WO2022271398A1 - Services périphériques hybrides - Google Patents

Services périphériques hybrides Download PDF

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Publication number
WO2022271398A1
WO2022271398A1 PCT/US2022/030982 US2022030982W WO2022271398A1 WO 2022271398 A1 WO2022271398 A1 WO 2022271398A1 US 2022030982 W US2022030982 W US 2022030982W WO 2022271398 A1 WO2022271398 A1 WO 2022271398A1
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Prior art keywords
computing
edge
service provider
network
applications
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PCT/US2022/030982
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English (en)
Inventor
Hema PENTAKOTA
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Microsoft Technology Licensing, Llc
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.)
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Priority claimed from US17/491,331 external-priority patent/US20220417172A1/en
Application filed by Microsoft Technology Licensing, Llc filed Critical Microsoft Technology Licensing, Llc
Publication of WO2022271398A1 publication Critical patent/WO2022271398A1/fr

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Classifications

    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/5013—Request control
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/502—Proximity

Definitions

  • a data center may house computer systems and various networking, storage, and other related components.
  • Data centers may, for example, be used by service providers to provide computing services to businesses and individuals as a remote computing service or provide “software as a service” (e.g., cloud computing).
  • Service providers may also utilize edge sites that may include a geographically distributed group of servers and other devices that work together to provide efficient delivery of content to end-users of data center services, with the goal being to provide services with high availability and improved latencies.
  • Multi-access Edge Computing is a type of edge computing that uses cellular networks and 5G to extend cloud services to local deployments.
  • Edge sites enable a data center to extend cloud services to local deployments using a distributed architecture that enables federated options for local and remote data and control management. It is desirable to provide the highest level of computing availability to users at their location, on premises, or via an edge site while at the same time providing performance and minimizing cost.
  • a hybrid edge service allows deployment of any application from any cloud to the closest edge site where the application is consumed.
  • the disclosed hybrid edge service enables private MEC deployments at locations such as venues and enterprises without the need to deploy infrastructure for MEC in those locations. This allows public and private MEC solutions to work seamlessly using various technologies such as 4G, 5G, MEC, and Industry 4.0.
  • the disclosed hybrid edge service also enables seamless orchestration of applications residing in any cloud to edge sites closest to the actual location of consumption.
  • third party micro data center locations may be used to enable seamless and efficient access, where the cloud provider’s software stack may be used as the platform.
  • the cloud provider’s software stack may be used as the platform.
  • Such a site may support a multi-tenant deployment of mobile network functions and MEC applications for operators, enterprises (public/private), and venues.
  • the disclosed hybrid edge service can provide the ability to enable MEC services for venues and enterprises (public/private) without the need to deploy infrastructure at those locations. Additionally, the disclosed hybrid edge service may provide the ability to deploy any application currently running in any cloud to the closest edge site based on consumption.
  • FIG. l is a diagram illustrating an example architecture in accordance with the present disclosure
  • FIG. 2A is a diagram illustrating an example architecture in accordance with the present disclosure
  • FIG. 2B is a diagram illustrating an example architecture in accordance with the present disclosure.
  • FIG. 3 is a diagram illustrating a data center in accordance with the present disclosure
  • FIG. 4 is a diagram illustrating a data center and local resources in accordance with the present disclosure
  • FIG. 5A is a diagram illustrating an architecture for providing hybrid edge services in accordance with the present disclosure
  • FIG. 5B is a diagram illustrating an architecture for providing hybrid edge services in accordance with the present disclosure.
  • FIG. 5C is a diagram illustrating an architecture for providing hybrid edge services in accordance with the present disclosure.
  • FIG. 5D is a diagram illustrating an architecture for providing hybrid edge services in accordance with the present disclosure.
  • FIG. 5E is a diagram illustrating an architecture for providing hybrid edge services in accordance with the present disclosure
  • FIG. 6 is a diagram illustrating an architecture for managing resources in accordance with the present disclosure
  • FIG. 7 is a flowchart depicting an example procedure for managing resources in accordance with the present disclosure.
  • FIG. 8 is an example computing system in accordance with the present disclosure.
  • various computing and network services may be configured to enable the service provider to deploy their footprints closer to the user’ s premises, thereby extending the reach of the computing and network services closer to the user premises.
  • an enterprise that provides network carrier services may want computing services located closer to their networks or their customers, a manufacturer may want to deploy computing resources closer to their facilities, or users may want to access applications at various venues such as stadiums or malls.
  • Users of virtualized computing resources may benefit in many ways by deploying resources such as virtual machines on resources that are located closer to their premises.
  • resources may refer to various types of multi-dimensional resources including applications, CPU, GPU, storage, etc.
  • Efficient management of the end-to-end capability services by the service provider can enable an experience that is seamless and consistent when using edge sites.
  • the integration of local and remote resources with a comprehensive remote resource management approach can minimize the overhead for the service provider by maximizing the capabilities of the edge site.
  • the effective distribution of the management functions can be determined based on the implications for various performance and security implications such as latency and data security.
  • the reach between remote and on-premise computing resources can be complex.
  • a remote computing service provider may implement multiple service regions and microregions. Some solutions may include dedicating a region and/or a microregion for a particular user. Other solutions may involve the remote service provider providing the remote service provider’s hardware resources to the user for installation on their premises.
  • the present disclosure enables the efficient distribution and accessibility of applications and workloads to computing resources located at various hierarchies between user devices, user premises, edge sites, and the cloud to efficiently deliver services and allow the service provider to implement a comprehensive solution to the user’s needs in an optimized manner.
  • the computing resources may be provided by the user, provided by network operators, or provided by the remote service provider.
  • Various embodiments disclosed herein include the integrated and federated management of the entire end-to-end capability
  • Management of the end-to-end capability service by the remote service provider can enable an experience that is seamless and more consistent between local, private, and public user footprints.
  • the integration of local and remote resources with a comprehensive resource management approach can minimize the overhead for operators, enterprises, and other end users, who will not need to perform complex management tasks and who will not need to install complex and expensive infrastructure.
  • the effective distribution of the applications and workloads can be determined based on the implications for various performance and security implications such as latency and data security.
  • the disclosed hybrid edge service with edge sites can enable serverless MEC applications in a flexible manner, for example for various venues such as shopping centers and mobile use cases such as connected vehicles.
  • the disclosed hybrid edge service may also be implemented in some examples as a pay-per-use or other flexible arrangement.
  • the edge sites may host at least a portion of edge zones of a cloud service provider.
  • the edge sites may also host the service provider’s software stack on third party hardware, thereby taking advantage of existing sites of the third party.
  • the edge site may be owned and managed by the cloud service provider or outsourced by the cloud service provider to the third party.
  • the edge site may provide multi-tenant hosting of applications for multiple operators and/or enterprises (public and private).
  • the applications may be stored or hosted at data centers of the cloud provider and can be managed and orchestrated to provide user access at edge sites by an application manager that can reside in the cloud.
  • access to an application by a user or user group such as a hospital, shopping center, stadium, education center, etc. may be provided by a proximate edge site then rather than the user having to host and build out a MEC at each of these venues.
  • the disclosed methods for providing access to applications via the edge sites may utilize 4G/5G networks at those locations.
  • an augmented reality (AR) application may be provided for sports at a stadium venue.
  • the AR application and content may be provided by an edge site near the stadium and users in the stadium may experience the AR application on their smartphones and mobile devices with a 4G/5G connection.
  • the disclosed methods can enable different network operators to provide customized experiences to their subscribers.
  • the disclosed hybrid edge service may further enable faster rollout of services by operators, enterprises, and other entities.
  • the cloud service provider may provide overall management and may also provide security.
  • the cloud service provider may provide centralized and seamless management of applications and content that are hosted at the service provider and orchestrated to the edge sites. This can enable a seamless user experience for mobile users who may move from one venue to another (e g., connected vehicles and drones in motion).
  • the edge sites may also host Y2X applications as needed.
  • Operators may also host their applications in their data center or at the cloud provider and leverage the edge sites to locate services closer to consumption. Operators may also consume edge site services (e.g., AR/VR/MR, gaming, smart city - traffic management and smart lights, IoT, etc.) on a usage basis without having to deploy or invest in infrastructure. For example, for any given event at a venue, an operator can have the edge site enable the desired services for the duration of the event.
  • edge site services e.g., AR/VR/MR, gaming, smart city - traffic management and smart lights, IoT, etc.
  • an orchestrator function may be implemented that intelligently places applications, tasks, and data on various edge networks based on the service agreements with operators and users, as well as capacities, bandwidth, policies, and other inputs.
  • a local edge manager may execute at each edge network.
  • the local edge manager may communicate with the orchestrator to locally manage containers and other components running at the edge.
  • the local edge manager and orchestrator may collectively monitor and manage applications and data, as well as available resources such as uplink and downlink capacity and computing capacity.
  • the local edge manager may send information to the orchestrator that indicates application usage, workload demands, and availabilities at the edge.
  • the orchestrator may analyze the information and send instructions to the local edge manager as to what applications and data should be made available at the edge and if any changes are needed.
  • the local edge manager may locally manage applications and data at the edge to efficiently provide access to users.
  • the applications, data, and capacities at the edge network may continuously change, for example when new events are occurring at the edge or when new users are using the edge. Therefore the local available applications, data, and compute capacity may change at any time.
  • the local edge manager may provide the updated information to the orchestrator which may generate updated instructions for redistributing applications and data at the edge sites.
  • One aspect of the present disclosure is the centralized and end-to-end management of services provided to end users, while cooperating with operators and edge providers to enable efficient and seamless delivery of the services.
  • the orchestrator may determine application and data distribution for the end-to-end system based on a cost function.
  • a two-level hierarchy may be implemented, where an orchestrator (first hierarchy) makes decisions for distribution of applications and data to local edge providers (second hierarchy).
  • first hierarchy an orchestrator
  • second hierarchy local edge providers
  • present disclosure may also be implemented when additional levels of hierarchy are implemented.
  • the cost function may allow for consideration of the various policies and costs for providing applications and data at one or more edge sites.
  • the constraints for the cost function may include policies, service level agreements, customer inputs, and the like.
  • the cost function may implement techniques such as a 0-1 loss function or a quadratic loss function.
  • the inputs to the workload optimization may include the following inputs: Pipeline of modules for an edge application/service
  • the decisions may include:
  • Additional parameters for determining application distribution can include network statistics including round trip time (RTT), bandwidth, loss rates, jitter, etc.
  • RTT round trip time
  • bandwidth bandwidth
  • loss rates loss rates
  • jitter etc.
  • the local edge manager can be configured to determine which state information pertaining to the applications should be sent to the cloud to allow the cloud to manage the applications. Appropriate state information can be transmitted to the orchestrator.
  • the local edge manager may continue to determine local usage and capacity information and may send updates to the orchestrator. This data may be used to update cost function estimates for applications and data.
  • the orchestrator may send updated instructions to the local edge manager as needed. For example, degradation of connectivity at an edge site can cause changes to the application distribution.
  • MEC mobile edge computing
  • 4G, 5G, or other cellular network MEC is a type of edge computing that uses cellular networks and 5G and enables a data center to extend cloud services to local deployments using a distributed architecture that provide federated options for local and remote data and control management.
  • MEC architectures may be implemented at cellular base stations or other edge nodes and enable operators to host content closer to the edge of the network, delivering high-bandwidth, low-latency applications to end users.
  • the cloud provider’s footprint may be co-located at a carrier site (e.g., carrier data center), allowing for the edge infrastructure and applications to run closer to the end user via the 5G network.
  • carrier site e.g., carrier data center
  • Ultra-reliable low-latency communications are useful for extremely time-sensitive and mission- critical applications, such as remote factory automation and remote robotic surgery.
  • 5G networks may enable a much greater density of transmitting and receiving devices, especially when sending small amounts of data. This can enable large-scale monitoring, measuring, and sensing applications in which large numbers of devices directly communicate with each other without human intervention — machine-to-machine communications (e.g., Internet of Things (IoT)).
  • IoT Internet of Things
  • edge computing as discussed herein, in which the equivalents of current cloud computing capabilities are brought closer to wireless devices to enable more rapid processing
  • network slicing in which different customers, applications, or both can have their own virtual slices of a common physical network.
  • FIG. 1 illustrates one example where users of a data center in accordance with some embodiments.
  • FIG. 1 illustrates data center 100 and 110 that are configured to provide computing resources to users 140 via user computers 130.
  • the computing resources provided by data centers 100 and 110 may be cached or replicated at edge nodes 120.
  • the computing resources provided by the data centers 100 and 110 and edge nodes 120 may include various types of resources, such as computing resources, data storage resources, data communication resources, and the like. Each type of computing resource may be general-purpose or may be available in a number of specific configurations.
  • computing resources may be available as virtual machines.
  • the virtual machines may be configured to execute applications, including Web servers, application servers, media servers, database servers, and the like.
  • Data storage resources may include file storage devices, block storage devices, and the like.
  • Each type or configuration of computing resource may be available in different configurations, such as the number of processors, and size of memory and/or storage capacity.
  • the resources may in some embodiments be offered to clients in units referred to as instances, such as virtual machine instances or storage instances.
  • a virtual computing instance may be referred to as a virtual machine and may, for example, comprise one or more servers with a specified computational capacity (which may be specified by indicating the type and number of CPUs, the main memory size and so on) and a specified software stack (e.g., a particular version of an operating system, which may in turn run on top of a hypervisor).
  • a specified computational capacity which may be specified by indicating the type and number of CPUs, the main memory size and so on
  • a specified software stack e.g., a particular version of an operating system, which may in turn run on top of a hypervisor.
  • FIG. 2A illustrates an example computing environment in which the embodiments described herein may be implemented.
  • FIG. 2A illustrates a service provider 200 that is configured to provide computing resources to users at user site 240.
  • the user site 240 may have user computers that may access services provided by service provider 200 via a network 230.
  • the computing resources provided by the service provider 200 may include various types of resources, such as computing resources, data storage resources, data communication resources, and the like.
  • computing resources may be available as virtual machines.
  • the virtual machines may be configured to execute applications, including Web servers, application servers, media servers, database servers, and the like.
  • Data storage resources may include file storage devices, block storage devices, and the like.
  • Networking resources may include virtual networking, software load balancer, and the like.
  • Service provider 200 may have various computing resources including servers, routers, and other devices that may provide remotely accessible computing and network resources using, for example, virtual machines. Other resources that may be provided include data storage resources. Service provider 200 may also execute functions that manage and control allocation of network resources, such as a network manager 220.
  • Network 230 may, for example, be a publicly accessible network of linked networks and may be operated by various entities, such as the Internet. In other embodiments, network 230 may be a private network, such as a dedicated network that is wholly or partially inaccessible to the public. Network 230 may provide access to computers and other devices at the user site 240.
  • FIG. 2B illustrates an example computing environment in which the embodiments described herein may be implemented.
  • FIG. 2B illustrates that an edge site 250 may be implemented to extend the physical reach of service provider 200 to provide localized computing resources to users at user site 240 using provided networks 260.
  • the computing resources provided by the edge site 250 may include some or all of the various types of resources provided by service provider 200.
  • the resources at edge site 250 may be under the control of service provider 200.
  • FIG. 3 illustrates an example computing environment in which the embodiments described herein may be implemented.
  • FIG. 3 illustrates a data center 300 that is configured to provide computing resources to users 300a, 300b, or 300c (which may be referred herein singularly as “a user 300” or in the plural as “the users 300") via user computers 303a, 303b, and 303c (which may be referred herein singularly as "a computer 303" or in the plural as “the computers 303") via a communications network 330.
  • the computing resources provided by the data center 300 may include various types of resources, such as computing resources, data storage resources, data communication resources, and the like. Each type of computing resource may be general-purpose or may be available in a number of specific configurations.
  • computing resources may be available as virtual machines.
  • the virtual machines may be configured to execute applications, including Web servers, application servers, media servers, database servers, and the like.
  • Data storage resources may include file storage devices, block storage devices, and the like.
  • Each type or configuration of computing resource may be available in different configurations, such as the number of processors, and size of memory and/or storage capacity.
  • the resources may in some embodiments be offered to clients in units referred to as instances, such as virtual machine instances or storage instances.
  • a virtual computing instance may be referred to as a virtual machine and may, for example, comprise one or more servers with a specified computational capacity (which may be specified by indicating the type and number of CPUs, the main memory size and so on) and a specified software stack (e.g., a particular version of an operating system, which may in turn run on top of a hypervisor).
  • a specified computational capacity which may be specified by indicating the type and number of CPUs, the main memory size and so on
  • a specified software stack e.g., a particular version of an operating system, which may in turn run on top of a hypervisor.
  • Data center 300 may correspond to service provider 100 in FIGs. 1 and 2, or edge site 150 of FIG. 2.
  • Data center 300 may include servers 336a, 336b, and 336c (which may be referred to herein singularly as “a server 336” or in the plural as “the servers 336") that may be standalone or installed in server racks, and provide computing resources available as virtual machines 338a and 338b (which may be referred to herein singularly as “a virtual machine 338” or in the plural as “the virtual machines 338”).
  • the virtual machines 338 may be configured to execute applications such as Web servers, application servers, media servers, database servers, and the like. Other resources that may be provided include data storage resources (not shown on FIG. 3) and may include file storage devices, block storage devices, and the like.
  • Servers 336 may also execute functions that manage and control allocation of resources in the data center, such as a controller 335. Controller 335 may be a fabric controller or another type of program configured to manage the allocation of virtual machines on servers 336.
  • communications network 330 may, for example, be a publicly accessible network of linked networks and may be operated by various entities, such as the Internet. In other embodiments, communications network 330 may be a private network, such as a corporate network that is wholly or partially inaccessible to the public.
  • Computers 303 may be computers utilized by users 300.
  • Computer 303a, 303b or 303c may be a server, a desktop or laptop personal computer, a tablet computer, a smartphone, a set-top box, or any other computing device capable of accessing data center 300.
  • User computer 303a or 303b may connect directly to the Internet (e g., via a cable modem).
  • User computer 303c may be internal to the data center 300 and may connect directly to the resources in the data center 300 via internal networks. Although only three user computers 303a, 303b, and 303c are depicted, it should be appreciated that there may be multiple user computers.
  • Computers 303 may also be utilized to configure aspects of the computing resources provided by data center 300.
  • data center 300 may provide a Web interface through which aspects of its operation may be configured through the use of a Web browser application program executing on user computer 303.
  • a stand-alone application program executing on user computer 303 may be used to access an application programming interface (API) exposed by data center 300 for performing the configuration operations.
  • API application programming interface
  • Servers 336 may be configured to provide the computing resources described above.
  • One or more of the servers 336 may be configured to execute a manager 330a or 330b (which may be referred herein singularly as “a manager 330" or in the plural as “the managers 330") configured to execute the virtual machines.
  • the managers 330 may be a virtual machine monitor (VMM), fabric controller, or another type of program configured to enable the execution of virtual machines 338 on servers 336, for example.
  • VMM virtual machine monitor
  • a network device 333 may be utilized to interconnect the servers 336a and 336b.
  • Network device 333 may comprise one or more switches, routers, or other network devices.
  • Network device 333 may also be connected to gateway 340, which is connected to communications network 330.
  • Network device 333 may facilitate communications within networks in data center 300, for example, by forwarding packets or other data communications as appropriate based on characteristics of such communications (e.g., header information including source and/or destination addresses, protocol identifiers, etc.) and/or the characteristics of the private network (e.g., routes based on network topology, etc.).
  • characteristics of such communications e.g., header information including source and/or destination addresses, protocol identifiers, etc.
  • the characteristics of the private network e.g., routes based on network topology, etc.
  • FIG. 3 has been greatly simplified and that many more networks and networking devices may be utilized to interconnect the various computing systems disclosed herein. These network topologies and devices should be apparent to those skilled in the art.
  • data center 300 described in FIG. 3 is merely illustrative and that other implementations might be utilized. Additionally, it should be appreciated that the functionality disclosed herein might be implemented in software, hardware or a combination of software and hardware. Other implementations should be apparent to those skilled in the art. It should also be appreciated that a server, gateway, or other computing device may comprise any combination of hardware or software that can interact and perform the described types of functionality, including without limitation desktop or other computers, database servers, network storage devices and other network devices, PDAs, tablets, smartphone, Internet appliances, television-based systems (e g., using set top boxes and/or personal/digital video recorders), and various other consumer products that include appropriate communication capabilities. In addition, the functionality provided by the illustrated modules may in some embodiments be combined in fewer modules or distributed in additional modules. Similarly, in some embodiments the functionality of some of the illustrated modules may not be provided and/or other additional functionality may be available.
  • FIG. 4 illustrates an example computing environment illustrating integration of computing resources that include an edge site 420 that is geographically proximate to a facility local to users 300, in accordance with the present disclosure.
  • one or more servers 436 may be installed at the edge site 420.
  • servers 436 instantiate and run virtual machines 438.
  • users 300 may specify configuration information for a virtual network to be provided for the user, with the configuration information optionally including a variety of types of information such as network addresses to be assigned to computing endpoints of the provided computer network, network topology information for the provided computer network, network access constraints for the provided computer network.
  • the network addresses may include, for example, one or more ranges of network addresses, which may correspond to a subset of virtual or private network addresses used for the user’ s private computer network.
  • the network topology information may indicate, for example, subsets of the computing endpoints to be grouped together, such as by specifying networking devices to be part of the provided computer network, or by otherwise indicating subnets of the provided computer network or other groupings of the provided computer network.
  • the network access constraint information may indicate, for example, for each of the provided computer network's computing endpoints, which other computing endpoints may intercommunicate with the computing node endpoint, or the types of communications allowed to/from the computing endpoints.
  • the architecture may include an on-site edge 520 that includes a local edge manager 522.
  • the on-site edge 520 may include resources such as virtual machine 525 running applications 526.
  • the architecture may include a network edge 530 that includes a local edge manager 532.
  • the network edge 530 may include resources such as virtual machine 535 running applications 536.
  • the architecture may include a cloud node 540 that orchestrator 542.
  • the cloud node 540 may include resources such as virtual machine 545 running applications 546.
  • Data source 510 may provide data for consumption by on-site edge 520.
  • Users 512 may consume applications 526 provided by on-site edge 520.
  • Further tasks of a processing pipeline may be processed at network edge 530, which may also provide applications 536.
  • Additional tasks of the processing pipeline may be processed at the cloud node 540, which may also provide applications 546.
  • the orchestrator 542 may cooperate with local edge manager 532 and local edge manager 522 to distribute applications, data, and tasks of a processing pipeline.
  • the architecture may include edge sites 550 that may provide services to venues such as stadiums, convention centers, and malls 555.
  • An operator data center may participate in the end-to-end delivery of applications which may be orchestrated by the cloud service provider.
  • a 4G / 5G router 551 may provide connectivity to an edge site.
  • An operator data center may participate in the end-to-end delivery of applications which may be orchestrated by the cloud service provider 556.
  • the architecture may include edge sites that may provide services to venues 557 such as supermarkets, first responders, and healthcare sites via localized connectivity such as Wifi.
  • venues 557 such as supermarkets, first responders, and healthcare sites via localized connectivity such as Wifi.
  • a 4G / 5G router 551 may provide connectivity to an edge site.
  • An operator data center may participate in the end-to-end delivery of applications which may be orchestrated by the cloud service provider 556.
  • requirements may include: five 9’s availability, ⁇ 5ms latency ⁇ 100m seconds latency for 5G Connectivity to MNO n/w Connectivity to venues & enterprises
  • platform requirements may include: Intrinsic security five 9’s availability, ⁇ 5ms latency
  • the architecture may include multiple edge sites 550 that may provide multi-tenant services to enterprises 560, 561 that may provide internal connectivity to on-site users via access points 562. Multiple edge sites may be interconnected with redundant paths to mitigate link and other failures.
  • FIG. 5E illustrated is one example architecture for providing hybrid edge services to multiple health care facilities in accordance with the present disclosure.
  • the architecture may include local connectivity using 5G infrastructure, which may provide connectivity to the edge site 550 and the cloud provider 556. In this way, healthcare providers 580 may share medical data securely across sites, have faster access to patient data, and leverage the edge site and cloud provider for storage, processing, and AI/ML based insights.
  • the architecture may include an edge node 660 that may include one or more resources 660.
  • the edge node 660 may include local edge manager agent 690, which may be a service running on the edge node 660 and configured to monitor local applications, data, resources, and capacities.
  • the edge manager agent 690 may be configured to track workloads that are being processed at the edge node 660, applications that are being accessed at the edge note 660, and may monitor various metrics including availability, read and write latency, etc.
  • the current status of all applications and resources may be sent to orchestrator 620 for analysis and performing actions as needed.
  • Edge manager agent 690 may be executed as a service running on the edge node 660.
  • Edge manager agent 690 may be configured to receive requests for data and operations from edge manager 630 at the data center 610.
  • the edge manager agent 690 may perform the requested operations at the edge node 660.
  • a local orchestrator 670 may distribute applications, data, and tasks among resources at the edge node 660.
  • the orchestrator 620 may be located in the data center / control plane 610.
  • the orchestrator 620 may be configured to receive information pertaining to applications and resources at an edge site. The information may be used to determine whether a workload should be handled by the edge node 660. The information may be used to determine whether a workload should be migrated to the edge node 660. The migration may be complementary to the initial decision on distribution to place the workloads.
  • Allocation manager 640 may be configured to determine a suitable edge site for providing applications or processing a given workload (e.g., virtual machines, containers, etc.). The allocation manager 640 may further be configured to maintain a list of all nodes at an edge site, their capabilities, and what applications are being accessed and what workloads are currently running on each server node. The capability list may be modified when a new information is received from edge manager agent 660.
  • FIG. 7 illustrated is an example operational procedure for providing a hybrid edge service in accordance with the present disclosure.
  • Such an operational procedure can be provided by one or more components illustrated in FIGs. 1 through 6.
  • the operational procedure may be implemented in a computing environment comprising a computing service provider and an edge computing network.
  • the edge computing network may comprise computing and storage devices configured to extend computing resources of the computing service provider to remote users of the computing service provider.
  • routine 700 is described as running on a system, it can be appreciated that the routine 700 and other operations described herein can be executed on an individual computing device or several devices.
  • operation 701 illustrates sending, by the computing service provider to the edge computing network, instructions for which applications and data are to be provided to users of the computing service provider at the edge computing network.
  • the edge computing network is selected based at least in part on the applications and data that are to be provided.
  • Operation 701 may be followed by operation 703.
  • Operation 703 illustrates allowing the instructed applications and data to be available to connected users by the edge computing network.
  • Operation 703 may be followed by operation 705.
  • Operation 705 illustrates sending, by the edge computing network to the computing service provider, updated capacity and usage data while providing the instructed applications at the edge computing network.
  • Operation 705 may be followed by operation 707.
  • Operation 707 illustrates applying, by the computing service provider, policies to the capacity and usage data to determine, by the computing service provider, a distribution of the applications and data.
  • the applications and data can be provided at the computing service provider or the edge computing network.
  • Operation 707 may be followed by operation 709.
  • Operation 709 illustrates adjusting the distribution of the applications and data with respect to one or more criteria.
  • Operation 709 may be followed by operation 711.
  • Operation 711 illustrates sending, by the computing service provider to the edge computing network, updated instructions for the applications and data when the distribution changes.
  • a component may also encompass other ways of leveraging a device to perform a function, such as, for example, a) a case in which at least some tasks are implemented in hard ASIC logic or the like; b) a case in which at least some tasks are implemented in soft (configurable) FPGA logic or the like; c) a case in which at least some tasks run as software on FPGA software processor overlays or the like; d) a case in which at least some tasks run as software on hard ASIC processors or the like, etc., or any combination thereof.
  • a component may represent a homogeneous collection of hardware acceleration devices, such as, for example, FPGA devices.
  • a component may represent a heterogeneous collection of different types of hardware acceleration devices including different types of FPGA devices having different respective processing capabilities and architectures, a mixture of FPGA devices and other types hardware acceleration devices, etc.
  • FIG. 8 illustrates a general-purpose computing device 800.
  • computing device 800 includes one or more processors 810a, 810b, and/or 81 On (which may be referred herein singularly as “a processor 810” or in the plural as “the processors 810") coupled to a system memory 88 via an input/output (I/O) interface 830.
  • processors 810a, 810b, and/or 81 On (which may be referred herein singularly as “a processor 810” or in the plural as “the processors 810") coupled to a system memory 88 via an input/output (I/O) interface 830.
  • I/O input/output
  • Computing device 800 further includes a network interface 840 coupled to I/O interface 830.
  • computing device 800 may be a uniprocessor system including one processor 810 or a multiprocessor system including several processors 810 (e.g., two, four, eight, or another suitable number).
  • Processors 810 may be any suitable processors capable of executing instructions.
  • processors 810 may be general-purpose or embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x88, PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA.
  • ISAs instruction set architectures
  • each of processors 810 may commonly, but not necessarily, implement the same ISA.
  • System memory 88 may be configured to store instructions and data accessible by processor(s) 810.
  • system memory 88 may be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory.
  • SRAM static random access memory
  • SDRAM synchronous dynamic RAM
  • program instructions and data implementing one or more desired functions are shown stored within system memory 820 as code 825 and data 828.
  • I/O interface 830 may be configured to coordinate I/O traffic between the processor 810, system memory 88, and any peripheral devices in the device, including network interface 840 or other peripheral interfaces.
  • I/O interface 830 may perform any necessary protocol, timing, or other data transformations to convert data signals from one component (e g., system memory 820) into a format suitable for use by another component (e.g., processor 810).
  • I/O interface 830 may include supportfor devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example.
  • PCI Peripheral Component Interconnect
  • USB Universal Serial Bus
  • I/O interface 830 may be split into two or more separate components. Also, in some embodiments some or all of the functionality of I/O interface 830, such as an interface to system memory 820, may be incorporated directly into processor 810.
  • Network interface 840 may be configured to allow data to be exchanged between computing device 800 and other device or devices 880 attached to a network or network(s) 880, such as other computer systems or devices as illustrated in FIGS. 1 through 5, for example. In various embodiments, network interface 840 may support communication via any suitable wired or wireless general data networks, such as types of Ethernet networks, for example.
  • network interface 840 may support communication via telecommunications/telephony networks such as analog voice networks or digital fiber communications networks, via storage area networks such as Fibre Channel SANs or via any other suitable type of network and/or protocol.
  • system memory 820 may be one embodiment of a computer-accessible medium configured to store program instructions and data as described above for FIGS. 1-7 for implementing embodiments of the corresponding methods and apparatus. However, in other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media.
  • a computer-accessible medium may include non-transitory storage media or memory media, such as magnetic or optical media, e.g., disk or DVD/CD coupled to computing device 800 via EO interface 830.
  • a non-transitory computer-accessible storage medium may also include any volatile or non-volatile media, such as RAM (e.g. SDRAM, DDR SDRAM, RDRAM, SRAM, etc.), ROM, etc., that may be included in some embodiments of computing device 800 as system memory 820 or another type of memory.
  • a computer- accessible medium may include transmission media or signals such as electrical, electromagnetic or digital signals, conveyed via a communication medium such as a network and/or a wireless link, such as may be implemented via network interface 840.
  • a communication medium such as a network and/or a wireless link, such as may be implemented via network interface 840.
  • Portions or all of multiple computing devices, such as those illustrated in FIG. 8, may be used to implement the described functionality in various embodiments; for example, software components running on a variety of different devices and servers may collaborate to provide the functionality.
  • portions of the described functionality may be implemented using storage devices, network devices, or special-purpose computer systems, in addition to or instead of being implemented using general- purpose computer systems.
  • the term "computing device,” as used herein, refers to at least all these types of devices and is not limited to these types of devices.
  • Computer-readable media as discussed herein may refer to a mass storage device, such as a solid-state drive, a hard disk or CD-ROM drive. However, it should be appreciated by those skilled in the art that computer-readable media can be any available computer storage media that can be accessed by a computing device.
  • computer storage media may include volatile and non- volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data.
  • computer media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, digital versatile disks (“DVD”), HD-DVD, BLU-RAY, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing devices discussed herein.
  • DVD digital versatile disks
  • HD-DVD high definition digital versatile disks
  • BLU-RAY blue ray
  • computer storage medium does not include waves, signals, and/or other transitory and/or intangible communication media, per se.
  • Encoding the software modules presented herein also may transform the physical structure of the computer-readable media presented herein.
  • the specific transformation of physical structure may depend on various factors, in different implementations of this description. Examples of such factors may include, but are not limited to, the technology used to implement the computer- readable media, whether the computer-readable media is characterized as primary or secondary storage, and the like.
  • the computer-readable media is implemented as semiconductor-based memory
  • the software disclosed herein may be encoded on the computer- readable media by transforming the physical state of the semiconductor memory.
  • the software may transform the state of transistors, capacitors, or other discrete circuit elements constituting the semiconductor memory.
  • the software also may transform the physical state of such components in order to store data thereupon.
  • the computer-readable media disclosed herein may be implemented using magnetic or optical technology.
  • the software presented herein may transform the physical state of magnetic or optical media, when the software is encoded therein. These transformations may include altering the magnetic characteristics of particular locations within given magnetic media. These transformations also may include altering the physical features or characteristics of particular locations within given optical media, to change the optical characteristics of those locations. Other transformations of physical media are possible without departing from the scope and spirit of the present description, with the foregoing examples provided only to facilitate this discussion.
  • any reference to “first,” “second,” etc. items and/or abstract concepts within the description is not intended to and should not be construed to necessarily correspond to any reference of “first,” “second,” etc. elements of the claims.
  • items and/or abstract concepts such as, for example, individual computing devices and/or operational states of the computing cluster may be distinguished by numerical designations without such designations corresponding to the claims or even other paragraphs of the Summary and/or Detailed Description.
  • any designation of a “first operational state” and “second operational state” of the computing cluster within a paragraph of this disclosure is used solely to distinguish two different operational states of the computing cluster within that specific paragraph - not any other paragraph and particularly not the claims.
  • Amethod for providing computing resources in a computing environment comprising a computing service provider and an edge computing network, the edge computing network comprising computing and storage devices configured to extend computing resources of the computing service provider to remote users of the computing service provider, the method comprising: sending, by the computing service provider to the edge computing network, instructions for which applications and data are to be provided to users of the computing service provider at the edge computing network; allowing the instructed applications and data to be available to connected users by the edge computing network; sending, by the edge computing network to the computing service provider, updated capacity and usage data while providing the instructed applications at the edge computing network; applying, by the computing service provider, policies and a cost function to the capacity and usage data to determine, by the computing service provider, a distribution of the applications and data, wherein: the applications and data can be provided at the computing service provider or the edge computing network; and the cost function is usable to optimize the distribution of the applications and data with respect to one or more criteria; and sending, by the computing service provider to the edge computing network, updated instructions for the applications and data when the distribution changes.
  • a method for providing computing resources in a computing environment comprising a computing service provider and an edge computing network, the edge computing network comprising computing and storage devices configured to extend computing resources of the computing service provider to remote users of the computing service provider, the edge computing network configured to provide tenant-based localized services to users of the computing service provider, the method comprising: sending, by the computing service provider to the edge computing network, instructions for which applications and data are to be provided to users of the computing service provider at the edge computing network, wherein the edge computing network is selected based at least in part on the applications and data that are to be provided; allowing the instructed applications and data to be available to connected users by the edge computing network; sending, by the edge computing network to the computing service provider, updated capacity and usage data while providing the instructed applications at the edge computing network; applying, by the computing service provider, policies to the capacity and usage data to determine, by the computing service provider, a distribution of the applications and data, wherein the applications and data can be provided at the computing service provider or the edge computing network; adjusting the distribution of the applications and data with
  • Clause 2 The method of clause 1, further comprising executing a local edge manager at the edge computing network, the local edge manager configured to monitor the capacity and usage data for computing and network resources at the edge computing network and send the capacity and usage data to the computing service provider.
  • Clause 3 The method of any of clauses 1-2, wherein the local edge manager is configured to monitor the capacity and usage data during execution of the applications.
  • Clause 4 The method of any of clauses 1-3, further comprising executing an orchestrator at the computing service provider that is configured to determine the applications and data distributions based on an optimization.
  • Clause 5 The method of any of clauses 1-4, further comprising using a cost function to determine an optimization of cost based on one or more constraints.
  • Clause 6 The method of any of clauses 1-5, wherein the constraints include a policy associated with a customer of the edge computing network.
  • Clause 7 The method of clauses 1-6, wherein the local edge manager is configured to distribute the applications within the computing resources at the edge computing network.
  • Clause 8 The method of any of clauses 1-7, wherein the edge computing network is configured to communicatively couple to the computing service provider over a 5G network.
  • Clause 9 A system for providing computing resources in a computing environment comprising a computing service provider and an edge computing network, the edge computing network comprising computing and storage devices configured to extend computing resources of the computing service provider to remote users of the computing service provider, the system comprising: one or more processors; and a memory in communication with the one or more processors, the memory having computer- readable instructions stored thereupon that, when executed by the one or more processors, cause the system to perform operations comprising: sending instructions for which applications and data are to be provided to users of the computing service provider at the edge computing network; allowing the instructed applications and data to be available to connected users by the edge computing network; receiving updated capacity and usage data while providing the instructed applications at the edge computing network; applying policies to the capacity and usage data to determine, by the computing service provider, a distribution of the applications and data, wherein the applications and data can be provided at the computing service
  • Clause 10 The system of clause 9, further comprising computer-readable instructions stored thereupon that, when executed by the one or more processors, cause the system to perform operations comprising: executing an orchestrator configured to determine the applications and data distributions based on the optimization.
  • Clause 11 The system of any of clauses 9 and 10, further comprising computer-readable instructions stored thereupon that, when executed by the one or more processors, cause the system to perform operations comprising: using a cost function to determine an optimization of cost based on one or more constraints.
  • Clause 12 The system of any clauses 9-11, wherein the constraints include a policy associated with a customer of the edge computing network.
  • Clause 13 The system of any clauses 9-12, wherein the edge computing network is configured to communicatively couple to the computing service provider over a 5G network.
  • a computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more processors of a computing device, cause the computing device to: sending instructions for which applications and data are to be provided to users of a computing service provider at an edge computing network, the edge computing network comprising computing and storage devices configured to extend computing resources of a computing service provider to remote users of the computing service provider; allowing the instructed applications and data to be available to connected users by the edge computing network; sending, by the edge computing network to the computing service provider, updated capacity and usage data while providing the instructed applications at the edge computing network; applying, by the computing service provider, policies to the capacity and usage data to determine, by the computing service provider, a distribution of the applications and data, wherein the applications and data can be provided at the computing service provider or the edge computing network; optimizing the distribution of the applications and data with respect to one or more criteria; and sending, by the computing service provider to the edge computing network, updated instructions for the applications and data when the distribution changes.
  • Clause 15 The computer-readable storage medium of clause 14, further comprising computer- executable instructions stored which, when executed by one or more processors of a computing device, cause the computing device to execute a local edge manager at the edge computing network, the local edge manager configured to monitor the capacity and usage data for computing and network resources at the edge computing network and send the capacity and usage data to the computing service provider.
  • Clause 16 The computer-readable storage medium of any of clauses 14 and 15, wherein the local edge manager is configured to monitor the capacity and usage data during execution of the applications.
  • Clause 17 The computer-readable storage medium of any of the clauses 14-16, wherein the local edge manager is configured to distribute the applications within the computing resources at the edge computing network.
  • Clause 18 The computer-readable storage medium of any of the clauses 14-17, further comprising using a cost function to determine an optimization of cost based on one or more constraints.
  • Clause 19 The computer-readable storage medium of any of the clauses 14-18, wherein the constraints include a policy associated with a customer of the edge computing network.
  • Clause 20 The computer-readable storage medium of any of the clauses 14-19, wherein the edge computing network is configured to communicatively couple to the computing service provider over a 5G network.

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Abstract

La présente invention concerne un environnement informatique qui comprend un fournisseur de services informatiques et un réseau informatique périphérique qui comprend des dispositifs de calcul et de stockage qui prolongent les ressources informatiques du fournisseur de services informatiques jusqu'à des utilisateurs distants du fournisseur de services informatiques. Le réseau informatique périphérique est configuré pour fournir des services localisés basés sur des locataires aux utilisateurs du fournisseur de services informatiques.
PCT/US2022/030982 2021-06-25 2022-05-26 Services périphériques hybrides WO2022271398A1 (fr)

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US20210168203A1 (en) * 2019-11-29 2021-06-03 Amazon Technologies, Inc. Dynamic resource movement in heterogeneous computing environments including cloud edge locations

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210168203A1 (en) * 2019-11-29 2021-06-03 Amazon Technologies, Inc. Dynamic resource movement in heterogeneous computing environments including cloud edge locations

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