WO2012082349A2 - Planification de charge de travail sur la base d'une politique d'énergie de plateforme - Google Patents

Planification de charge de travail sur la base d'une politique d'énergie de plateforme Download PDF

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Publication number
WO2012082349A2
WO2012082349A2 PCT/US2011/062305 US2011062305W WO2012082349A2 WO 2012082349 A2 WO2012082349 A2 WO 2012082349A2 US 2011062305 W US2011062305 W US 2011062305W WO 2012082349 A2 WO2012082349 A2 WO 2012082349A2
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WO
WIPO (PCT)
Prior art keywords
workload
platform
memory
server platform
server
Prior art date
Application number
PCT/US2011/062305
Other languages
English (en)
Other versions
WO2012082349A3 (fr
Inventor
Ravi Giri
Original Assignee
Intel Corporation
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 Corporation filed Critical Intel Corporation
Publication of WO2012082349A2 publication Critical patent/WO2012082349A2/fr
Publication of WO2012082349A3 publication Critical patent/WO2012082349A3/fr

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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
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
    • G06F1/3234—Power saving characterised by the action undertaken
    • G06F1/329—Power saving characterised by the action undertaken by task scheduling
    • 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]
    • G06F9/5094—Allocation of resources, e.g. of the central processing unit [CPU] where the allocation takes into account power or heat criteria
    • 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
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Definitions

  • the invention relates to power management and more particularly to workload scheduling of an electronic system based on a platform energy policy.
  • Fig. 1 is a block diagram of a server platform in accordance with some embodiments of the invention.
  • Fig. 2 is a block diagram of a data center system in accordance with some embodiments of the invention.
  • Fig. 3 is a flow diagram in accordance with some embodiments of the invention.
  • Fig. 4 is a block diagram of another data center system in accordance with some embodiments of the invention.
  • Fig. 5 is another flow diagram in accordance with some embodiments of the invention.
  • a server platform 10 may include one or more processing cores 12 and memory 14 in electrical communication with the one or more processing cores 12.
  • the memory 14 may store code which when executed causes the server platform 10 to store a platform power correlation factor, receive workload requirements for a workload from a workload scheduler, determine a current and expected energy consumption based on the workload requirements and the platform performance correlation factor, communicate the current and expected energy consumption for the workload to the workload scheduler, and if the workload is dispatched to the server platform from the workload scheduler, store the workload requirements in the memory and modify characteristics of the server platform to execute the workload.
  • the platform power correlation factor may correspond to an expected power draw at various levels of resource utilization.
  • the workload requirements may correspond to one or more of a number of processing cores, an amount of memory needed, and an expected run time.
  • the workload scheduler may be configured to determine if the workload can be sent to the server platform 10 based on the current and expected energy consumption for the workload communicated to the workload scheduler from the server platform 10 and pre-configured power and temperature thresholds for the server platform 10 and also one or more of rack location, row location, and other data center specific information.
  • the modified server platform 10 In some embodiments of the server platform 10, the modified
  • characteristics of the workload may include one or more of a processing core to switch off, a portion of memory to switch off, a power profile, and a performance profile.
  • the memory 14 to store the platform power correlation factor and the workload requirements may include a non-volatile memory, such as flash memory.
  • a data center system 20 may include one or more server platforms 22, a workload scheduler 24, and a set of stored data 26 shared between the one or more server platforms 22 and the workload scheduler 24.
  • the server platforms 22 may include one or more processing cores and memory in electrical communication with the one or more processing cores.
  • the memory may store code which when executed causes the server platform 22 to store a platform power correlation factor, receive workload requirements for a workload from the workload scheduler, determine a current and expected energy consumption based on the workload requirements and the platform performance correlation factor, communicate the current and expected energy consumption for the workload to the workload scheduler, and if the workload is dispatched to the server platform from the workload scheduler, store the workload requirements in the memory and modify characteristics of the server platform to execute the workload.
  • the workload scheduler 24 may determine if the workload can be sent to the server platform 22 based on the current and expected energy consumption for the workload communicated to the workload scheduler 24 from the server platform 22 and pre-configured power and temperature thresholds for the server platform 22 and also one or more of rack location, row location, and other data center specific information.
  • the set of stored data may include at least one of a platform compute policy and a platform energy policy.
  • the platform power correlation factor may correspond to an expected power draw at various levels of resource utilization.
  • the workload requirements may correspond to one or more of a number of processing cores, an amount of memory needed, and an expected run time.
  • the modified characteristics of the workload may include one or more of a processing core to switch off, a portion of memory to switch off, a power profile, and a performance profile.
  • the memory to store the platform power correlation factor and the workload requirements may include a non-volatile memory, such as a flash memory.
  • a method of operating a server platform in accordance with some embodiments of the invention may include storing a platform power correlation factor in a memory (e.g. at block 30), receiving workload requirements for a workload from a workload scheduler (e.g. at block 31 ), determining a current and expected energy consumption based on the workload requirements and the platform performance correlation factor (e.g. at block 32), communicating the current and expected energy consumption for the workload to the workload scheduler (e.g. at block 33), and if the workload is dispatched to the server platform from the workload scheduler, storing the workload requirements in the memory and modifying characteristics of the server platform to execute the workload (e.g. at block 34).
  • the platform power correlation factor may correspond to an expected power draw at various levels of resource utilization (e.g. at block 35).
  • the workload requirements may correspond to one or more of a number of processing cores, an amount of memory needed, and an expected run time (e.g. at block 36).
  • the workload scheduler may determine if the workload can be sent to the server platform based on the current and expected energy consumption for the workload communicated to the workload scheduler from the server platform and pre-configured power and temperature thresholds for the server platform and also one or more of rack location, row location, and other data center specific information (e.g. at block 37).
  • the modified characteristics of the workload may include one or more of a processing core to switch off, a portion of memory to switch off, a power profile, and a performance profile (e.g. at block 38).
  • the memory for storing the platform power correlation factor and the workload requirements may include a non-volatile memory (e.g. at block 39), such as a flash memory.
  • some embodiments of the invention may provide a technique for data center energy efficiency with power and thermal aware workload scheduling.
  • some embodiments of the invention may involve balancing IT load, energy efficiency, location awareness, and / or a platform power correlation.
  • some embodiments of the invention may be useful in a data center utilizing server platforms that have service processors with temperature and power sensors (e.g. IPMI 2.0 and above including, for example, Intel's Node Manager).
  • the cost of energy for a large scale data center may be the single largest operational expense for the data center.
  • Such data center environments may see relatively high server resource utilization (e.g. CPU, memory, I/O) and as a result higher energy consumption for running the servers as well as cooling them.
  • server resource utilization e.g. CPU, memory, I/O
  • some embodiments of the invention may provide a platform capability that helps lower energy cost with little or no throughput impact.
  • some embodiments of the invention may provide platform level hardware and / or software capabilities that workload schedulers can use to intelligently schedule and dispatch jobs to achieve improved or optimal compute utilization as well as energy consumption.
  • some embodiments of the invention may provide an energy policy engine for HPC / cloud type of computing needs.
  • the available compute capacity expressed, for example, in normalized units such as SPECint or SPECfp or an application specific performance indicator specific to a particular data center (e.g. an HPC shop);
  • the workload requirements may include information about a preferred execution environment for the workload such as architecture, number of cores, memory, and / or disk space, among other workload requirement information such as priority or criticality;
  • Location information related to the server platforms For example, row / rack / data center location information.
  • some embodiments of the invention may include a platform power correlation factor stored in memory.
  • the platform power correlation factor may be embedded in the firmware.
  • the platform power correlation factor may allow the data center system to determine an expected power draw at various level of resource utilization as well as to determine an expected power draw if some of the resources were switched off.
  • the data center system may also have the ability to record the location information for the server platforms and / or components of the server platforms in the data center.
  • Some server platforms may provide some capability (e.g. Intel's Node ManagerTM) to manage server power consumption (e.g. read the server power usage and set basic policies for controlling power usage).
  • some embodiments of the present invention may provide a method for workload schedulers to interact directly with the platform and leverage existing platform abilities such as node manager, etc to efficiently schedule workloads while optimizing energy consumption.
  • a data center system 40 includes one or more server platforms 42 in communication with a workload scheduler 44 and a data share 46.
  • the server platform 42 may include a combination of software (e.g. drivers, operating systems, and applications) and hardware / firmware (e.g. a
  • the data share 46 may include information related to a configuration management database (CMDB), compute policies, and energy policies).
  • CMDB configuration management database
  • some embodiments of the invention may provide a direct interface for workload schedulers to interact with platform capabilities. For example, the ability to map workload efficiency to power consumption of each platform and / or the ability to record location in data center and assist in self- manageability.
  • the platform interface to the workload schedulers may provide a mechanism to store the relevant bits of data in platform level flash storage.
  • the workload scheduler may send the workload requirements to a server platform (e.g. number of cores, amount of memory needed, and an expected run time; e.g. at block 50).
  • the server platform may respond with a current and expected energy consumption based on the power to performance correlation factor (e.g. based on the workload requirements and the platform power correlation factor; e.g. at blocks 51 and 52).
  • the server platform may also provide additional information from the node manager and / or service processor (e.g. location information; e.g. at block 53).
  • the workload scheduler may then determine if the workload can be sent to that server platform based on how the response matches against pre-configured power and temperature thresholds for that rack / row / data center (e.g. at blocks 54 and 55). If the workload can be run on the server platform, then the workload scheduler may dispatch the job and store the workload requirements in the data store (e.g. at block 56). Based on the workload requirements, the server platform may modify some operating characteristics to execute the workload (e.g. switching off some cores, etc.; e.g. at block 57 ) and perform those actions (e.g. utilizing the internal interface to the node manager / service processor; e.g. at block 58).
  • the server platform can automatically switch remaining cores to a low power state and ensure no power capping is done to achieve highest throughput.
  • all cores can be switched on to high power state but some of the memory DIMM's can be turned off.
  • the server platform may shut down half the cores and update the performance capability data so that the workload scheduler is aware of degraded capability.
  • some embodiments of the invention may also help in system management. For example, being able to query the server platform itself for performance capability information and location information may enable highly accurate and reliable manageability.
  • components of an energy efficient data center with power and thermal aware workload scheduling may include a flash memory based data store, extensions to a manageability engine interface to allow host OS and applications such as workload schedulers to transact with the server platforms, and an interface to the server platform firmware / BIOS , service process and other related platform capabilities such as a node manager.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Power Sources (AREA)

Abstract

Selon certains modes de réalisation de l'invention, un système de centre de données peut comprendre une ou plusieurs plateformes de serveur, un planificateur de charge de travail et un ensemble de données stockées partagées entre les plateformes de serveur et le planificateur de charge de travail. Les plateformes de serveur peuvent comprendre des cœurs de processeur et de la mémoire en communication électrique avec les cœurs de processeur. La mémoire peut stocker un code qui, lorsqu'il est exécuté, amène la plateforme de serveur à stocker un facteur de corrélation de puissance de plateforme, recevoir des exigences de charge de travail pour une charge de travail en provenance d'un planificateur de charge de travail, déterminer des consommations d'énergie courante et attendue sur la base des exigences de charge de travail et du facteur de corrélation de performance de plateforme, communiquer les consommations d'énergie courante et attendue pour la charge de travail au planificateur de charge de travail, et si la charge de travail est distribuée à la plateforme de serveur par le planificateur de charge de travail, stocker les exigences de charge de travail dans la mémoire et modifier des caractéristiques de la plateforme de serveur afin d'exécuter la charge de travail. Le planificateur de charge de travail peut déterminer si la charge de travail peut être envoyée ou non à la plateforme de serveur sur la base des consommations d'énergie courante et attendue pour la charge de travail et de seuils de puissance et de température préconfigurés pour la plateforme de serveur, et également d'informations d'emplacement de bâti, d'emplacement de rangée et/ou d'autres informations spécifiques du centre de données. L'ensemble de données stockées peut comprendre une politique de calcul de plateforme et/ou une politique d'énergie de plateforme. D'autres modes de réalisation sont décrits et revendiqués.
PCT/US2011/062305 2010-12-16 2011-11-29 Planification de charge de travail sur la base d'une politique d'énergie de plateforme WO2012082349A2 (fr)

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IN3003DE2010 2010-12-16
IN3003/DEL/2010 2010-12-16

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WO2012082349A3 WO2012082349A3 (fr) 2012-08-16

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104049716A (zh) * 2014-06-03 2014-09-17 中国科学院计算技术研究所 一种结合温度感知的计算机节能方法及系统
US9558088B2 (en) 2012-12-13 2017-01-31 International Business Machines Corporation Using environmental signatures for test scheduling
EP3267312A1 (fr) * 2016-07-07 2018-01-10 Honeywell International Inc. Organe de commande multivariable de commande coordonnée de dispositifs informatiques et infrastructure de bâtiment dans des centres de données ou d'autres emplacements

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6167524A (en) * 1998-04-06 2000-12-26 International Business Machines Corporation Apparatus and method for efficient battery utilization in portable personal computers
US20040249515A1 (en) * 2001-06-18 2004-12-09 Johnson Daniel T. Enterprise energy management system
US20090007128A1 (en) * 2007-06-28 2009-01-01 International Business Machines Corporation method and system for orchestrating system resources with energy consumption monitoring
US20090187782A1 (en) * 2008-01-23 2009-07-23 Palo Alto Research Center Incorporated Integrated energy savings and business operations in data centers

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6167524A (en) * 1998-04-06 2000-12-26 International Business Machines Corporation Apparatus and method for efficient battery utilization in portable personal computers
US20040249515A1 (en) * 2001-06-18 2004-12-09 Johnson Daniel T. Enterprise energy management system
US20090007128A1 (en) * 2007-06-28 2009-01-01 International Business Machines Corporation method and system for orchestrating system resources with energy consumption monitoring
US20090187782A1 (en) * 2008-01-23 2009-07-23 Palo Alto Research Center Incorporated Integrated energy savings and business operations in data centers

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9558088B2 (en) 2012-12-13 2017-01-31 International Business Machines Corporation Using environmental signatures for test scheduling
CN104049716A (zh) * 2014-06-03 2014-09-17 中国科学院计算技术研究所 一种结合温度感知的计算机节能方法及系统
EP3267312A1 (fr) * 2016-07-07 2018-01-10 Honeywell International Inc. Organe de commande multivariable de commande coordonnée de dispositifs informatiques et infrastructure de bâtiment dans des centres de données ou d'autres emplacements

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