling connectivity with each of the seven other sleds in data center 400 .
  • sled 404 A- 1 in rack 402 A may possess signaling connectivity with sled 404 A- 2 in rack 402 A, as well as the six other sleds 404 B- 1 , 404 B- 2 , 404 C- 1 , 404 C- 2 , 404 D- 1 , and 404 D- 2 that are distributed among the other racks 402 B, 402 C, and 402 D of data center 400 .
  • the embodiments are not limited to this example.
  • FIG. 5 illustrates an overview of a connectivity scheme 500 that may generally be representative of link-layer connectivity that may be established in some embodiments among the various sleds of a data center, such as any of example data centers 100 , 300 , and 400 of FIGS. 1, 3, and 4 .
  • Connectivity scheme 500 may be implemented using an optical fabric that features a dual-mode optical switching infrastructure 514 .
  • Dual-mode optical switching infrastructure 514 may generally comprise a switching infrastructure that is capable of receiving communications according to multiple link-layer protocols via a same unified set of optical signaling media, and properly switching such communications.
  • dual-mode optical switching infrastructure 514 may be implemented using one or more dual-mode optical switches 515 .
  • dual-mode optical switches 515 may generally comprise high-radix switches.
  • dual-mode optical switches 515 may comprise multi-ply switches, such as four-ply switches. In various embodiments, dual-mode optical switches 515 may feature integrated silicon photonics that enable them to switch communications with significantly reduced latency in comparison to conventional switching devices. In some embodiments, dual-mode optical switches 515 may constitute leaf switches 530 in a leaf-spine architecture additionally including one or more dual-mode optical spine switches 520 .
  • dual-mode optical switches may be capable of receiving both Ethernet protocol communications carrying Internet Protocol (IP packets) and communications according to a second, high-performance computing (HPC) link-layer protocol (e.g., Intel's Omni-Path Architecture's, Infiniband) via optical signaling media of an optical fabric.
  • HPC high-performance computing
  • connectivity scheme 500 may thus provide support for link-layer connectivity via both Ethernet links and HPC links.
  • both Ethernet and HPC communications can be supported by a single high-bandwidth, low-latency switch fabric.
  • the embodiments are not limited to this example.
  • FIG. 6 illustrates a general overview of a rack architecture 600 that may be representative of an architecture of any particular one of the racks depicted in FIGS. 1 to 4 according to some embodiments.
  • rack architecture 600 may generally feature a plurality of sled spaces into which sleds may be inserted, each of which may be robotically-accessible via a rack access region 601 .
  • rack architecture 600 features five sled spaces 603 - 1 to 603 - 5 .
  • Sled spaces 603 - 1 to 603 - 5 feature respective multi-purpose connector modules (MPCMs) 616 - 1 to 616 - 5 .
  • MPCMs multi-purpose connector modules
  • FIG. 7 illustrates an example of a sled 704 that may be representative of a sled of such a type.
  • sled 704 may comprise a set of physical resources 705 , as well as an MPCM 716 designed to couple with a counterpart MPCM when sled 704 is inserted into a sled space such as any of sled spaces 603 - 1 to 603 - 5 of FIG. 6 .
  • Sled 704 may also feature an expansion connector 717 .
  • Expansion connector 717 may generally comprise a socket, slot, or other type of connection element that is capable of accepting one or more types of expansion modules, such as an expansion sled 718 .
  • expansion connector 717 may provide physical resources 705 with access to supplemental computing resources 705 B residing on expansion sled 718 .
  • the embodiments are not limited in this context.
  • FIG. 8 illustrates an example of a rack architecture 800 that may be representative of a rack architecture that may be implemented in order to provide support for sleds featuring expansion capabilities, such as sled 704 of FIG. 7 .
  • rack architecture 800 includes seven sled spaces 803 - 1 to 803 - 7 , which feature respective MPCMs 816 - 1 to 816 - 7 .
  • Sled spaces 803 - 1 to 803 - 7 include respective primary regions 803 - 1 A to 803 - 7 A and respective expansion regions 803 - 1 B to 803 - 7 B.
  • the primary region may generally constitute a region of the sled space that physically accommodates the inserted sled.
  • the expansion region may generally constitute a region of the sled space that can physically accommodate an expansion module, such as expansion sled 718 of FIG. 7 , in the event that the inserted sled is configured with such a module.
  • FIG. 9 illustrates an example of a rack 902 that may be representative of a rack implemented according to rack architecture 800 of FIG. 8 according to some embodiments.
  • rack 902 features seven sled spaces 903 - 1 to 903 - 7 , which include respective primary regions 903 - 1 A to 903 - 7 A and respective expansion regions 903 - 1 B to 903 - 7 B.
  • temperature control in rack 902 may be implemented using an air cooling system.
  • rack 902 may feature a plurality of fans 919 that are generally arranged to provide air cooling within the various sled spaces 903 - 1 to 903 - 7 .
  • the height of the sled space is greater than the conventional “1U” server height.
  • fans 919 may generally comprise relatively slow, large diameter cooling fans as compared to fans used in conventional rack configurations. Running larger diameter cooling fans at lower speeds may increase fan lifetime relative to smaller diameter cooling fans running at higher speeds while still providing the same amount of cooling.
  • the sleds are physically shallower than conventional rack dimensions. Further, components are arranged on each sled to reduce thermal shadowing (i.e., not arranged serially in the direction of air flow).
  • the wider, shallower sleds allow for an increase in device performance because the devices can be operated at a higher thermal envelope (e.g., 250 W) due to improved cooling (i.e., no thermal shadowing, more space between devices, more room for larger heat sinks, etc.).
  • a higher thermal envelope e.g. 250 W
  • improved cooling i.e., no thermal shadowing, more space between devices, more room for larger heat sinks, etc.
  • MPCMs 916 - 1 to 916 - 7 may be configured to provide inserted sleds with access to power sourced by respective power modules 920 - 1 to 920 - 7 , each of which may draw power from an external power source 921 .
  • external power source 921 may deliver alternating current (AC) power to rack 902
  • power modules 920 - 1 to 920 - 7 may be configured to convert such AC power to direct current (DC) power to be sourced to inserted sleds.
  • power modules 920 - 1 to 920 - 7 may be configured to convert 277-volt AC power into 12-volt DC power for provision to inserted sleds via respective MPCMs 916 - 1 to 916 - 7 .
  • the embodiments are not limited to this example.
  • MPCMs 916 - 1 to 916 - 7 may also be arranged to provide inserted sleds with optical signaling connectivity to a dual-mode optical switching infrastructure 914 , which may be the same as—or similar to—dual-mode optical switching infrastructure 514 of FIG. 5 .
  • optical connectors contained in MPCMs 916 - 1 to 916 - 7 may be designed to couple with counterpart optical connectors contained in MPCMs of inserted sleds to provide such sleds with optical signaling connectivity to dual-mode optical switching infrastructure 914 via respective lengths of optical cabling 922 - 1 to 922 - 7 .
  • each such length of optical cabling may extend from its corresponding MPCM to an optical interconnect loom 923 that is external to the sled spaces of rack 902 .
  • optical interconnect loom 923 may be arranged to pass through a support post or other type of load-bearing element of rack 902 . The embodiments are not limited in this context. Because inserted sleds connect to an optical switching infrastructure via MPCMs, the resources typically spent in manually configuring the rack cabling to accommodate a newly inserted sled can be saved.
  • FIG. 10 illustrates an example of a sled 1004 that may be representative of a sled designed for use in conjunction with rack 902 of FIG. 9 according to some embodiments.
  • Sled 1004 may feature an MPCM 1016 that comprises an optical connector 1016 A and a power connector 1016 B, and that is designed to couple with a counterpart MPCM of a sled space in conjunction with insertion of MPCM 1016 into that sled space. Coupling MPCM 1016 with such a counterpart MPCM may cause power connector 1016 to couple with a power connector comprised in the counterpart MPCM. This may generally enable physical resources 1005 of sled 1004 to source power from an external source, via power connector 1016 and power transmission media 1024 that conductively couples power connector 1016 to physical resources 1005 .
  • Dual-mode optical network interface circuitry 1026 may generally comprise circuitry that is capable of communicating over optical signaling media according to each of multiple link-layer protocols supported by dual-mode optical switching infrastructure 914 of FIG. 9 .
  • dual-mode optical network interface circuitry 1026 may be capable both of Ethernet protocol communications and of communications according to a second, high-performance protocol.
  • dual-mode optical network interface circuitry 1026 may include one or more optical transceiver modules 1027 , each of which may be capable of transmitting and receiving optical signals over each of one or more optical channels. The embodiments are not limited in this context.
  • Coupling MPCM 1016 with a counterpart MPCM of a sled space in a given rack may cause optical connector 1016 A to couple with an optical connector comprised in the counterpart MPCM.
  • This may generally establish optical connectivity between optical cabling of the sled and dual-mode optical network interface circuitry 1026 , via each of a set of optical channels 1025 .
  • Dual-mode optical network interface circuitry 1026 may communicate with the physical resources 1005 of sled 1004 via electrical signaling media 1028 .
  • a relatively higher thermal envelope e.g. 250 W
  • a sled may include one or more additional features to facilitate air cooling, such as a heat pipe and/or heat sinks arranged to dissipate heat generated by physical resources 1005 .
  • additional features such as a heat pipe and/or heat sinks arranged to dissipate heat generated by physical resources 1005 .
  • any given sled that features the design elements of sled 1004 may also feature an expansion connector according to some embodiments. The embodiments are not limited in this context.
  • FIG. 11 illustrates an example of a data center 1100 that may generally be representative of one in/for which one or more techniques described herein may be implemented according to various embodiments.
  • a physical infrastructure management framework 1150 A may be implemented to facilitate management of a physical infrastructure 1100 A of data center 1100 .
  • one function of physical infrastructure management framework 1150 A may be to manage automated maintenance functions within data center 1100 , such as the use of robotic maintenance equipment to service computing equipment within physical infrastructure 1100 A.
  • physical infrastructure 1100 A may feature an advanced telemetry system that performs telemetry reporting that is sufficiently robust to support remote automated management of physical infrastructure 1100 A.
  • telemetry information provided by such an advanced telemetry system may support features such as failure prediction/prevention capabilities and capacity planning capabilities.
  • physical infrastructure management framework 1150 A may also be configured to manage authentication of physical infrastructure components using hardware attestation techniques. For example, robots may verify the authenticity of components before installation by analyzing information collected from a radio frequency identification (RFID) tag associated with each component to be installed.
  • RFID radio frequency identification
  • the physical infrastructure 1100 A of data center 1100 may comprise an optical fabric 1112 , which may include a dual-mode optical switching infrastructure 1114 .
  • Optical fabric 1112 and dual-mode optical switching infrastructure 1114 may be the same as—or similar to—optical fabric 412 of FIG. 4 and dual-mode optical switching infrastructure 514 of FIG. 5 , respectively, and may provide high-bandwidth, low-latency, multi-protocol connectivity among sleds of data center 1100 .
  • the availability of such connectivity may make it feasible to disaggregate and dynamically pool resources such as accelerators, memory, and storage.
  • one or more pooled accelerator sleds 1130 may be included among the physical infrastructure 1100 A of data center 1100 , each of which may comprise a pool of accelerator resources—such as co-processors and/or FPGAs, for example—that is globally accessible to other sleds via optical fabric 1112 and dual-mode optical switching infrastructure 1114 .
  • accelerator resources such as co-processors and/or FPGAs, for example
  • one or more pooled storage sleds 1132 may be included among the physical infrastructure 1100 A of data center 1100 , each of which may comprise a pool of storage resources that is available globally accessible to other sleds via optical fabric 1112 and dual-mode optical switching infrastructure 1114 .
  • such pooled storage sleds 1132 may comprise pools of solid-state storage devices such as solid-state drives (SSDs).
  • SSDs solid-state drives
  • one or more high-performance processing sleds 1134 may be included among the physical infrastructure 1100 A of data center 1100 .
  • high-performance processing sleds 1134 may comprise pools of high-performance processors, as well as cooling features that enhance air cooling to yield a higher thermal envelope of up to 250 W or more.
  • any given high-performance processing sled 1134 may feature an expansion connector 1117 that can accept a far memory expansion sled, such that the far memory that is locally available to that high-performance processing sled 1134 is disaggregated from the processors and near memory comprised on that sled.
  • such a high-performance processing sled 1134 may be configured with far memory using an expansion sled that comprises low-latency SSD storage.
  • the optical infrastructure allows for compute resources on one sled to utilize remote accelerator/FPGA, memory, and/or SSD resources that are disaggregated on a sled located on the same rack or any other rack in the data center.
  • the remote resources can be located one switch jump away or two-switch jumps away in the spine-leaf network architecture described above with reference to FIG. 5 .
  • the embodiments are not limited in this context.
  • one or more layers of abstraction may be applied to the physical resources of physical infrastructure 1100 A in order to define a virtual infrastructure, such as a software-defined infrastructure 1100 B.
  • virtual computing resources 1136 of software-defined infrastructure 1100 B may be allocated to support the provision of cloud services 1140 .
  • particular sets of virtual computing resources 1136 may be grouped for provision to cloud services 1140 in the form of SDI services 1138 .
  • cloud services 1140 may include—without limitation—software as a service (SaaS) services 1142 , platform as a service (PaaS) services 1144 , and infrastructure as a service (IaaS) services 1146 .
  • management of software-defined infrastructure 1100 B may be conducted using a virtual infrastructure management framework 1150 B.
  • virtual infrastructure management framework 1150 B may be designed to implement workload fingerprinting techniques and/or machine-learning techniques in conjunction with managing allocation of virtual computing resources 1136 and/or SDI services 1138 to cloud services 1140 .
  • virtual infrastructure management framework 1150 B may use/consult telemetry data in conjunction with performing such resource allocation.
  • an application/service management framework 1150 C may be implemented in order to provide quality of service (QoS) management capabilities for cloud services 1140 .
  • QoS quality of service
  • an illustrative compute device 1200 for automatic discovery of fault domains includes a processor 1220 , an input/output (I/O) subsystem 1222 , a memory 1224 , and a data storage device 1226 .
  • the compute device 1200 may be embodied as server computer, a rack server, a blade server, a compute node, and/or a sled in a data center, such as a sled 204 as described above in connection with FIG. 2 , a sled of the physical infrastructure 1100 A as described above in connection with FIG. 11 , or another sled of the data center.
  • the processor 1220 may be embodied as any type of processor capable of performing the functions described herein.
  • the processor 1220 may be embodied as a single or multi-core processor(s), digital signal processor, microcontroller, or other processor or processing/controlling circuit.
  • the memory 1224 may be embodied as any type of volatile or non-volatile memory or data storage capable of performing the functions described herein. In operation, the memory 1224 may store various data and software used during operation of the compute device 1200 such operating systems, applications, programs, libraries, and drivers.
  • the memory 1224 is communicatively coupled to the processor 1220 via the I/O subsystem 1222 , which may be embodied as circuitry and/or components to facilitate input/output operations with the processor 1220 , the memory 1224 , and other components of the compute device 1200 .
  • the I/O subsystem 1222 may be embodied as, or otherwise include, memory controller hubs, input/output control hubs, sensor hubs, firmware devices, communication links (i.e., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.) and/or other components and subsystems to facilitate the input/output operations.
  • the I/O subsystem 1222 may form a portion of a system-on-a-chip (SoC) and be incorporated, along with the processor 1220 , the memory 1224 , and other components of the compute device 1200 , on a single integrated circuit chip.
  • SoC system-on-a-chip
  • the data storage device 1226 may be embodied as any type of device or devices configured for short-term or long-term storage of data such as, for example, memory devices and circuits, memory cards, hard disk drives, solid-state drives, non-volatile flash memory, or other data storage devices.
  • the compute device 1200 may also include a communications circuitry 1228 , which may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications between the compute device 1200 and other remote devices over a computer network (not shown).
  • the communications circuitry 1228 may be configured to use any one or more communication technology (e.g., wired or wireless communications) and associated protocols (e.g., Ethernet, InfiniBand®, Bluetooth®, Wi-Fi®, WiMAX, 3G, 4G LTE, etc.) to effect such communication.
  • communication technology e.g., wired or wireless communications
  • associated protocols e.g., Ethernet, InfiniBand®, Bluetooth®, Wi-Fi®, WiMAX, 3G, 4G LTE, etc.
  • the compute device 1200 may further include one or more peripheral devices 1232 .
  • the peripheral devices 1232 may include any number of additional input/output devices, interface devices, hardware accelerators, and/or other peripheral devices.
  • the peripheral devices 1232 may include a touch screen, graphics circuitry, a graphical processing unit (GPU) and/or processor graphics, an audio device, a microphone, a camera, a keyboard, a mouse, a network interface, and/or other input/output devices, interface devices, and/or peripheral devices.
  • GPU graphical processing unit
  • the compute device 1200 may establish an environment 1300 during operation.
  • the illustrative environment 1300 includes a fault domain manager 1302 and a distributed processing adapter 1304 .
  • Each of the components of the environment 1300 may be embodied as hardware, firmware, software, or a combination thereof.
  • one or more of the components of the environment 1300 may be embodied as circuitry or a collection of electrical devices (e.g., fault domain manager 1302 circuitry and distributed processing adapter 1304 circuitry, etc.).
  • one or more of the fault domain manager 1302 circuitry and distributed processing adapter 1304 circuitry may form a portion of one or more of the compute device 1200 , the communication circuitry 1228 , the I/O subsystem 1222 , and/or other components of the compute device 1200 .
  • the illustrative environment 1300 includes fault domain data 1306 which may be embodied as any data indicative of rack data, datacenter manager computer data, network connectivity data, power source device data, power zone data, or the like.
  • the illustrative environment 1300 further includes sled data 1308 , which may be embodied as any data indicative of sled identification data, which may further include data regarding compute devices connected to a particular sled (e.g., a sled 1718 as described in greater detail below with respect to FIG. 17 ).
  • sled identification data may include identifiers for power source devices, rack compute devices, top-of-rack switch devices, and any other devices directly or indirectly connected to a sled.
  • Sled identification data may further include data regarding sled capabilities, manufacturer and device type/class identifiers, or the like.
  • Sled data may further include sled health data.
  • sled health data may be embodied as any data indicative of a number of sockets, a number of cores, a number of drives, a latency metric, a reliability metric, and a memory capacity metric associated with the sled.
  • Sled health data may also be embodied as any data indicative of other metrics such as sled performance data, sled performance history data, or the like.
  • both sled identification data and sled health data may be embodied as any data indicative or particular qualities or capabilities of the sled.
  • sled data for a compute sled may be embodied as any data indicative of a number of sockets, a number of cores, speed, memory types, memory modes, memory capacity and memory performance, NIC data or the like for the compute sled.
  • sled data for a storage sled may be embodied as any data indicative of a number of drives, drive information (e.g. performance, latency, bandwidth ⁇ , capacity, endurance, reliability (e.g., replication metrics), security, or the like.
  • Sled data for an accelerator sled may be embodied as any data indicative of a number of accelerator devices (e.g., ASICs, FPGAs), accelerator device performance, an accelerator gate count, identifiers related to bitstreams of data input or output from the accelerator device, network address data, health data, or the like.
  • Sled data for a network sled may be embodied as any data indicative of a number of ports, per port performance, network configuration, port connectivity, health data, or the like.
  • the fault domain manager 1302 which may be embodied as hardware, firmware, software, virtualized hardware, emulated architecture, and/or a combination thereof as discussed above, is configured to receive sled data for at least one sled in a computer network.
  • the fault domain manager 1302 may receive sled data for one of the sleds 1716 that are described in greater detail below with respect to FIG. 17 .
  • sled data may include one or more of sled identification data and sled health data.
  • the fault domain manager 1302 receives sled data each time a sled boots up.
  • each sled may be configured to transmit sled data to a connected fault domain manager 1302 at boot time.
  • the sled employs the link layer discovery protocol (LLDP) to generate sled data information.
  • LLDP refers to a network protocol that can be used by a device to advertise its identity, capabilities, and neighbors. When activated, LLDP is used by a device to send messages (also referred as advertisements) that include data that may be sent in the form of an Ethernet frame. Each frame contains one LLDP Data Unit (LLDPDU). Each LLDPDU is a sequence of type-length-value (TLV) structures.
  • the LLDP is extended to enable a sled to provide sled data as described herein.
  • the fault domain manager 1302 is configured to receive one or more LLDPUs, some of which will contain one or more items of sled data as described above.
  • the fault domain manager 1302 is also configured to parse the sled identification data to identify a power zone.
  • a datacenter may be organized into one or more power zones, each of which may include a group of power source devices (e.g., main power lines, unlimited power supply devices, backup generator devices, or the like). Additionally, each power zone is associated with one or more sleds (e.g., power zone A 1710 as described below with respect to FIG. 17 ).
  • One power zone may be associated with multiple sleds. In other words, power source devices grouped into one power zone may be supplying power services to multiple sleds. Accordingly, the fault domain manager 1302 identifies and stores data regarding the power zone associated with a sled.
  • the fault domain manager receives sled data for multiple sleds and parses sled identification data for each sled.
  • the fault domain manager 1302 is also configured to collate or otherwise group sleds by power zone identifier.
  • the fault domain manager 1302 is configured to group all sleds that are associated with a particular power zone.
  • the fault domain manager 1302 is configured to perform this discovery process for each sled for which sled data is available.
  • the fault domain manager 1302 is also configured to generate a fault domain mapping using the identified power zone. More specifically, the fault domain manager 1302 generates a fault domain mapping that includes the power zone.
  • a fault domain may describe the structure or organization of the group of compute devices that are associated with a particular power zone.
  • the fault domain may include rack devices, switch devices, power source devices, and sleds.
  • the fault domain mapping may be embodied as any data indicative of interconnections between rack devices, switch devices, power source devices, and sleds.
  • the sled identification data may be embodied as any data indicative of the top-of-rack switches that a sled is connected to, as well as the rack device(s) that each of the identified top-of-rack switches is connected to.
  • the fault domain manager 1302 is also configured to store the generated fault domain mapping in a data structure.
  • the fault domain manager 1302 may store the fault domain mapping in a tree structure, with a power zone identifier represented by a root or parent node with rack devices represented as child nodes, switches represented as child nodes of the rack device nodes, and sleds represented as child nodes of the switch device nodes.
  • the fault domain manager 1302 may store the fault domain mapping in a graph database, with each device stored as a node object and relationship data stored in edge objects (e.g., top-of-rack switch X connects to sled Y and sled Z).
  • the distributed processing adapter 1304 which may be embodied as hardware, firmware, software, virtualized hardware, emulated architecture, and/or a combination thereof, is configured to convert the generated fault domain mapping into a consumable fault domain mapping that is consumable by a distributed processing software system (sometimes also referred to as a “distributed storage software system”). More specifically, the distributed processing adapter adapts the fault domain mapping data (e.g., that is stored in a data structure as described above) and programmatically converts into a particular format. The format may be determined based on the target distributed processing software system.
  • distributed processing software system refers to any software or software framework used for distributed storage and processing of large datasets.
  • a distributed processing software system includes a storage component, one that may be embodied as a distributed file system, for example.
  • a distributed processing software system includes a processing component to process the data from the file system.
  • a programming model and/or schema may be provided that provides interfaces for object-, block- and file-level storage.
  • Distributed processing software systems often enable processing of large datasets by distributing the large dataset into smaller chunks across multiple processing nodes, delivering packaged processing code to those nodes, and processing these chunks in parallel.
  • a Hadoop distributed file system may be able to consume the fault domain mapping data if it is formatted for consumption by code written in the Java® programming language (JAVA is a registered trademark of the Oracle Corporation, located at Redwood Shores, Redwood City, Calif., USA).
  • Java® programming language Java® programming language
  • the distributed processing adapter 1304 formats the fault domain mapping data into a format consumable by a Hadoop architecture. After conversion, the distributed processing adapter 1304 provides the fault domain mapping to the distributed processing software system.
  • Ceph is a registered trademark of Red Hat Inc., located at Raleigh, N.C., USA.
  • Cassandra is a registered trademark of Apache Software Foundation, located at Forest Hill, Md., USA.
  • MongoDB is a registered trademark of MongoDB Inc., located at Palo Alto, Calif., United States.
  • each of the fault domain manager 1302 and the distributed processing adapter 1304 may be separately embodied as hardware, firmware, software, virtualized hardware, emulated architecture, and/or a combination thereof.
  • the distributed processing adapter 1304 may be embodied as a hardware component, while the fault domain manager 1302 is embodied as a virtualized hardware component or as some other combination of hardware, firmware, software, virtualized hardware, emulated architecture, and/or a combination thereof.
  • a sled 1718 (as described in greater detail below with respect to FIG. 17 ) may establish an environment similar to the environment 1300 described above.
  • a sled 1718 may execute a method 1400 for automatic discovery of fault domains. More specifically, FIG. 14 describes the processes a sled 1718 executes to generate sled data that will later be used by, for example, the fault domain manager 1302 .
  • the method 1400 begins in block 1402 , in which the sled 1718 initiates a sled boot process.
  • the sled 1718 is configured to, for example, initiate loading of operating system program loading, as in block 1404 .
  • the sled 1718 performs a sled data discovery process.
  • sled data discovery is performed using an LLDP protocol extension.
  • LLDP is used to transmit advertisements by a device including data regarding the device's identity, capabilities, neighbors, and the like.
  • the sled 1718 discovers its capabilities (for example, those described above with respect to sled identification data and sled health data).
  • the sled 1718 discovers the sled 1718 's position (e.g., with respect to other sleds and other devices).
  • the sled 1718 may be a member of a group of compute sleds, accelerator sleds, or other sleds loaded in a single rack.
  • the sled 1718 discovers the rack device(s), switch device(s), and power source devices that it is directly or indirectly connected to.
  • the sled 1718 queries connected devices with specific queries regarding rack, switch, and power source devices that are associated with the sled 1718 .
  • the sled 1718 itself may receive, for example, LLDP advertisements that bear identification data for other devices.
  • a rack device may send the sled 1718 an LLDP advertisement indicating to the sled 1718 that the sled 1718 is connected to that rack device.
  • the sled 1718 is configured to perform discovery protocols (e.g., LLDP) across switch domains.
  • the sled 1718 may be configured to receive advertisements from specific devices.
  • a compute device may be configured during installation to transmit data to a specific sled (or any compute device).
  • the LLDP protocol (which may be disabled by default) will be activated on a specific compute device.
  • the compute device e.g., a rack device
  • the compute device may be configured during installation to transmit periodic LLDP advertisements to all connected switch devices, which are, in turn, configured during installation to transmit periodic LLDP advertisements to all connected sleds.
  • FIG. 17 a diagram 1700 illustrates a potential connectivity between rack devices, switch devices, and sleds. This connectivity may be advertised to a particular sled 1718 using the methods illustrated above.
  • the sled 1718 transmits the discovered sled data to a fault domain manager 1302 .
  • the fault domain manager 1302 may be a sub-component of the sled 1718 that is executing method 1400 .
  • the generated sled data will be passed to the fault domain manager 1302 of the sled 1718 .
  • the fault domain manager 1302 may be a component of another sled or compute device, in which case the sled 1718 transmits the generated sled data to the remote fault domain manager 1302 located on another compute device.
  • the compute device 1200 may execute a method 1500 for automatic discovery of fault domains. It should be appreciated that, in some embodiments, the operations of the method 1500 may be performed by one or more components of the compute device 1200 as shown in FIG. 13 , such as a processor 1220 . More specifically, the processor 1220 may execute (in hardware or software) at least one of the fault domain manager 1302 and the distributed processing adapter 1304 . The method 1500 may also be performed by one or more components of a sled 1718 as shown in FIG. 17 .
  • the method 1500 begins in block 1502 , in which the compute device 1200 checks for sled boot up. As described above, method 1500 may be performed by a sled 1718 which in turn detects whether it has been booted up. If method 1500 is performed by another compute device 1200 , the compute device 1200 is configured to check monitoring applications that monitor sled activity and transmit alerts to the compute device 1200 , as shown in block 1504 . In block 1506 , the compute device 1200 receives sled boot up confirmation (e.g., directly from a sled 1718 or from a monitoring application). In block 1508 , the compute device 1200 receives sled data from a sled 1718 . Referring now to FIG.
  • a diagram 1700 illustrates one potential computing network in which multiple sleds are interconnected with various other types of devices. Any one of the devices illustrated in FIG. 17 may represent the compute device 1200 and may perform method 1500 and receive sled data for one or more of sleds 1718 .
  • the compute device 1200 receives sled identification data (as described above with respect to sled data 1308 ). Similarly, in block 1512 , the compute device 1200 receives sled health data (as described above with respect to sled data 1308 ). In block 1514 , the compute device 1200 parses sled data to identify at least one power zone corresponding to the sled 1718 . More specifically, the compute device 1200 identifies at least one rack device, switch device, and/or power source device connected directly or indirectly to the sled 1718 .
  • the compute device 1200 is configured to parse the sled data by reading each LLDPU and extracting one or more type-length-value structures (TLVs) inside the LLDPU that represent one or more items of sled data.
  • TLVs type-length-value structures
  • one TLV may be (2: 9 bits: 1234), where type 2 represents the data type (e.g., Port ID), length 9 bits represents the length of the value, and 1234 represents the value itself (e.g., a port identifier for a port associated with a sled 1718 ).
  • the compute device 1200 is also configured to parse specific types of incoming sled data.
  • the compute device 1200 parses storage sled data, which may be embodied as any data indicative of a number of drives, drive information (e.g. performance, latency, bandwidth), capacity, endurance, reliability (e.g., replication metrics), security, or the like.
  • the compute device 1200 parses compute sled data, which may be embodied as any data indicative of a number of sockets, a number of cores, speed, memory types, memory modes, memory capacity and memory performance, NIC data or the like.
  • the compute device 1200 parses accelerator sled data, which may be embodied as any data indicative of a number of accelerator devices (e.g., ASICs, FPGAs), accelerator device performance, an accelerator gate count, identifiers related to bitstreams of data input or output from the accelerator device, network address data, health data, or the like.
  • accelerator devices e.g., ASICs, FPGAs
  • accelerator gate count e.g., an accelerator gate count
  • identifiers related to bitstreams of data input or output from the accelerator device e.g., network address data, health data, or the like.
  • the compute device 1200 identifies a rack device associated with the sled 1718 based on the sled identification data.
  • the compute device 1200 may determine the rack device from sled data received directly from the sled 1718 .
  • the compute device 1200 may determine the rack device from sled data received from another device connected to the sled 1718 (e.g., a top-of-rack switch device).
  • the compute device 1200 uses an LLDP extension to enable the native LLDP protocol to include an optional TLV that bears data regarding the rack device associated with the sled 1718 .
  • the compute device 1200 identifies a manager computer associated with the sled 1718 based on sled identification data.
  • a manager computer is any compute device (e.g., an orchestrator server or resource manager computer) that manages one or more power zones (or one or more rack devices in conjunction with other devices).
  • the compute device 1200 may determine the manager computer from sled data received directly from the sled 1718 .
  • the compute device 1200 may determine the manager computer from sled data received from another device connected to the sled 1718 (e.g., a top-of-rack switch device).
  • the compute device 1200 uses an LLDP extension to enable the native LLDP protocol to include an optional TLV that bears data regarding the manager computer associated with the sled 1718 .
  • the method 1500 continues at block 1526 , where the compute device 1200 determines a switch that is associated with a rack device for a rack which the sled 1718 is located or otherwise associated with the sled 1718 .
  • the sled 1718 may be part of a group of sleds that are located within a rack where the sleds are connected to computers outside the rack using a top-of-rack switch device 1716 .
  • the compute device 1200 identifies power source device based on the determined top-of-rack switch device 1716 .
  • the compute device 1200 determines whether there are additional sleds for which sled data is available.
  • the method returns to block 1508 to receive more sled data. If there are no more sleds for which sled data is available, the method advances to block 1532 , shown in FIG. 16 .
  • a diagram 1700 illustrates one potential computing network in which multiple sleds are interconnected with various other types of devices.
  • a logical root is connected to one or more datacenters 1704 .
  • the root 1702 may be a system operator's computer, a mainframe computer used to manage a large number of compute devices, or the like.
  • Each datacenter 1704 may also be thought of as one or more manager computers as described above with respect to block 1524 .
  • a datacenter 1704 is connected to one or more power zones 1706 , 1708 , 1710 , 1712 .
  • each power zone represents a collection of power source devices (e.g., main power lines, unlimited power supply devices, backup generator devices, or the like) that is in turn connected to one or more rack devices 1714 .
  • Each rack device is connected to one or more switches 1716 , represented herein as top-of-rack switches 1716 .
  • Each top-of-rack switch 1716 is connected to one or more sleds 1718 (which may be placed inside or associated with a rack device 1714 ).
  • FIG. 17 also illustrates a fault domain 1722 .
  • the fault domain 1722 may be embodied as any data indicative of a subset of the computing network.
  • the fault domain 1722 may be defined as the group of devices that rely on the same power zone 1706 (which may include various power source devices).
  • a composed node 1720 is shown as a combination of one or more sleds 1718 , where each sled may perform specific functions.
  • sleds 1718 may be selected across fault domains.
  • one sled in the composition may be a compute sled from one fault domain, another an accelerator sled from another fault domain, and so on.
  • the illustrative embodiment provides a virtual-to-physical topology that represents the connectivity and, relatedly, the power zone for a composed node 1720 .
  • the compute device 1200 continues to execute the method 1500 for automatic discovery of fault domains.
  • the method 1500 continues at block 1532 , in which the compute device 1200 generates a fault domain mapping based on the sled data. More specifically, and referring to block 1534 , the compute device 1200 generates the fault domain mapping based on switch data. For example, switch data for each sled 1718 is collated by sled and the sleds connected to each switch (e.g., a top-of-rack switch in a rack) are stored in a data structure (e.g., a tree or graph) in association with an identifier for the switch.
  • a data structure e.g., a tree or graph
  • the compute device 1200 generates the fault domain mapping based on power source device data.
  • Power source device data (or power zone data) for each sled 1718 is collated by sled and an identifier for a power source device (e.g., an AC power source) is stored in a data structure (e.g., a tree or graph) in association with each connected sled.
  • the compute device 1200 generates the fault domain mapping based on rack data. For example, an identifier for the rack device 1714 that holds the sled is stored in a data structure (e.g., a tree or graph) in association with identifiers for the sled.
  • the compute device 1200 generates the fault domain mapping based on manager computer data. For example, an identifier for the manager computer of a datacenter 1704 that manages the sled is stored in a data structure (e.g., a tree or graph) in association with identifiers for the sled.
  • a data structure e.g., a tree or graph
  • the compute device 1200 collates the power source device identifiers to determine the group of sleds associated with the same power zone.
  • each power source device may supply power across rack domains and across switch domains. Grouping sleds by power zone may generate a fault domain 1722 , as illustrated in FIG. 17 .
  • the compute device proceeds to generate a fault domain for each sled and complete a fault domain mapping that includes each fault domain.
  • the compute device 1200 may programmatically convert the fault domain mapping into a consumable fault domain mapping. More specifically, the consumable fault domain mapping is configured to be consumed by a distributed data processing software system, as described above with respect to FIG. 13 .
  • the compute device 1200 stores the software performing the conversion in the form of a plug-in application.
  • the compute device 1200 selects the plug-in application that corresponds to the targeted distributed data processing software system and uses that plug-in application to perform the conversion.
  • the plug-in applications for each distributed data processing software system may be components of the distributed processing adapter 1304 .
  • the fault domain discovery process of the compute device 1200 may be adapted to multiple data processing software systems.
  • the compute device 1200 converts a fault domain mapping for a composed node.
  • sleds 1718 may be selected across fault domains in the case of a composed node.
  • the consumable fault domain mapping reflects the cross-domain sled composition of the composed node.
  • the compute device 1200 transmits the converted fault domain mapping to distributed data processing software.
  • the compute device 1200 may expose the converted fault domain mapping to distributed data processing software using an API.
  • the distributed data processing software may use the fault domain mapping information, for example, to distribute application data to multiple fault domains or to otherwise improve fault tolerance.
  • An embodiment of the technologies disclosed herein may include any one or more, and any combination of, the examples described below.
  • Example 1 includes a compute device to auto-discover power system fault domains within a computer network, the compute device comprising: one or more processors; and one or more memory devices having stored therein a plurality of instructions that, when executed by the one or more processors, cause the compute device to: receive sled data for at least one sled of a plurality of sleds in the computer network, the sled data including sled identification data and sled health data; parse the sled identification data to identify at least one power zone wherein each power zone includes a subset of the plurality of sleds; generate a fault domain mapping using the at least one identified power zone; convert the generated fault domain mapping into a consumable fault domain mapping that is consumable by a distributed processing software system; and provide the consumable fault domain mapping to the distributed processing software system.
  • Example 2 includes the subject matter of Example 1, and wherein to receive the sled data comprises to receive an advertisement generated by the at least one sled via a link layer discovery protocol (LLDP), wherein LLDP is configured to identify one or more relationships between one or more sleds of the plurality of sleds, and wherein the sled health data includes one or more of a number of sockets, a number of cores, a number of drives, a latency metric, a reliability metric, and a memory capacity metric.
  • LLDP link layer discovery protocol
  • Example 3 includes the subject matter of any of Examples 1 and 2, and wherein the one or more memory devices have stored therein a plurality of instructions that, when executed by the one or more processors, further cause the compute device to identify, within the sled identification data, a first top-of-rack switch identifier for a first top-of-rack switch that is communicatively coupled to the at least one sled of the plurality of sleds.
  • Example 4 includes the subject matter of any of Examples 1-3, and wherein the one or more memory devices have stored therein a plurality of instructions that, when executed by the one or more processors, further cause the compute device to identify, within the sled identification data, a second top-of-rack switch identifier for a second top-of-rack switch that is distinct from the first top-of-rack switch, wherein the second-top-of-rack switch is communicatively coupled to the at least one sled.
  • Example 5 includes the subject matter of any of Examples 1-4, and wherein the one or more memory devices have stored therein a plurality of instructions that, when executed by the one or more processors, further cause the compute device to determine that the first top-of-rack switch and the second top-of-rack switch are communicatively coupled to a rack compute device.
  • Example 6 includes the subject matter of any of Examples 1-5, and wherein the one or more memory devices have stored therein a plurality of instructions that, when executed by the one or more processors, further cause the compute device to determine that the rack compute device is communicatively coupled to at least one power source device, wherein the at least one power source device is associated with the at least one power zone.
  • Example 7 includes the subject matter of any of Examples 1-6, and wherein the one or more memory devices have stored therein a plurality of instructions that, when executed by the one or more processors, further cause the compute device to determine that the rack compute device is communicatively coupled to at least one other power source device, wherein the at least one other power source device is associated with at least one other power zone.
  • Example 8 includes the subject matter of any of Examples 1-7, and wherein the computer network includes a composed node, wherein the composed node includes the at least one sled, and wherein the one or more memory devices have stored therein a plurality of instructions that, when executed by the one or more processors, further cause the compute device to: determine that the at least one sled corresponds to the at least one identified power zone; and identify that the composed node corresponds to the at least one identified power zone within the fault domain mapping.
  • Example 9 includes the subject matter of any of Examples 1-8, and wherein the one or more memory devices have stored therein a plurality of instructions that, when executed by the one or more processors, further cause the compute device to identify, within the sled identification data, a datacenter identifier for a datacenter, wherein the datacenter includes at least one manager computer that manages at least one sled of the plurality of sleds, and wherein the manager computer is communicatively coupled to the at least one sled.
  • Example 10 includes the subject matter of any of Examples 1-9, and wherein the one or more memory devices have stored therein a plurality of instructions that, when executed by the one or more processors, further cause the compute device to: determine a data format corresponding to the distributed processing software system; select a plug-in application based on the data format; and convert the generated fault domain mapping into a consumable fault domain mapping by executing the selected plug-in application.
  • Example 11 includes the subject matter of any of Examples 1-10, and wherein the distributed processing software system includes at least one of Apache Hadoop, Apache Cassandra, Ceph, and MongoDB.
  • Example 12 includes a compute sled to transmit sled data to a fault domain manager within a computer network, the compute sled comprising: one or more processors; and one or more memory devices having stored therein a plurality of instructions that, when executed by the one or more processors, cause the compute sled to: initiate a boot process for the compute sled; discover sled data, wherein the sled data includes sled identification data and sled health data, wherein to discover the sled identification data comprises to identify a first top-of-rack switch, wherein the first top-of-rack switch is communicatively coupled to the compute sled; generate an advertisement that includes the sled data, wherein the advertisement is configured to identify at least one relationship between the compute sled and at least one other compute sled within the computer network; and transmit the sled data to the fault domain manager.
  • Example 13 includes the subject matter of Example 12, wherein to initiate the boot process comprises to initiate an operating system program load process.
  • Example 14 includes the subject matter of any of Examples 12 and 13, wherein to discover the sled data includes to discover, within the sled identification data, a second top-of-rack switch identifier for a second top-of-rack switch that is distinct from the first top-of-rack switch, wherein the second-top-of-rack switch is communicatively coupled to the compute sled.
  • Example 15 includes the subject matter of any of Examples 12-14, wherein the first top-of-rack switch and the second top-of-rack switch are communicatively coupled to a rack compute device.
  • Example 16 includes the subject matter of any of Examples 12-15, wherein the rack compute device is communicatively coupled to at least one power source device, wherein the at least one power source device is associated with at least one power zone.
  • Example 17 includes the subject matter of any of Examples 12-16, wherein the rack compute device is communicatively coupled to at least one other power source device, wherein the at least one other power source device is associated with at least one other power zone.
  • Example 18 includes the subject matter of any of Examples 12-17, wherein to discover the sled data includes to discover, within the sled identification data, sled identification data further includes a datacenter identifier for a datacenter, wherein the datacenter includes at least one manager computer that manages at least one sled of the plurality of sleds, and wherein the manager computer is communicatively coupled to the at least one sled.
  • Example 19 includes the subject matter of any of Examples 12-18, wherein the sled health data includes one or more of a number of sockets, a number of cores, a number of drives, a latency metric, a reliability metric, and a memory capacity metric.
  • Example 20 includes the subject matter of any of Examples 12-19, wherein to generate the advertisement comprises to generate the advertisement using a link layer discovery protocol (LLDP).
  • LLDP link layer discovery protocol
  • Example 21 includes a method of automatically discovering power system fault domains within a computer network, the method comprising: receiving sled data for at least one sled of a plurality of sleds in the computer network, the sled data including sled identification data and sled health data; parsing the sled identification data to identify at least one power zone wherein each power zone includes a subset of the plurality of sleds; generating a fault domain mapping using the at least one identified power zone; converting the generated fault domain mapping into a consumable fault domain mapping that is consumable by a distributed processing software system; and providing the consumable fault domain mapping to the distributed processing software system.
  • Example 22 includes the subject matter of Example 21, and further comprising receiving sled data by an advertisement generated by the at least one sled via a link layer discovery protocol (LLDP), wherein LLDP is configured to identify one or more relationships between one or more sleds of the plurality of sleds, and wherein the sled health data includes one or more of a number of sockets, a number of cores, a number of drives, a latency metric, a reliability metric, and a memory capacity metric.
  • LLDP link layer discovery protocol
  • Example 23 includes the subject matter of any of Examples 21 and 22, and further comprising identifying, within the sled identification data, a first top-of-rack switch identifier for a first top-of-rack switch that is communicatively coupled to the at least one sled of the plurality of sleds.
  • Example 24 includes the subject matter of any of Examples 21-23, and further comprising identifying, within the sled identification data, a second top-of-rack switch identifier for a second top-of-rack switch that is distinct from the first top-of-rack switch, wherein the second-top-of-rack switch is communicatively coupled to the at least one sled.
  • Example 25 includes the subject matter of any of Examples 21-24, and further comprising determining that the first top-of-rack switch and the second top-of-rack switch are communicatively coupled to a rack compute device.
  • Example 26 includes the subject matter of any of Examples 21-25, and further comprising determining that the rack compute device is communicatively coupled to at least one power source device, wherein the at least one power source device is associated with the at least one power zone.
  • Example 27 includes the subject matter of any of Examples 21-26, and further comprising determining that the rack compute device is communicatively coupled to at least one other power source device, wherein the at least one other power source device is associated with at least one other power zone.
  • Example 28 includes the subject matter of any of Examples 21-27, and wherein the computer network includes a composed node, wherein the composed node includes the at least one sled, and further comprising: determining that the at least one sled corresponds to the at least one identified power zone; and identifying that the composed node corresponds to the at least one identified power zone within the fault domain mapping.
  • Example 29 includes the subject matter of any of Examples 21-28, and further comprising identifying, within the sled identification data, a datacenter identifier for a datacenter, wherein the datacenter includes at least one manager computer that manages at least one sled of the plurality of sleds, and wherein the manager computer is communicatively coupled to the at least one sled.
  • Example 30 includes the subject matter of any of Examples 21-29, and further comprising: determining a data format corresponding to the distributed processing software system; selecting a plug-in application based on the data format; and converting the generated fault domain mapping into a consumable fault domain mapping by executing the selected plug-in application.
  • Example 31 includes the subject matter of any of Examples 21-30, and wherein the distributed processing software system includes at least one of Apache Hadoop, Apache Cassandra, Ceph, and MongoDB.
  • Example 32 includes a method of transmitting sled data to a fault domain manager within a computer network, the method comprising: initiating, by a compute sled, a boot process for the compute sled; discovering, by the compute sled, sled data, wherein the sled data includes sled identification data and sled health data, wherein to discover the sled identification data comprises to identify a first top-of-rack switch, wherein the first top-of-rack switch is communicatively coupled to the compute sled; generating, by the compute sled, an advertisement that includes the sled data, wherein the advertisement is configured to identify at least one relationship between the compute sled and at least one other compute sled within the computer network; and transmitting, by the compute sled, the sled data to the fault domain manager.
  • Example 33 includes the subject matter of Example 32, and wherein initiating the boot process comprises initiating initiate an operating system program load process.
  • Example 34 includes the subject matter of any of Examples 32 and 33, and wherein discovering the sled data includes discovering, within the sled identification data, a second top-of-rack switch identifier for a second top-of-rack switch that is distinct from the first top-of-rack switch, wherein the second-top-of-rack switch is communicatively coupled to the compute sled.
  • Example 35 includes the subject matter of any of Examples 32-34, and wherein the first top-of-rack switch and the second top-of-rack switch are communicatively coupled to a rack compute device.
  • Example 36 includes the subject matter of any of Examples 32-35, and wherein the rack compute device is communicatively coupled to at least one power source device, wherein the at least one power source device is associated with at least one power zone.
  • Example 37 includes the subject matter of any of Examples 32-36, and wherein the rack compute device is communicatively coupled to at least one other power source device, wherein the at least one other power source device is associated with at least one other power zone.
  • Example 38 includes the subject matter of any of Examples 32-37, and wherein discovering the sled data includes discovering, within the sled identification data, sled identification data further includes a datacenter identifier for a datacenter, wherein the datacenter includes at least one manager computer that manages at least one sled of the plurality of sleds, and wherein the manager computer is communicatively coupled to the at least one sled.
  • Example 39 includes the subject matter of any of Examples 32-38, and wherein the sled health data includes one or more of a number of sockets, a number of cores, a number of drives, a latency metric, a reliability metric, and a memory capacity metric.
  • Example 40 includes the subject matter of any of Examples 32-39, and wherein generating the advertisement comprises generating the advertisement using a link layer discovery protocol (LLDP).
  • LLDP link layer discovery protocol
  • Example 41 includes a computing device comprising: a processor; and a memory having stored therein a plurality of instructions that when executed by the processor cause the computing device to perform the method of any of Examples 21-40.
  • Example 42 includes one or more non-transitory, computer readable storage media comprising a plurality of instructions stored thereon that in response to being executed result in a computing device performing the method of any of Examples 21-40.
  • Example 43 includes a computing device comprising means for performing the method of any of Examples 21-40.
  • Example 44 includes a compute device to auto-discover power system fault domains within a computer network, the compute device comprising: fault domain manager circuitry to: receive sled data for at least one sled of a plurality of sleds in the computer network, the sled data including sled identification data and sled health data; parse the sled identification data to identify at least one power zone wherein each power zone includes a subset of the plurality of sleds; generate a fault domain mapping using the at least one identified power zone; and distributed processing adapter circuitry to: convert the generated fault domain mapping into a consumable fault domain mapping that is consumable by a distributed processing software system; and provide the consumable fault domain mapping to the distributed processing software system.
  • fault domain manager circuitry to: receive sled data for at least one sled of a plurality of sleds in the computer network, the sled data including sled identification data and sled health data; parse the sled identification data to identify at least one power zone wherein
  • Example 45 includes the subject matter of Example 44, and wherein to receive the sled data comprises to receive an advertisement generated by the at least one sled via a link layer discovery protocol (LLDP), wherein LLDP is configured to identify one or more relationships between one or more sleds of the plurality of sleds, and wherein the sled health data includes one or more of a number of sockets, a number of cores, a number of drives, a latency metric, a reliability metric, and a memory capacity metric.
  • LLDP link layer discovery protocol
  • Example 46 includes the subject matter of any of Examples 44 and 45, and wherein the fault domain manager circuitry is further to identify, within the sled identification data, a first top-of-rack switch identifier for a first top-of-rack switch that is communicatively coupled to the at least one sled of the plurality of sleds.
  • Example 47 includes the subject matter of any of Examples 44-46, and wherein the fault domain manager circuitry is further to identify, within the sled identification data, a second top-of-rack switch identifier for a second top-of-rack switch that is distinct from the first top-of-rack switch, wherein the second-top-of-rack switch is communicatively coupled to the at least one sled.
  • Example 48 includes the subject matter of any of Examples 44-47, and wherein the fault domain manager circuitry is further to determine that the first top-of-rack switch and the second top-of-rack switch are communicatively coupled to a rack compute device.
  • Example 49 includes the subject matter of any of Examples 44-48, and wherein the fault domain manager circuitry is further to determine that the rack compute device is communicatively coupled to at least one power source device, wherein the at least one power source device is associated with the at least one power zone.
  • Example 50 includes the subject matter of any of Examples 44-49, and wherein the fault domain manager circuitry is further to determine that the rack compute device is communicatively coupled to at least one other power source device, wherein the at least one other power source device is associated with at least one other power zone.
  • Example 51 includes the subject matter of any of Examples 44-50, and wherein the computer network includes a composed node, wherein the composed node includes the at least one sled, and wherein the fault domain manager circuitry is further to: determine that the at least one sled corresponds to the at least one identified power zone; and identify that the composed node corresponds to the at least one identified power zone within the fault domain mapping.
  • Example 52 includes the subject matter of any of Examples 44-51, and wherein the fault domain manager circuitry is further to identify, within the sled identification data, a datacenter identifier for a datacenter, wherein the datacenter includes at least one manager computer that manages at least one sled of the plurality of sleds, and wherein the manager computer is communicatively coupled to the at least one sled.
  • Example 53 includes the subject matter of any of Examples 44-52, and wherein the distributed processing adapter circuitry is further to: determine a data format corresponding to the distributed processing software system; select a plug-in application based on the data format; and convert the generated fault domain mapping into a consumable fault domain mapping by executing the selected plug-in application.
  • Example 54 includes the subject matter of any of Examples 44-53, and wherein the distributed processing software system includes at least one of Apache Hadoop, Apache Cassandra, Ceph, and MongoDB.
  • Example 55 includes a compute sled to transmit sled data to a fault domain manager within a computer network, the compute sled comprising: compute engine circuitry to: initiate a boot process for the compute sled; discover sled data, wherein the sled data includes sled identification data and sled health data, wherein to discover the sled identification data comprises to identify a first top-of-rack switch, wherein the first top-of-rack switch is communicatively coupled to the compute sled; generate an advertisement that includes the sled data, wherein the advertisement is configured to identify at least one relationship between the compute sled and at least one other compute sled within the computer network; and transmit the sled data to the fault domain manager.
  • Example 56 includes the subject matter of Example 55, wherein to initiate the boot process comprises to initiate an operating system program load process.
  • Example 57 includes the subject matter of any of Examples 55 and 56, wherein to discover the sled data includes to discover, within the sled identification data, a second top-of-rack switch identifier for a second top-of-rack switch that is distinct from the first top-of-rack switch, wherein the second-top-of-rack switch is communicatively coupled to the compute sled.
  • Example 58 includes the subject matter of any of Examples 55-57, wherein the first top-of-rack switch and the second top-of-rack switch are communicatively coupled to a rack compute device.
  • Example 59 includes the subject matter of any of Examples 55-58, wherein the rack compute device is communicatively coupled to at least one power source device, wherein the at least one power source device is associated with at least one power zone.
  • Example 60 includes the subject matter of any of Examples 55-59, wherein the rack compute device is communicatively coupled to at least one other power source device, wherein the at least one other power source device is associated with at least one other power zone.
  • Example 61 includes the subject matter of any of Examples 55-60, wherein to discover the sled data includes to discover, within the sled identification data, sled identification data further includes a datacenter identifier for a datacenter, wherein the datacenter includes at least one manager computer that manages at least one sled of the plurality of sleds, and wherein the manager computer is communicatively coupled to the at least one sled.
  • Example 62 includes the subject matter of any of Examples 55-61, wherein the sled health data includes one or more of a number of sockets, a number of cores, a number of drives, a latency metric, a reliability metric, and a memory capacity metric.
  • Example 63 includes the subject matter of any of Examples 55-62, wherein to generate the advertisement comprises to generate the advertisement using a link layer discovery protocol (LLDP).
  • LLDP link layer discovery protocol
  • Example 64 includes a compute device of automatically discovering power system fault domains within a computer network, the compute device comprising: circuitry for receiving sled data for at least one sled of a plurality of sleds in the computer network, the sled data including sled identification data and sled health data; means for parsing the sled identification data to identify at least one power zone wherein each power zone includes a subset of the plurality of sleds; means for generating a fault domain mapping using the at least one identified power zone; means for converting the generated fault domain mapping into a consumable fault domain mapping that is consumable by a distributed processing software system; and means for providing the consumable fault domain mapping to the distributed processing software system.
  • Example 65 includes the subject matter of Example 64, and further comprising means for receiving sled data by an advertisement generated by the at least one sled via a link layer discovery protocol (LLDP), wherein LLDP is configured to identify one or more relationships between one or more sleds of the plurality of sleds, and wherein the sled health data includes one or more of a number of sockets, a number of cores, a number of drives, a latency metric, a reliability metric, and a memory capacity metric.
  • LLDP link layer discovery protocol
  • Example 66 includes the subject matter of any of Examples 64 and 65, and further comprising means for identifying, within the sled identification data, a first top-of-rack switch identifier for a first top-of-rack switch that is communicatively coupled to the at least one sled of the plurality of sleds.
  • Example 67 includes the subject matter of any of Examples 64-66, and further comprising means for identifying, within the sled identification data, a second top-of-rack switch identifier for a second top-of-rack switch that is distinct from the first top-of-rack switch, wherein the second-top-of-rack switch is communicatively coupled to the at least one sled.
  • Example 68 includes the subject matter of any of Examples 64-67, and further comprising means for determining that the first top-of-rack switch and the second top-of-rack switch are communicatively coupled to a rack compute device.
  • Example 69 includes the subject matter of any of Examples 64-68, and further comprising means for determining that the rack compute device is communicatively coupled to at least one power source device, wherein the at least one power source device is associated with the at least one power zone.
  • Example 70 includes the subject matter of any of Examples 64-69, and further comprising means for determining that the rack compute device is communicatively coupled to at least one other power source device, wherein the at least one other power source device is associated with at least one other power zone.
  • Example 71 includes the subject matter of any of Examples 64-70, and wherein the computer network includes a composed node, wherein the composed node includes the at least one sled, and further comprising: means for determining that the at least one sled corresponds to the at least one identified power zone; and means for identifying that the composed node corresponds to the at least one identified power zone within the fault domain mapping.
  • Example 72 includes the subject matter of any of Examples 64-71, and further comprising means for identifying, within the sled identification data, a datacenter identifier for a datacenter, wherein the datacenter includes at least one manager computer that manages at least one sled of the plurality of sleds, and wherein the manager computer is communicatively coupled to the at least one sled.
  • Example 73 includes the subject matter of any of Examples 64-72, and further comprising: means for determining a data format corresponding to the distributed processing software system; means for selecting a plug-in application based on the data format; and means for converting the generated fault domain mapping into a consumable fault domain mapping by executing the selected plug-in application.
  • Example 74 includes the subject matter of any of Examples 64-73, and wherein the distributed processing software system includes at least one of Apache Hadoop, Apache Cassandra, Ceph, and MongoDB.
  • Example 75 includes a compute device of transmitting sled data to a fault domain manager within a computer network, the compute device comprising: circuitry for initiating, by a compute sled, a boot process for the compute sled; means for discovering, by the compute sled, sled data, wherein the sled data includes sled identification data and sled health data, wherein to discover the sled identification data comprises to identify a first top-of-rack switch, wherein the first top-of-rack switch is communicatively coupled to the compute sled; means for generating, by the compute sled, an advertisement that includes the sled data, wherein the advertisement is configured to identify at least one relationship between the compute sled and at least one other compute sled within the computer network; and circuitry for transmitting, by the compute sled, the sled data to the fault domain manager.
  • Example 76 includes the subject matter of Example 75, and wherein the circuitry for initiating the boot process comprises circuitry for initiating initiate an operating system program load process.
  • Example 77 includes the subject matter of any of Examples 75 and 76, and wherein the means for discovering the sled data includes means for discovering, within the sled identification data, a second top-of-rack switch identifier for a second top-of-rack switch that is distinct from the first top-of-rack switch, wherein the second-top-of-rack switch is communicatively coupled to the compute sled.
  • Example 78 includes the subject matter of any of Examples 75-77, and wherein the first top-of-rack switch and the second top-of-rack switch are communicatively coupled to a rack compute device.
  • Example 79 includes the subject matter of any of Examples 75-78, and wherein the rack compute device is communicatively coupled to at least one power source device, wherein the at least one power source device is associated with at least one power zone.
  • Example 80 includes the subject matter of any of Examples 75-79, and wherein the rack compute device is communicatively coupled to at least one other power source device, wherein the at least one other power source device is associated with at least one other power zone.
  • Example 81 includes the subject matter of any of Examples 75-80, and wherein the means for discovering the sled data includes means for discovering, within the sled identification data, sled identification data further includes a datacenter identifier for a datacenter, wherein the datacenter includes at least one manager computer that manages at least one sled of the plurality of sleds, and wherein the manager computer is communicatively coupled to the at least one sled.
  • Example 82 includes the subject matter of any of Examples 75-81, and wherein the sled health data includes one or more of a number of sockets, a number of cores, a number of drives, a latency metric, a reliability metric, and a memory capacity metric.
  • Example 83 includes the subject matter of any of Examples 75-82, and wherein the means for generating the advertisement comprises means for generating the advertisement using a link layer discovery protocol (LLDP).
  • LLDP link layer discovery protocol

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Software Systems (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Quality & Reliability (AREA)
  • Human Computer Interaction (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Thermal Sciences (AREA)
  • Power Engineering (AREA)
  • Computer Security & Cryptography (AREA)
  • Mechanical Engineering (AREA)
  • Robotics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Cooling Or The Like Of Electrical Apparatus (AREA)
  • Multi Processors (AREA)
  • Business, Economics & Management (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • Economics (AREA)
  • Technology Law (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Manufacturing & Machinery (AREA)
  • Game Theory and Decision Science (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)
  • Data Mining & Analysis (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • General Health & Medical Sciences (AREA)
US15/850,325 2017-08-30 2017-12-21 Technologies for auto-discovery of fault domains Abandoned US20190068466A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US15/850,325 US20190068466A1 (en) 2017-08-30 2017-12-21 Technologies for auto-discovery of fault domains

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
IN201741030632 2017-08-30
IN201741030632 2017-08-30
US201762584401P 2017-11-10 2017-11-10
US15/850,325 US20190068466A1 (en) 2017-08-30 2017-12-21 Technologies for auto-discovery of fault domains

Publications (1)

Publication Number Publication Date
US20190068466A1 true US20190068466A1 (en) 2019-02-28

Family

ID=65434219

Family Applications (24)

Application Number Title Priority Date Filing Date
US15/850,325 Abandoned US20190068466A1 (en) 2017-08-30 2017-12-21 Technologies for auto-discovery of fault domains
US15/858,316 Abandoned US20190065260A1 (en) 2017-08-30 2017-12-29 Technologies for kernel scale-out
US15/858,549 Abandoned US20190065401A1 (en) 2017-08-30 2017-12-29 Technologies for providing efficient memory access on an accelerator sled
US15/858,748 Active 2039-08-11 US11614979B2 (en) 2017-08-30 2017-12-29 Technologies for configuration-free platform firmware
US15/858,286 Abandoned US20190068523A1 (en) 2017-08-30 2017-12-29 Technologies for allocating resources across data centers
US15/858,542 Active 2039-10-02 US11748172B2 (en) 2017-08-30 2017-12-29 Technologies for providing efficient pooling for a hyper converged infrastructure
US15/858,557 Abandoned US20190065083A1 (en) 2017-08-30 2017-12-29 Technologies for providing efficient access to pooled accelerator devices
US15/858,305 Abandoned US20190068464A1 (en) 2017-08-30 2017-12-29 Technologies for machine learning schemes in dynamic switching between adaptive connections and connection optimization
US15/858,288 Abandoned US20190068521A1 (en) 2017-08-30 2017-12-29 Technologies for automated network congestion management
US15/859,385 Abandoned US20190065281A1 (en) 2017-08-30 2017-12-30 Technologies for auto-migration in accelerated architectures
US15/859,388 Abandoned US20190065231A1 (en) 2017-08-30 2017-12-30 Technologies for migrating virtual machines
US15/859,364 Active 2039-07-30 US11392425B2 (en) 2017-08-30 2017-12-30 Technologies for providing a split memory pool for full rack connectivity
US15/859,368 Active 2040-02-21 US11422867B2 (en) 2017-08-30 2017-12-30 Technologies for composing a managed node based on telemetry data
US15/859,363 Abandoned US20190068444A1 (en) 2017-08-30 2017-12-30 Technologies for providing efficient transfer of results from accelerator devices in a disaggregated architecture
US15/859,394 Active 2040-04-27 US11467885B2 (en) 2017-08-30 2017-12-30 Technologies for managing a latency-efficient pipeline through a network interface controller
US15/859,366 Abandoned US20190065261A1 (en) 2017-08-30 2017-12-30 Technologies for in-processor workload phase detection
US15/916,394 Abandoned US20190065415A1 (en) 2017-08-30 2018-03-09 Technologies for local disaggregation of memory
US15/933,855 Active 2039-05-07 US11030017B2 (en) 2017-08-30 2018-03-23 Technologies for efficiently booting sleds in a disaggregated architecture
US15/942,108 Abandoned US20190067848A1 (en) 2017-08-30 2018-03-30 Memory mezzanine connectors
US15/942,101 Active 2040-07-19 US11416309B2 (en) 2017-08-30 2018-03-30 Technologies for dynamic accelerator selection
US16/022,962 Active 2038-12-31 US11055149B2 (en) 2017-08-30 2018-06-29 Technologies for providing workload-based sled position adjustment
US16/023,803 Active 2038-07-17 US10888016B2 (en) 2017-08-30 2018-06-29 Technologies for automated servicing of sleds of a data center
US16/642,520 Abandoned US20200192710A1 (en) 2017-08-30 2018-08-30 Technologies for enabling and metering the utilization of features on demand
US16/642,523 Abandoned US20200257566A1 (en) 2017-08-30 2018-08-30 Technologies for managing disaggregated resources in a data center

Family Applications After (23)

Application Number Title Priority Date Filing Date
US15/858,316 Abandoned US20190065260A1 (en) 2017-08-30 2017-12-29 Technologies for kernel scale-out
US15/858,549 Abandoned US20190065401A1 (en) 2017-08-30 2017-12-29 Technologies for providing efficient memory access on an accelerator sled
US15/858,748 Active 2039-08-11 US11614979B2 (en) 2017-08-30 2017-12-29 Technologies for configuration-free platform firmware
US15/858,286 Abandoned US20190068523A1 (en) 2017-08-30 2017-12-29 Technologies for allocating resources across data centers
US15/858,542 Active 2039-10-02 US11748172B2 (en) 2017-08-30 2017-12-29 Technologies for providing efficient pooling for a hyper converged infrastructure
US15/858,557 Abandoned US20190065083A1 (en) 2017-08-30 2017-12-29 Technologies for providing efficient access to pooled accelerator devices
US15/858,305 Abandoned US20190068464A1 (en) 2017-08-30 2017-12-29 Technologies for machine learning schemes in dynamic switching between adaptive connections and connection optimization
US15/858,288 Abandoned US20190068521A1 (en) 2017-08-30 2017-12-29 Technologies for automated network congestion management
US15/859,385 Abandoned US20190065281A1 (en) 2017-08-30 2017-12-30 Technologies for auto-migration in accelerated architectures
US15/859,388 Abandoned US20190065231A1 (en) 2017-08-30 2017-12-30 Technologies for migrating virtual machines
US15/859,364 Active 2039-07-30 US11392425B2 (en) 2017-08-30 2017-12-30 Technologies for providing a split memory pool for full rack connectivity
US15/859,368 Active 2040-02-21 US11422867B2 (en) 2017-08-30 2017-12-30 Technologies for composing a managed node based on telemetry data
US15/859,363 Abandoned US20190068444A1 (en) 2017-08-30 2017-12-30 Technologies for providing efficient transfer of results from accelerator devices in a disaggregated architecture
US15/859,394 Active 2040-04-27 US11467885B2 (en) 2017-08-30 2017-12-30 Technologies for managing a latency-efficient pipeline through a network interface controller
US15/859,366 Abandoned US20190065261A1 (en) 2017-08-30 2017-12-30 Technologies for in-processor workload phase detection
US15/916,394 Abandoned US20190065415A1 (en) 2017-08-30 2018-03-09 Technologies for local disaggregation of memory
US15/933,855 Active 2039-05-07 US11030017B2 (en) 2017-08-30 2018-03-23 Technologies for efficiently booting sleds in a disaggregated architecture
US15/942,108 Abandoned US20190067848A1 (en) 2017-08-30 2018-03-30 Memory mezzanine connectors
US15/942,101 Active 2040-07-19 US11416309B2 (en) 2017-08-30 2018-03-30 Technologies for dynamic accelerator selection
US16/022,962 Active 2038-12-31 US11055149B2 (en) 2017-08-30 2018-06-29 Technologies for providing workload-based sled position adjustment
US16/023,803 Active 2038-07-17 US10888016B2 (en) 2017-08-30 2018-06-29 Technologies for automated servicing of sleds of a data center
US16/642,520 Abandoned US20200192710A1 (en) 2017-08-30 2018-08-30 Technologies for enabling and metering the utilization of features on demand
US16/642,523 Abandoned US20200257566A1 (en) 2017-08-30 2018-08-30 Technologies for managing disaggregated resources in a data center

Country Status (5)

Country Link
US (24) US20190068466A1 (zh)
EP (1) EP3676708A4 (zh)
CN (8) CN109426316A (zh)
DE (1) DE112018004798T5 (zh)
WO (5) WO2019045929A1 (zh)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10785549B2 (en) 2016-07-22 2020-09-22 Intel Corporation Technologies for switching network traffic in a data center
US11093311B2 (en) * 2016-11-29 2021-08-17 Intel Corporation Technologies for monitoring node cluster health
US11137922B2 (en) 2016-11-29 2021-10-05 Intel Corporation Technologies for providing accelerated functions as a service in a disaggregated architecture
US20220129601A1 (en) * 2020-10-26 2022-04-28 Oracle International Corporation Techniques for generating a configuration for electrically isolating fault domains in a data center
US11411994B2 (en) * 2019-04-05 2022-08-09 Cisco Technology, Inc. Discovering trustworthy devices using attestation and mutual attestation
US11436113B2 (en) * 2018-06-28 2022-09-06 Twitter, Inc. Method and system for maintaining storage device failure tolerance in a composable infrastructure
US11650598B2 (en) * 2017-12-30 2023-05-16 Telescent Inc. Automated physical network management system utilizing high resolution RFID, optical scans and mobile robotic actuator

Families Citing this family (140)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10693977B2 (en) * 2017-01-30 2020-06-23 Centurylink Intellectual Property Llc Method and system for implementing dual network telemetry application programming interface (API) framework
US9948724B2 (en) * 2015-09-10 2018-04-17 International Business Machines Corporation Handling multi-pipe connections
US10346315B2 (en) 2017-05-26 2019-07-09 Oracle International Corporation Latchless, non-blocking dynamically resizable segmented hash index
US10574580B2 (en) 2017-07-04 2020-02-25 Vmware, Inc. Network resource management for hyper-converged infrastructures
US11119835B2 (en) 2017-08-30 2021-09-14 Intel Corporation Technologies for providing efficient reprovisioning in an accelerator device
US11106427B2 (en) * 2017-09-29 2021-08-31 Intel Corporation Memory filtering for disaggregate memory architectures
US10511690B1 (en) 2018-02-20 2019-12-17 Intuit, Inc. Method and apparatus for predicting experience degradation events in microservice-based applications
WO2019190449A1 (en) * 2018-03-26 2019-10-03 Hewlett-Packard Development Company, L.P. Generation of kernels based on physical states
US10761726B2 (en) * 2018-04-16 2020-09-01 VWware, Inc. Resource fairness control in distributed storage systems using congestion data
US11315013B2 (en) * 2018-04-23 2022-04-26 EMC IP Holding Company LLC Implementing parameter server in networking infrastructure for high-performance computing
US10599553B2 (en) * 2018-04-27 2020-03-24 International Business Machines Corporation Managing cloud-based hardware accelerators
US10977085B2 (en) 2018-05-17 2021-04-13 International Business Machines Corporation Optimizing dynamical resource allocations in disaggregated data centers
US11221886B2 (en) 2018-05-17 2022-01-11 International Business Machines Corporation Optimizing dynamical resource allocations for cache-friendly workloads in disaggregated data centers
US11330042B2 (en) * 2018-05-17 2022-05-10 International Business Machines Corporation Optimizing dynamic resource allocations for storage-dependent workloads in disaggregated data centers
US10893096B2 (en) 2018-05-17 2021-01-12 International Business Machines Corporation Optimizing dynamical resource allocations using a data heat map in disaggregated data centers
US10936374B2 (en) 2018-05-17 2021-03-02 International Business Machines Corporation Optimizing dynamic resource allocations for memory-dependent workloads in disaggregated data centers
US10841367B2 (en) 2018-05-17 2020-11-17 International Business Machines Corporation Optimizing dynamical resource allocations for cache-dependent workloads in disaggregated data centers
US10601903B2 (en) 2018-05-17 2020-03-24 International Business Machines Corporation Optimizing dynamical resource allocations based on locality of resources in disaggregated data centers
US10684887B2 (en) * 2018-05-25 2020-06-16 Vmware, Inc. Live migration of a virtualized compute accelerator workload
US10795713B2 (en) 2018-05-25 2020-10-06 Vmware, Inc. Live migration of a virtualized compute accelerator workload
US11042406B2 (en) 2018-06-05 2021-06-22 Intel Corporation Technologies for providing predictive thermal management
US11431648B2 (en) 2018-06-11 2022-08-30 Intel Corporation Technologies for providing adaptive utilization of different interconnects for workloads
US20190384376A1 (en) * 2018-06-18 2019-12-19 American Megatrends, Inc. Intelligent allocation of scalable rack resources
US11388835B1 (en) * 2018-06-27 2022-07-12 Amazon Technologies, Inc. Placement of custom servers
US12034593B1 (en) 2018-07-10 2024-07-09 Cable Television Laboratories, Inc. Systems and methods for advanced core network controls
US11968548B1 (en) * 2018-07-10 2024-04-23 Cable Television Laboratories, Inc. Systems and methods for reducing communication network performance degradation using in-band telemetry data
US11347678B2 (en) * 2018-08-06 2022-05-31 Oracle International Corporation One-sided reliable remote direct memory operations
US10977193B2 (en) 2018-08-17 2021-04-13 Oracle International Corporation Remote direct memory operations (RDMOs) for transactional processing systems
EP3844689A4 (en) 2018-08-30 2022-06-08 Rigetti & Co, LLC HIGH-PERFORMANCE, LOW-LATENCY HYBRID COMPUTING
US11188348B2 (en) * 2018-08-31 2021-11-30 International Business Machines Corporation Hybrid computing device selection analysis
US11012423B2 (en) 2018-09-25 2021-05-18 International Business Machines Corporation Maximizing resource utilization through efficient component communication in disaggregated datacenters
US11650849B2 (en) * 2018-09-25 2023-05-16 International Business Machines Corporation Efficient component communication through accelerator switching in disaggregated datacenters
US11182322B2 (en) 2018-09-25 2021-11-23 International Business Machines Corporation Efficient component communication through resource rewiring in disaggregated datacenters
US11163713B2 (en) 2018-09-25 2021-11-02 International Business Machines Corporation Efficient component communication through protocol switching in disaggregated datacenters
US11138044B2 (en) * 2018-09-26 2021-10-05 Micron Technology, Inc. Memory pooling between selected memory resources
US10901893B2 (en) * 2018-09-28 2021-01-26 International Business Machines Corporation Memory bandwidth management for performance-sensitive IaaS
US10962389B2 (en) * 2018-10-03 2021-03-30 International Business Machines Corporation Machine status detection
WO2020072819A1 (en) * 2018-10-03 2020-04-09 Rigetti & Co, Inc. Parcelled quantum resources
US10768990B2 (en) * 2018-11-01 2020-09-08 International Business Machines Corporation Protecting an application by autonomously limiting processing to a determined hardware capacity
US11055186B2 (en) * 2018-11-27 2021-07-06 Red Hat, Inc. Managing related devices for virtual machines using robust passthrough device enumeration
US11275622B2 (en) * 2018-11-29 2022-03-15 International Business Machines Corporation Utilizing accelerators to accelerate data analytic workloads in disaggregated systems
US10901918B2 (en) * 2018-11-29 2021-01-26 International Business Machines Corporation Constructing flexibly-secure systems in a disaggregated environment
US10831975B2 (en) 2018-11-29 2020-11-10 International Business Machines Corporation Debug boundaries in a hardware accelerator
US11048318B2 (en) * 2018-12-06 2021-06-29 Intel Corporation Reducing microprocessor power with minimal performance impact by dynamically adapting runtime operating configurations using machine learning
US10970107B2 (en) * 2018-12-21 2021-04-06 Servicenow, Inc. Discovery of hyper-converged infrastructure
US10771344B2 (en) * 2018-12-21 2020-09-08 Servicenow, Inc. Discovery of hyper-converged infrastructure devices
US11269593B2 (en) * 2019-01-23 2022-03-08 Sap Se Global number range generation
US11271804B2 (en) * 2019-01-25 2022-03-08 Dell Products L.P. Hyper-converged infrastructure component expansion/replacement system
US11429440B2 (en) * 2019-02-04 2022-08-30 Hewlett Packard Enterprise Development Lp Intelligent orchestration of disaggregated applications based on class of service
US10817221B2 (en) * 2019-02-12 2020-10-27 International Business Machines Corporation Storage device with mandatory atomic-only access
US10949101B2 (en) * 2019-02-25 2021-03-16 Micron Technology, Inc. Storage device operation orchestration
US11294992B2 (en) * 2019-03-12 2022-04-05 Xilinx, Inc. Locking execution of cores to licensed programmable devices in a data center
US11443018B2 (en) * 2019-03-12 2022-09-13 Xilinx, Inc. Locking execution of cores to licensed programmable devices in a data center
US11531869B1 (en) * 2019-03-28 2022-12-20 Xilinx, Inc. Neural-network pooling
JP7176455B2 (ja) * 2019-03-28 2022-11-22 オムロン株式会社 監視システム、設定装置および監視方法
US11243817B2 (en) * 2019-03-29 2022-02-08 Intel Corporation Technologies for data migration between edge accelerators hosted on different edge locations
US11055256B2 (en) * 2019-04-02 2021-07-06 Intel Corporation Edge component computing system having integrated FaaS call handling capability
US11089137B2 (en) * 2019-04-02 2021-08-10 International Business Machines Corporation Dynamic data transmission
US11263122B2 (en) * 2019-04-09 2022-03-01 Vmware, Inc. Implementing fine grain data coherency of a shared memory region
US11416294B1 (en) * 2019-04-17 2022-08-16 Juniper Networks, Inc. Task processing for management of data center resources
US11003479B2 (en) * 2019-04-29 2021-05-11 Intel Corporation Device, system and method to communicate a kernel binary via a network
CN110053650B (zh) * 2019-05-06 2022-06-07 湖南中车时代通信信号有限公司 一种列车自动运行系统、列车自动运行系统架构及列车自动运行系统的模块管理方法
CN110203600A (zh) * 2019-06-06 2019-09-06 北京卫星环境工程研究所 适用于航天器总装物料的自动存储及拣选系统
US11481117B2 (en) * 2019-06-17 2022-10-25 Hewlett Packard Enterprise Development Lp Storage volume clustering based on workload fingerprints
US20200409748A1 (en) * 2019-06-28 2020-12-31 Intel Corporation Technologies for managing accelerator resources
US10877817B1 (en) * 2019-06-28 2020-12-29 Intel Corporation Technologies for providing inter-kernel application programming interfaces for an accelerated architecture
US10949362B2 (en) * 2019-06-28 2021-03-16 Intel Corporation Technologies for facilitating remote memory requests in accelerator devices
US11514017B2 (en) 2019-08-02 2022-11-29 Jpmorgan Chase Bank, N.A. Systems and methods for provisioning a new secondary IdentityIQ instance to an existing IdentityIQ instance
US11082411B2 (en) * 2019-08-06 2021-08-03 Advanced New Technologies Co., Ltd. RDMA-based data transmission method, network interface card, server and medium
US10925166B1 (en) * 2019-08-07 2021-02-16 Quanta Computer Inc. Protection fixture
JP7334784B2 (ja) * 2019-08-22 2023-08-29 日本電気株式会社 ロボット制御システム、ロボット制御方法、及び、プログラム
US10999403B2 (en) 2019-09-27 2021-05-04 Red Hat, Inc. Composable infrastructure provisioning and balancing
EP4042279A4 (en) * 2019-10-10 2023-11-01 Channel One Holdings Inc. METHOD AND SYSTEMS FOR THE CONTEMPORARY EXECUTION OF COMPUTER PROCESSES
CN110650609B (zh) * 2019-10-10 2020-12-01 珠海与非科技有限公司 一种分布式储存的云服务器
US11200046B2 (en) * 2019-10-22 2021-12-14 Lenovo Enterprise Solutions (Singapore) Pte. Ltd. Managing composable compute system infrastructure with support for decoupled firmware updates
DE102020127704A1 (de) 2019-10-29 2021-04-29 Nvidia Corporation Techniken zum effizienten transferieren von daten an einem prozessor
US11080051B2 (en) 2019-10-29 2021-08-03 Nvidia Corporation Techniques for efficiently transferring data to a processor
CN112749121A (zh) * 2019-10-31 2021-05-04 中兴通讯股份有限公司 基于pcie总线的多芯片互联系统
US11342004B2 (en) * 2019-11-07 2022-05-24 Quantum Corporation System and method for rapid replacement of robotic media mover in automated media library
US10747281B1 (en) * 2019-11-19 2020-08-18 International Business Machines Corporation Mobile thermal balancing of data centers
US11782810B2 (en) * 2019-11-22 2023-10-10 Dell Products, L.P. Systems and methods for automated field replacement component configuration
US11263105B2 (en) 2019-11-26 2022-03-01 Lucid Software, Inc. Visualization tool for components within a cloud infrastructure
US11861219B2 (en) 2019-12-12 2024-01-02 Intel Corporation Buffer to reduce write amplification of misaligned write operations
US11789878B2 (en) 2019-12-19 2023-10-17 Intel Corporation Adaptive fabric allocation for local and remote emerging memories based prediction schemes
US11321259B2 (en) * 2020-02-14 2022-05-03 Sony Interactive Entertainment Inc. Network architecture providing high speed storage access through a PCI express fabric between a compute node and a storage server
US11636503B2 (en) * 2020-02-26 2023-04-25 At&T Intellectual Property I, L.P. System and method for offering network slice as a service
US11122123B1 (en) 2020-03-09 2021-09-14 International Business Machines Corporation Method for a network of storage devices
US11121941B1 (en) 2020-03-12 2021-09-14 Cisco Technology, Inc. Monitoring communications to identify performance degradation
US20210304025A1 (en) * 2020-03-24 2021-09-30 Facebook, Inc. Dynamic quality of service management for deep learning training communication
US11115497B2 (en) * 2020-03-25 2021-09-07 Intel Corporation Technologies for providing advanced resource management in a disaggregated environment
US11630696B2 (en) 2020-03-30 2023-04-18 International Business Machines Corporation Messaging for a hardware acceleration system
US11509079B2 (en) * 2020-04-06 2022-11-22 Hewlett Packard Enterprise Development Lp Blind mate connections with different sets of datums
US12001826B2 (en) 2020-04-24 2024-06-04 Intel Corporation Device firmware update techniques
CN115039072A (zh) * 2020-04-30 2022-09-09 英特尔公司 用于跨服务器阵列分布的功能即服务实现方式的编译
US11177618B1 (en) * 2020-05-14 2021-11-16 Dell Products L.P. Server blind-mate power and signal connector dock
US11295135B2 (en) * 2020-05-29 2022-04-05 Corning Research & Development Corporation Asset tracking of communication equipment via mixed reality based labeling
US11374808B2 (en) * 2020-05-29 2022-06-28 Corning Research & Development Corporation Automated logging of patching operations via mixed reality based labeling
US11947971B2 (en) * 2020-06-11 2024-04-02 Hewlett Packard Enterprise Development Lp Remote resource configuration mechanism
US11687629B2 (en) * 2020-06-12 2023-06-27 Baidu Usa Llc Method for data protection in a data processing cluster with authentication
US11360789B2 (en) 2020-07-06 2022-06-14 International Business Machines Corporation Configuration of hardware devices
CN111824668B (zh) * 2020-07-08 2022-07-19 北京极智嘉科技股份有限公司 机器人以及基于机器人的容器存入、取出方法
US11681557B2 (en) * 2020-07-31 2023-06-20 International Business Machines Corporation Systems and methods for managing resources in a hyperconverged infrastructure cluster
US11757982B2 (en) 2020-08-05 2023-09-12 Avesha, Inc. Performing load balancing self adjustment within an application environment
US12126675B2 (en) 2020-08-05 2024-10-22 Avesha, Inc. Distributed computing system employing application slice overlay networks
US20220092481A1 (en) * 2020-09-18 2022-03-24 Dell Products L.P. Integration optimization using machine learning algorithms
US11570243B2 (en) 2020-09-22 2023-01-31 Commvault Systems, Inc. Decommissioning, re-commissioning, and commissioning new metadata nodes in a working distributed data storage system
US11314687B2 (en) * 2020-09-24 2022-04-26 Commvault Systems, Inc. Container data mover for migrating data between distributed data storage systems integrated with application orchestrators
US20210011787A1 (en) * 2020-09-25 2021-01-14 Francesc Guim Bernat Technologies for scaling inter-kernel technologies for accelerator device kernels
US11405451B2 (en) * 2020-09-30 2022-08-02 Jpmorgan Chase Bank, N.A. Data pipeline architecture
US11379402B2 (en) 2020-10-20 2022-07-05 Micron Technology, Inc. Secondary device detection using a synchronous interface
US11803493B2 (en) * 2020-11-30 2023-10-31 Dell Products L.P. Systems and methods for management controller co-processor host to variable subsystem proxy
US20210092069A1 (en) * 2020-12-10 2021-03-25 Intel Corporation Accelerating multi-node performance of machine learning workloads
US11948014B2 (en) * 2020-12-15 2024-04-02 Google Llc Multi-tenant control plane management on computing platform
US11662934B2 (en) * 2020-12-15 2023-05-30 International Business Machines Corporation Migration of a logical partition between mutually non-coherent host data processing systems
US11645104B2 (en) * 2020-12-22 2023-05-09 Reliance Jio Infocomm Usa, Inc. Intelligent data plane acceleration by offloading to distributed smart network interfaces
US11994997B2 (en) * 2020-12-23 2024-05-28 Intel Corporation Memory controller to manage quality of service enforcement and migration between local and pooled memory
US11445028B2 (en) 2020-12-30 2022-09-13 Dell Products L.P. System and method for providing secure console access with multiple smart NICs using NC-SL and SPDM
US11451493B2 (en) * 2021-01-06 2022-09-20 Mellanox Technologies, Ltd. Connection management in a network adapter
US11803216B2 (en) 2021-02-03 2023-10-31 Hewlett Packard Enterprise Development Lp Contiguous plane infrastructure for computing systems
US11785735B2 (en) * 2021-02-19 2023-10-10 CyberSecure IPS, LLC Intelligent cable patching of racks to facilitate cable installation
US12112212B2 (en) 2021-02-26 2024-10-08 Google Llc Controlling system load based on memory bandwidth
US11503743B2 (en) * 2021-03-12 2022-11-15 Baidu Usa Llc High availability fluid connector for liquid cooling
US11470015B1 (en) * 2021-03-22 2022-10-11 Amazon Technologies, Inc. Allocating workloads to heterogenous worker fleets
US20220321403A1 (en) * 2021-04-02 2022-10-06 Nokia Solutions And Networks Oy Programmable network segmentation for multi-tenant fpgas in cloud infrastructures
US20220342688A1 (en) * 2021-04-26 2022-10-27 Dell Products L.P. Systems and methods for migration of virtual computing resources using smart network interface controller acceleration
US20220350675A1 (en) 2021-05-03 2022-11-03 Avesha, Inc. Distributed computing system with multi tenancy based on application slices
US11714775B2 (en) 2021-05-10 2023-08-01 Zenlayer Innovation LLC Peripheral component interconnect (PCI) hosting device
US12045643B1 (en) * 2021-06-03 2024-07-23 Amazon Technologies, Inc. Power aware load placement for sub-lineups
US20210328933A1 (en) * 2021-06-25 2021-10-21 Akhilesh Thyagaturu Network flow-based hardware allocation
US20230004786A1 (en) * 2021-06-30 2023-01-05 Micron Technology, Inc. Artificial neural networks on a deep learning accelerator
IT202100017564A1 (it) * 2021-07-02 2023-01-02 Fastweb S P A Apparato robotico per eseguire operazioni di manutenzione su un componente elettronico
EP4142442B1 (en) * 2021-08-30 2024-04-17 Ovh Cooling assembly for a data center rack and method for assembling a rack system
US20230093868A1 (en) * 2021-09-22 2023-03-30 Ridgeline, Inc. Mechanism for real-time identity resolution in a distributed system
US20230115664A1 (en) * 2021-10-08 2023-04-13 Seagate Technology Llc Resource management for disaggregated architectures
US20230121562A1 (en) * 2021-10-15 2023-04-20 Dell Products, L.P. Telemetry of artificial intelligence (ai) and/or machine learning (ml) workloads
US12066964B1 (en) * 2021-12-10 2024-08-20 Amazon Technologies, Inc. Highly available modular hardware acceleration device
US20230222012A1 (en) * 2022-01-12 2023-07-13 Dell Products L.P. Method for scaling up microservices based on api call tracing history
US11863385B2 (en) * 2022-01-21 2024-01-02 International Business Machines Corporation Optimizing container executions with network-attached hardware components of a composable disaggregated infrastructure
US11921582B2 (en) 2022-04-29 2024-03-05 Microsoft Technology Licensing, Llc Out of band method to change boot firmware configuration
CN115052055B (zh) * 2022-08-17 2022-11-11 北京左江科技股份有限公司 一种基于FPGA的网络报文的checksum卸载方法

Family Cites Families (192)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2704350B1 (fr) * 1993-04-22 1995-06-02 Bull Sa Structure physique d'un sous système de mémoire de masse.
JP3320344B2 (ja) * 1997-09-19 2002-09-03 富士通株式会社 ライブラリ装置用カートリッジ移送ロボットおよびライブラリ装置
US6158000A (en) * 1998-09-18 2000-12-05 Compaq Computer Corporation Shared memory initialization method for system having multiple processor capability
US6230265B1 (en) * 1998-09-30 2001-05-08 International Business Machines Corporation Method and system for configuring resources in a data processing system utilizing system power control information
US7287096B2 (en) * 2001-05-19 2007-10-23 Texas Instruments Incorporated Method for robust, flexible reconfiguration of transceive parameters for communication systems
US7536715B2 (en) * 2001-05-25 2009-05-19 Secure Computing Corporation Distributed firewall system and method
US6901580B2 (en) * 2001-06-22 2005-05-31 Intel Corporation Configuration parameter sequencing and sequencer
US7415723B2 (en) * 2002-06-11 2008-08-19 Pandya Ashish A Distributed network security system and a hardware processor therefor
US7408876B1 (en) * 2002-07-02 2008-08-05 Extreme Networks Method and apparatus for providing quality of service across a switched backplane between egress queue managers
US20040073834A1 (en) * 2002-10-10 2004-04-15 Kermaani Kaamel M. System and method for expanding the management redundancy of computer systems
US7386889B2 (en) * 2002-11-18 2008-06-10 Trusted Network Technologies, Inc. System and method for intrusion prevention in a communications network
US7031154B2 (en) * 2003-04-30 2006-04-18 Hewlett-Packard Development Company, L.P. Louvered rack
US7238104B1 (en) * 2003-05-02 2007-07-03 Foundry Networks, Inc. System and method for venting air from a computer casing
US7146511B2 (en) * 2003-10-07 2006-12-05 Hewlett-Packard Development Company, L.P. Rack equipment application performance modification system and method
US20050132084A1 (en) * 2003-12-10 2005-06-16 Heung-For Cheng Method and apparatus for providing server local SMBIOS table through out-of-band communication
US7552217B2 (en) 2004-04-07 2009-06-23 Intel Corporation System and method for Automatic firmware image recovery for server management operational code
US7809836B2 (en) 2004-04-07 2010-10-05 Intel Corporation System and method for automating bios firmware image recovery using a non-host processor and platform policy to select a donor system
US7421535B2 (en) * 2004-05-10 2008-09-02 International Business Machines Corporation Method for demoting tracks from cache
JP4335760B2 (ja) * 2004-07-08 2009-09-30 富士通株式会社 ラックマウント型収納ユニットおよびラックマウント型ディスクアレイ装置
US7685319B2 (en) * 2004-09-28 2010-03-23 Cray Canada Corporation Low latency communication via memory windows
WO2006074239A2 (en) * 2005-01-05 2006-07-13 Xtremedata, Inc. Systems and methods for providing co-processors to computing systems
US20110016214A1 (en) * 2009-07-15 2011-01-20 Cluster Resources, Inc. System and method of brokering cloud computing resources
US7634584B2 (en) * 2005-04-27 2009-12-15 Solarflare Communications, Inc. Packet validation in virtual network interface architecture
US9135074B2 (en) * 2005-05-19 2015-09-15 Hewlett-Packard Development Company, L.P. Evaluating performance of workload manager based on QoS to representative workload and usage efficiency of shared resource for plurality of minCPU and maxCPU allocation values
US8799980B2 (en) * 2005-11-16 2014-08-05 Juniper Networks, Inc. Enforcement of network device configuration policies within a computing environment
TW200720941A (en) * 2005-11-18 2007-06-01 Inventec Corp Host computer memory configuration data remote access method and system
US7493419B2 (en) * 2005-12-13 2009-02-17 International Business Machines Corporation Input/output workload fingerprinting for input/output schedulers
US8713551B2 (en) * 2006-01-03 2014-04-29 International Business Machines Corporation Apparatus, system, and method for non-interruptively updating firmware on a redundant hardware controller
US20070271560A1 (en) * 2006-05-18 2007-11-22 Microsoft Corporation Deploying virtual machine to host based on workload characterizations
US7472211B2 (en) * 2006-07-28 2008-12-30 International Business Machines Corporation Blade server switch module using out-of-band signaling to detect the physical location of an active drive enclosure device
US8098658B1 (en) * 2006-08-01 2012-01-17 Hewett-Packard Development Company, L.P. Power-based networking resource allocation
US8010565B2 (en) * 2006-10-16 2011-08-30 Dell Products L.P. Enterprise rack management method, apparatus and media
US8068351B2 (en) * 2006-11-10 2011-11-29 Oracle America, Inc. Cable management system
US20090089564A1 (en) * 2006-12-06 2009-04-02 Brickell Ernie F Protecting a Branch Instruction from Side Channel Vulnerabilities
US8112524B2 (en) * 2007-01-15 2012-02-07 International Business Machines Corporation Recommending moving resources in a partitioned computer
US7738900B1 (en) 2007-02-15 2010-06-15 Nextel Communications Inc. Systems and methods of group distribution for latency sensitive applications
US8140719B2 (en) * 2007-06-21 2012-03-20 Sea Micro, Inc. Dis-aggregated and distributed data-center architecture using a direct interconnect fabric
CN101431432A (zh) * 2007-11-06 2009-05-13 联想(北京)有限公司 刀片服务器
US8078865B2 (en) * 2007-11-20 2011-12-13 Dell Products L.P. Systems and methods for configuring out-of-band bios settings
US8214467B2 (en) * 2007-12-14 2012-07-03 International Business Machines Corporation Migrating port-specific operating parameters during blade server failover
US20100267376A1 (en) * 2007-12-17 2010-10-21 Nokia Corporation Accessory Configuration and Management
US8645965B2 (en) * 2007-12-31 2014-02-04 Intel Corporation Supporting metered clients with manycore through time-limited partitioning
US8225159B1 (en) * 2008-04-25 2012-07-17 Netapp, Inc. Method and system for implementing power savings features on storage devices within a storage subsystem
US8166263B2 (en) * 2008-07-03 2012-04-24 Commvault Systems, Inc. Continuous data protection over intermittent connections, such as continuous data backup for laptops or wireless devices
US20100125695A1 (en) * 2008-11-15 2010-05-20 Nanostar Corporation Non-volatile memory storage system
US20100091458A1 (en) * 2008-10-15 2010-04-15 Mosier Jr David W Electronics chassis with angled card cage
US8954977B2 (en) * 2008-12-09 2015-02-10 Intel Corporation Software-based thread remapping for power savings
US8798045B1 (en) * 2008-12-29 2014-08-05 Juniper Networks, Inc. Control plane architecture for switch fabrics
US20100229175A1 (en) * 2009-03-05 2010-09-09 International Business Machines Corporation Moving Resources In a Computing Environment Having Multiple Logically-Partitioned Computer Systems
CN102439580A (zh) * 2009-03-20 2012-05-02 普林斯顿大学托管委员会 用于网络加速以及对高速缓存文件系统进行高效索引编制的系统及方法
US8321870B2 (en) * 2009-08-14 2012-11-27 General Electric Company Method and system for distributed computation having sub-task processing and sub-solution redistribution
US20110055838A1 (en) * 2009-08-28 2011-03-03 Moyes William A Optimized thread scheduling via hardware performance monitoring
WO2011045863A1 (ja) * 2009-10-16 2011-04-21 富士通株式会社 電子装置及び電子装置の筐体
CN101706802B (zh) * 2009-11-24 2013-06-05 成都市华为赛门铁克科技有限公司 一种数据写入、修改及恢复的方法、装置及服务器
US9129052B2 (en) * 2009-12-03 2015-09-08 International Business Machines Corporation Metering resource usage in a cloud computing environment
CN102135923A (zh) * 2010-01-21 2011-07-27 鸿富锦精密工业（深圳）有限公司 将操作系统集成到bios芯片的方法及启动该操作系统的方法
US8638553B1 (en) * 2010-03-31 2014-01-28 Amazon Technologies, Inc. Rack system cooling with inclined computing devices
US8601297B1 (en) * 2010-06-18 2013-12-03 Google Inc. Systems and methods for energy proportional multiprocessor networks
US8171142B2 (en) * 2010-06-30 2012-05-01 Vmware, Inc. Data center inventory management using smart racks
IT1401647B1 (it) * 2010-07-09 2013-08-02 Campatents B V Metodo per monitorare cambi di configurazione di un dispostivo di controllo di una macchina automatica
US8259450B2 (en) * 2010-07-21 2012-09-04 Birchbridge Incorporated Mobile universal hardware platform
US9428336B2 (en) * 2010-07-28 2016-08-30 Par Systems, Inc. Robotic storage and retrieval systems
US8824222B2 (en) * 2010-08-13 2014-09-02 Rambus Inc. Fast-wake memory
US8914805B2 (en) * 2010-08-31 2014-12-16 International Business Machines Corporation Rescheduling workload in a hybrid computing environment
US8489939B2 (en) * 2010-10-25 2013-07-16 At&T Intellectual Property I, L.P. Dynamically allocating multitier applications based upon application requirements and performance and reliability of resources
WO2012057546A2 (ko) * 2010-10-28 2012-05-03 엘지전자 주식회사 무선랜 시스템에서 데이터 프레임 송수신 방법 및 장치
US8838286B2 (en) * 2010-11-04 2014-09-16 Dell Products L.P. Rack-level modular server and storage framework
US8762668B2 (en) * 2010-11-18 2014-06-24 Hitachi, Ltd. Multipath switching over multiple storage systems
US9563479B2 (en) * 2010-11-30 2017-02-07 Red Hat, Inc. Brokering optimized resource supply costs in host cloud-based network using predictive workloads
CN102693181A (zh) * 2011-03-25 2012-09-26 鸿富锦精密工业（深圳）有限公司 分位刷写系统及方法
US9405550B2 (en) * 2011-03-31 2016-08-02 International Business Machines Corporation Methods for the transmission of accelerator commands and corresponding command structure to remote hardware accelerator engines over an interconnect link
US20120303322A1 (en) * 2011-05-23 2012-11-29 Rego Charles W Incorporating memory and io cycle information into compute usage determinations
CN103748560A (zh) * 2011-07-01 2014-04-23 惠普发展公司，有限责任合伙企业 用于管理计算资源的方法和系统
US9317336B2 (en) * 2011-07-27 2016-04-19 Alcatel Lucent Method and apparatus for assignment of virtual resources within a cloud environment
US8713257B2 (en) * 2011-08-26 2014-04-29 Lsi Corporation Method and system for shared high speed cache in SAS switches
US8755176B2 (en) * 2011-10-12 2014-06-17 Xyratex Technology Limited Data storage system, an energy module and a method of providing back-up power to a data storage system
US9237107B2 (en) * 2011-11-15 2016-01-12 New Jersey Institute Of Technology Fair quantized congestion notification (FQCN) to mitigate transport control protocol (TCP) throughput collapse in data center networks
EP2783287A4 (en) * 2011-11-23 2014-12-03 Ericsson Telefon Ab L M METHOD AND DEVICE FOR DISTRIBUTED PROCESSING TASKS
DE102011119693A1 (de) * 2011-11-29 2013-05-29 Universität Heidelberg System, Computer-implementiertes Verfahren und Computerprogrammprodukt zur direkten Kommunikation zwischen Hardwarebeschleunigern in einem Computercluster
US8732291B2 (en) * 2012-01-13 2014-05-20 Accenture Global Services Limited Performance interference model for managing consolidated workloads in QOS-aware clouds
US20130185729A1 (en) * 2012-01-13 2013-07-18 Rutgers, The State University Of New Jersey Accelerating resource allocation in virtualized environments using workload classes and/or workload signatures
US9336061B2 (en) * 2012-01-14 2016-05-10 International Business Machines Corporation Integrated metering of service usage for hybrid clouds
US9367360B2 (en) * 2012-01-30 2016-06-14 Microsoft Technology Licensing, Llc Deploying a hardware inventory as a cloud-computing stamp
TWI462017B (zh) * 2012-02-24 2014-11-21 Wistron Corp 伺服器部署系統及資料更新的方法
WO2013180691A1 (en) * 2012-05-29 2013-12-05 Intel Corporation Peer-to-peer interrupt signaling between devices coupled via interconnects
CN102694863B (zh) * 2012-05-30 2015-08-26 电子科技大学 基于负载调整和系统容错的分布式存储系统的实现方法
JP5983045B2 (ja) * 2012-05-30 2016-08-31 富士通株式会社 ライブラリ装置
US8832268B1 (en) * 2012-08-16 2014-09-09 Amazon Technologies, Inc. Notification and resolution of infrastructure issues
CN107678412B (zh) * 2012-10-08 2020-05-15 费希尔-罗斯蒙特系统公司 用利用覆盖的派生和链接的定义配置图形元素对象的方法
US9202040B2 (en) 2012-10-10 2015-12-01 Globalfoundries Inc. Chip authentication using multi-domain intrinsic identifiers
US9047417B2 (en) * 2012-10-29 2015-06-02 Intel Corporation NUMA aware network interface
US20140185225A1 (en) * 2012-12-28 2014-07-03 Joel Wineland Advanced Datacenter Designs
US9367419B2 (en) 2013-01-08 2016-06-14 American Megatrends, Inc. Implementation on baseboard management controller of single out-of-band communication access to multiple managed computer nodes
WO2014113451A1 (en) * 2013-01-15 2014-07-24 Intel Corporation A rack assembly structure
US9201837B2 (en) * 2013-03-13 2015-12-01 Futurewei Technologies, Inc. Disaggregated server architecture for data centers
US9582010B2 (en) * 2013-03-14 2017-02-28 Rackspace Us, Inc. System and method of rack management
US9634958B2 (en) * 2013-04-02 2017-04-25 Amazon Technologies, Inc. Burst capacity for user-defined pools
US9104562B2 (en) * 2013-04-05 2015-08-11 International Business Machines Corporation Enabling communication over cross-coupled links between independently managed compute and storage networks
CN103281351B (zh) * 2013-04-19 2016-12-28 武汉方寸科技有限公司 一种高效能遥感数据处理与分析的云服务平台
US20140317267A1 (en) * 2013-04-22 2014-10-23 Advanced Micro Devices, Inc. High-Density Server Management Controller
US20140337496A1 (en) * 2013-05-13 2014-11-13 Advanced Micro Devices, Inc. Embedded Management Controller for High-Density Servers
CN103294521B (zh) * 2013-05-30 2016-08-10 天津大学 一种降低数据中心通信负载及能耗的方法
US9436600B2 (en) * 2013-06-11 2016-09-06 Svic No. 28 New Technology Business Investment L.L.P. Non-volatile memory storage for multi-channel memory system
US20150033222A1 (en) 2013-07-25 2015-01-29 Cavium, Inc. Network Interface Card with Virtual Switch and Traffic Flow Policy Enforcement
US10069686B2 (en) * 2013-09-05 2018-09-04 Pismo Labs Technology Limited Methods and systems for managing a device through a manual information input module
US9306861B2 (en) * 2013-09-26 2016-04-05 Red Hat Israel, Ltd. Automatic promiscuous forwarding for a bridge
US9413713B2 (en) * 2013-12-05 2016-08-09 Cisco Technology, Inc. Detection of a misconfigured duplicate IP address in a distributed data center network fabric
US9792243B2 (en) * 2013-12-26 2017-10-17 Intel Corporation Computer architecture to provide flexibility and/or scalability
US9705798B1 (en) * 2014-01-07 2017-07-11 Google Inc. Systems and methods for routing data through data centers using an indirect generalized hypercube network
US9444695B2 (en) * 2014-01-30 2016-09-13 Xerox Corporation Methods and systems for scheduling a task
WO2015130613A1 (en) * 2014-02-27 2015-09-03 Intel Corporation Techniques to allocate configurable computing resources
CN105940637B (zh) * 2014-02-27 2020-03-31 英特尔公司 用于机架级架构计算系统的工作负荷优化、调度和布置的方法和装置
US9363926B1 (en) * 2014-03-17 2016-06-07 Amazon Technologies, Inc. Modular mass storage system with staggered backplanes
US9925492B2 (en) * 2014-03-24 2018-03-27 Mellanox Technologies, Ltd. Remote transactional memory
US10218645B2 (en) * 2014-04-08 2019-02-26 Mellanox Technologies, Ltd. Low-latency processing in a network node
US9503391B2 (en) * 2014-04-11 2016-11-22 Telefonaktiebolaget Lm Ericsson (Publ) Method and system for network function placement
WO2015167437A1 (en) * 2014-04-28 2015-11-05 New Jersey Institute Of Technology Congestion management for datacenter network
US9081828B1 (en) * 2014-04-30 2015-07-14 Igneous Systems, Inc. Network addressable storage controller with storage drive profile comparison
TWI510933B (zh) * 2014-05-13 2015-12-01 Acer Inc 遠端存取資料的方法以及本地端裝置
CN109067833B (zh) * 2014-05-22 2021-06-22 华为技术有限公司 一种节点互连装置、资源控制节点和服务器系统
US9477279B1 (en) * 2014-06-02 2016-10-25 Datadirect Networks, Inc. Data storage system with active power management and method for monitoring and dynamical control of power sharing between devices in data storage system
US9602351B2 (en) * 2014-06-06 2017-03-21 Microsoft Technology Licensing, Llc Proactive handling of network faults
US9684575B2 (en) * 2014-06-23 2017-06-20 Liqid Inc. Failover handling in modular switched fabric for data storage systems
US10382279B2 (en) * 2014-06-30 2019-08-13 Emc Corporation Dynamically composed compute nodes comprising disaggregated components
US10122605B2 (en) * 2014-07-09 2018-11-06 Cisco Technology, Inc Annotation of network activity through different phases of execution
US9892079B2 (en) * 2014-07-25 2018-02-13 Rajiv Ganth Unified converged network, storage and compute system
US9262144B1 (en) * 2014-08-20 2016-02-16 International Business Machines Corporation Deploying virtual machine instances of a pattern to regions of a hierarchical tier using placement policies and constraints
US9684531B2 (en) * 2014-08-21 2017-06-20 International Business Machines Corporation Combining blade servers based on workload characteristics
CN104168332A (zh) * 2014-09-01 2014-11-26 广东电网公司信息中心 高性能计算中负载均衡与节点状态监控方法
US9858104B2 (en) * 2014-09-24 2018-01-02 Pluribus Networks, Inc. Connecting fabrics via switch-to-switch tunneling transparent to network servers
US10630767B1 (en) * 2014-09-30 2020-04-21 Amazon Technologies, Inc. Hardware grouping based computing resource allocation
US10061599B1 (en) * 2014-10-16 2018-08-28 American Megatrends, Inc. Bus enumeration acceleration
US9098451B1 (en) * 2014-11-21 2015-08-04 Igneous Systems, Inc. Shingled repair set for writing data
US9886306B2 (en) * 2014-11-21 2018-02-06 International Business Machines Corporation Cross-platform scheduling with long-term fairness and platform-specific optimization
WO2016090485A1 (en) * 2014-12-09 2016-06-16 Cirba Ip Inc. System and method for routing computing workloads based on proximity
US20160173600A1 (en) 2014-12-15 2016-06-16 Cisco Technology, Inc. Programmable processing engine for a virtual interface controller
US10057186B2 (en) * 2015-01-09 2018-08-21 International Business Machines Corporation Service broker for computational offloading and improved resource utilization
EP3046028B1 (en) * 2015-01-15 2020-02-19 Alcatel Lucent Load-balancing and scaling of cloud resources by migrating a data session
US10114692B2 (en) * 2015-01-27 2018-10-30 Quantum Corporation High/low energy zone data storage
US10234930B2 (en) * 2015-02-13 2019-03-19 Intel Corporation Performing power management in a multicore processor
JP2016167143A (ja) * 2015-03-09 2016-09-15 富士通株式会社 情報処理システムおよび情報処理システムの制御方法
US9276900B1 (en) * 2015-03-19 2016-03-01 Igneous Systems, Inc. Network bootstrapping for a distributed storage system
US10848408B2 (en) * 2015-03-26 2020-11-24 Vmware, Inc. Methods and apparatus to control computing resource utilization of monitoring agents
US10606651B2 (en) * 2015-04-17 2020-03-31 Microsoft Technology Licensing, Llc Free form expression accelerator with thread length-based thread assignment to clustered soft processor cores that share a functional circuit
US10019388B2 (en) * 2015-04-28 2018-07-10 Liqid Inc. Enhanced initialization for data storage assemblies
US9910664B2 (en) * 2015-05-04 2018-03-06 American Megatrends, Inc. System and method of online firmware update for baseboard management controller (BMC) devices
US20160335209A1 (en) * 2015-05-11 2016-11-17 Quanta Computer Inc. High-speed data transmission using pcie protocol
US9696781B2 (en) * 2015-05-28 2017-07-04 Cisco Technology, Inc. Automated power control for reducing power usage in communications networks
US11203486B2 (en) * 2015-06-02 2021-12-21 Alert Innovation Inc. Order fulfillment system
US9792248B2 (en) * 2015-06-02 2017-10-17 Microsoft Technology Licensing, Llc Fast read/write between networked computers via RDMA-based RPC requests
US9606836B2 (en) * 2015-06-09 2017-03-28 Microsoft Technology Licensing, Llc Independently networkable hardware accelerators for increased workflow optimization
CN204887839U (zh) * 2015-07-23 2015-12-16 中兴通讯股份有限公司 一种单板模块级水冷系统
US10055218B2 (en) * 2015-08-11 2018-08-21 Quanta Computer Inc. System and method for adding and storing groups of firmware default settings
US10348574B2 (en) * 2015-08-17 2019-07-09 Vmware, Inc. Hardware management systems for disaggregated rack architectures in virtual server rack deployments
US10736239B2 (en) * 2015-09-22 2020-08-04 Z-Impact, Inc. High performance computing rack and storage system with forced cooling
US10387209B2 (en) * 2015-09-28 2019-08-20 International Business Machines Corporation Dynamic transparent provisioning of resources for application specific resources
US10162793B1 (en) * 2015-09-29 2018-12-25 Amazon Technologies, Inc. Storage adapter device for communicating with network storage
US9888607B2 (en) * 2015-09-30 2018-02-06 Seagate Technology Llc Self-biasing storage device sled
US10216643B2 (en) * 2015-11-23 2019-02-26 International Business Machines Corporation Optimizing page table manipulations
US9811347B2 (en) * 2015-12-14 2017-11-07 Dell Products, L.P. Managing dependencies for human interface infrastructure (HII) devices
US10028401B2 (en) * 2015-12-18 2018-07-17 Microsoft Technology Licensing, Llc Sidewall-accessible dense storage rack
US20170180220A1 (en) * 2015-12-18 2017-06-22 Intel Corporation Techniques to Generate Workload Performance Fingerprints for Cloud Infrastructure Elements
US10452467B2 (en) 2016-01-28 2019-10-22 Intel Corporation Automatic model-based computing environment performance monitoring
US10581711B2 (en) * 2016-01-28 2020-03-03 Oracle International Corporation System and method for policing network traffic flows using a ternary content addressable memory in a high performance computing environment
EP3420450A1 (en) * 2016-02-23 2019-01-02 Telefonaktiebolaget LM Ericsson (publ) Methods and modules relating to allocation of host machines
US20170257970A1 (en) * 2016-03-04 2017-09-07 Radisys Corporation Rack having uniform bays and an optical interconnect system for shelf-level, modular deployment of sleds enclosing information technology equipment
US9811281B2 (en) * 2016-04-07 2017-11-07 International Business Machines Corporation Multi-tenant memory service for memory pool architectures
US10701141B2 (en) * 2016-06-30 2020-06-30 International Business Machines Corporation Managing software licenses in a disaggregated environment
US11706895B2 (en) * 2016-07-19 2023-07-18 Pure Storage, Inc. Independent scaling of compute resources and storage resources in a storage system
US10034407B2 (en) * 2016-07-22 2018-07-24 Intel Corporation Storage sled for a data center
US10234833B2 (en) * 2016-07-22 2019-03-19 Intel Corporation Technologies for predicting power usage of a data center
US20180034908A1 (en) * 2016-07-27 2018-02-01 Alibaba Group Holding Limited Disaggregated storage and computation system
US10365852B2 (en) * 2016-07-29 2019-07-30 Vmware, Inc. Resumable replica resynchronization
US10193997B2 (en) 2016-08-05 2019-01-29 Dell Products L.P. Encoded URI references in restful requests to facilitate proxy aggregation
US10127107B2 (en) * 2016-08-14 2018-11-13 Nxp Usa, Inc. Method for performing data transaction that selectively enables memory bank cuts and memory device therefor
US10108560B1 (en) * 2016-09-14 2018-10-23 Evol1-Ip, Llc Ethernet-leveraged hyper-converged infrastructure
US10303458B2 (en) * 2016-09-29 2019-05-28 Hewlett Packard Enterprise Development Lp Multi-platform installer
US10776342B2 (en) * 2016-11-18 2020-09-15 Tuxena, Inc. Systems and methods for recovering lost clusters from a mounted volume
US10726131B2 (en) * 2016-11-21 2020-07-28 Facebook, Inc. Systems and methods for mitigation of permanent denial of service attacks
EP3549350A4 (en) * 2016-11-29 2021-03-24 Intel Corporation MILLIMETRIC WAVE CHASSIS INTERCONNECTION TECHNOLOGIES
US20180150256A1 (en) * 2016-11-29 2018-05-31 Intel Corporation Technologies for data deduplication in disaggregated architectures
US10503671B2 (en) * 2016-12-29 2019-12-10 Oath Inc. Controlling access to a shared resource
US10282549B2 (en) * 2017-03-07 2019-05-07 Hewlett Packard Enterprise Development Lp Modifying service operating system of baseboard management controller
US10967465B2 (en) * 2017-03-08 2021-04-06 Bwxt Nuclear Energy, Inc. Apparatus and method for baffle bolt repair
US20180288152A1 (en) * 2017-04-01 2018-10-04 Anjaneya R. Chagam Reddy Storage dynamic accessibility mechanism method and apparatus
US10331581B2 (en) * 2017-04-10 2019-06-25 Hewlett Packard Enterprise Development Lp Virtual channel and resource assignment
US10355939B2 (en) * 2017-04-13 2019-07-16 International Business Machines Corporation Scalable data center network topology on distributed switch
US10467052B2 (en) * 2017-05-01 2019-11-05 Red Hat, Inc. Cluster topology aware container scheduling for efficient data transfer
US10303615B2 (en) * 2017-06-16 2019-05-28 Hewlett Packard Enterprise Development Lp Matching pointers across levels of a memory hierarchy
US20190166032A1 (en) * 2017-11-30 2019-05-30 American Megatrends, Inc. Utilization based dynamic provisioning of rack computing resources
US10447273B1 (en) * 2018-09-11 2019-10-15 Advanced Micro Devices, Inc. Dynamic virtualized field-programmable gate array resource control for performance and reliability
US11201818B2 (en) * 2019-04-04 2021-12-14 Cisco Technology, Inc. System and method of providing policy selection in a network

Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11595277B2 (en) 2016-07-22 2023-02-28 Intel Corporation Technologies for switching network traffic in a data center
US10791384B2 (en) 2016-07-22 2020-09-29 Intel Corporation Technologies for switching network traffic in a data center
US10802229B2 (en) 2016-07-22 2020-10-13 Intel Corporation Technologies for switching network traffic in a data center
US12081323B2 (en) 2016-07-22 2024-09-03 Intel Corporation Techniques to control system updates and configuration changes via the cloud
US11128553B2 (en) 2016-07-22 2021-09-21 Intel Corporation Technologies for switching network traffic in a data center
US12040889B2 (en) 2016-07-22 2024-07-16 Intel Corporation Technologies for switching network traffic in a data center
US10785549B2 (en) 2016-07-22 2020-09-22 Intel Corporation Technologies for switching network traffic in a data center
US11907557B2 (en) 2016-11-29 2024-02-20 Intel Corporation Technologies for dividing work across accelerator devices
US11977923B2 (en) 2016-11-29 2024-05-07 Intel Corporation Cloud-based scale-up system composition
US11995330B2 (en) 2016-11-29 2024-05-28 Intel Corporation Technologies for providing accelerated functions as a service in a disaggregated architecture
US11137922B2 (en) 2016-11-29 2021-10-05 Intel Corporation Technologies for providing accelerated functions as a service in a disaggregated architecture
US11093311B2 (en) * 2016-11-29 2021-08-17 Intel Corporation Technologies for monitoring node cluster health
US11650598B2 (en) * 2017-12-30 2023-05-16 Telescent Inc. Automated physical network management system utilizing high resolution RFID, optical scans and mobile robotic actuator
US11436113B2 (en) * 2018-06-28 2022-09-06 Twitter, Inc. Method and system for maintaining storage device failure tolerance in a composable infrastructure
US20220413976A1 (en) * 2018-06-28 2022-12-29 Twitter, Inc. Method and System for Maintaining Storage Device Failure Tolerance in a Composable Infrastructure
US11411994B2 (en) * 2019-04-05 2022-08-09 Cisco Technology, Inc. Discovering trustworthy devices using attestation and mutual attestation
US11956273B2 (en) 2019-04-05 2024-04-09 Cisco Technology, Inc. Discovering trustworthy devices using attestation and mutual attestation
US20220129601A1 (en) * 2020-10-26 2022-04-28 Oracle International Corporation Techniques for generating a configuration for electrically isolating fault domains in a data center

Also Published As

Publication number Publication date
CN109428889A (zh) 2019-03-05
US20190069433A1 (en) 2019-02-28
US20190065415A1 (en) 2019-02-28
CN109426646B (zh) 2024-07-30
WO2019046620A1 (en) 2019-03-07
CN109426316A (zh) 2019-03-05
CN109428841B (zh) 2024-09-06
EP3676708A1 (en) 2020-07-08
US20190067848A1 (en) 2019-02-28
US11030017B2 (en) 2021-06-08
WO2019046639A1 (en) 2019-03-07
US20190065083A1 (en) 2019-02-28
DE112018004798T5 (de) 2020-06-18
US20190068523A1 (en) 2019-02-28
US20190065212A1 (en) 2019-02-28
US11422867B2 (en) 2022-08-23
US20200192710A1 (en) 2020-06-18
US20190068509A1 (en) 2019-02-28
WO2019045928A1 (en) 2019-03-07
US20190065281A1 (en) 2019-02-28
CN109428843A (zh) 2019-03-05
CN109426568A (zh) 2019-03-05
US10888016B2 (en) 2021-01-05
CN109426633A (zh) 2019-03-05
US11055149B2 (en) 2021-07-06
US20190068693A1 (en) 2019-02-28
CN109428841A (zh) 2019-03-05
US11467885B2 (en) 2022-10-11
US20200257566A1 (en) 2020-08-13
US20190068464A1 (en) 2019-02-28
US20190068696A1 (en) 2019-02-28
US20190065231A1 (en) 2019-02-28
US20190065401A1 (en) 2019-02-28
EP3676708A4 (en) 2021-06-02
US20190065260A1 (en) 2019-02-28
US11392425B2 (en) 2022-07-19
WO2019045929A1 (en) 2019-03-07
US20190068444A1 (en) 2019-02-28
US20190062053A1 (en) 2019-02-28
WO2019045930A1 (en) 2019-03-07
US20190065112A1 (en) 2019-02-28
US20190068521A1 (en) 2019-02-28
US11614979B2 (en) 2023-03-28
US20190065261A1 (en) 2019-02-28
US11748172B2 (en) 2023-09-05
CN109426646A (zh) 2019-03-05
US11416309B2 (en) 2022-08-16
CN109426630A (zh) 2019-03-05
US20190068698A1 (en) 2019-02-28
US20190065172A1 (en) 2019-02-28

Similar Documents

Publication Publication Date Title
US20190068466A1 (en) Technologies for auto-discovery of fault domains
US11354053B2 (en) Technologies for lifecycle management with remote firmware
US11689436B2 (en) Techniques to configure physical compute resources for workloads via circuit switching
US11128555B2 (en) Methods and apparatus for SDI support for automatic and transparent migration
US11093311B2 (en) Technologies for monitoring node cluster health

Legal Events

Date Code Title Description
AS Assignment

Owner name: INTEL CORPORATION, CALIFORNIA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:CHAGAM REDDY, ANJANEYA REDDY;REEL/FRAME:045036/0040

Effective date: 20180110

STCT Information on status: administrative procedure adjustment

Free format text: PROSECUTION SUSPENDED

STPP Information on status: patent application and granting procedure in general

Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION

STPP Information on status: patent application and granting procedure in general

Free format text: NON FINAL ACTION MAILED

STPP Information on status: patent application and granting procedure in general

Free format text: RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER

STPP Information on status: patent application and granting procedure in general

Free format text: FINAL REJECTION MAILED

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION