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Coordinating processor and main memory for efficientserver power control

Published: 31 May 2011 Publication History

Abstract

With the number of high-density servers in data centers rapidly increasing, power control with performance optimization has become a key challenge to gain a high return on investment, by safely accommodating the maximized number of servers allowed by the limited power supply and cooling facilities in a data center. Various power control solutions have been recently proposed for high-density servers and different components in a server to avoid system failures due to power overload or overheating. Existing solutions, unfortunately, either rely only on the processor for server power control, with the assumption that it is the only major power consumer, or limit power only for a single component, such as main memory. As a result, the synergy between the processor and main memory is impaired by uncoordinated power adaptations, resulting in degraded overall system performance. In this paper, we propose a novel power control solution that can precisely limit the peak power consumption of a server below a desired budget. Our solution adapts the power states of both the processor and memory in a coordinated manner, based on their power demands, to achieve optimized system performance. Our solution also features a control algorithm that is designed rigorously based on advanced feedback control theory for guaranteed control accuracy and system stability. Compared with two state-of-the-art server power control solutions, experimental results show that our solution, on average, achieves up to 23% better performance than one baseline for CPU-intensive benchmarks and doubles the performance of the other baseline when the power budget is tight.

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Cited By

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  • (2021)The Case for Cross-Component Power Coordination on Power Bounded SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.306823532:10(2464-2476)Online publication date: 1-Oct-2021
  • (2020)All-Digital Control-Theoretic Scheme to Optimize Energy Budget and Allocation in Multi-CoresIEEE Transactions on Computers10.1109/TC.2019.296385969:5(706-721)Online publication date: 1-May-2020
  • (2020)Runtime power allocation approach for GAMESS hybrid CPU‐GPU implementationConcurrency and Computation: Practice and Experience10.1002/cpe.591732:24Online publication date: 21-Aug-2020
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cover image ACM Conferences
ICS '11: Proceedings of the international conference on Supercomputing
May 2011
398 pages
ISBN:9781450301022
DOI:10.1145/1995896
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 31 May 2011

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Author Tags

  1. data center
  2. memory
  3. power capping
  4. power control
  5. server

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ICS '11
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ICS '11: International Conference on Supercomputing
May 31 - June 4, 2011
Arizona, Tucson, USA

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Overall Acceptance Rate 629 of 2,180 submissions, 29%

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Cited By

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  • (2021)The Case for Cross-Component Power Coordination on Power Bounded SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.306823532:10(2464-2476)Online publication date: 1-Oct-2021
  • (2020)All-Digital Control-Theoretic Scheme to Optimize Energy Budget and Allocation in Multi-CoresIEEE Transactions on Computers10.1109/TC.2019.296385969:5(706-721)Online publication date: 1-May-2020
  • (2020)Runtime power allocation approach for GAMESS hybrid CPU‐GPU implementationConcurrency and Computation: Practice and Experience10.1002/cpe.591732:24Online publication date: 21-Aug-2020
  • (2019)Minimizing Data Center Uninterruptable Power Supply Overload by Server Power CappingIEEE Communications Letters10.1109/LCOMM.2019.291971723:8(1342-1346)Online publication date: Aug-2019
  • (2018)Coordinated Optimization of the Performance of Processors and Memory in a Heterogeneous System under Energy Constraints2018 15th International Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN)10.1109/I-SPAN.2018.00025(100-106)Online publication date: Oct-2018
  • (2018)Improve Energy Efficiency by Processor Overclocking and Memory Frequency Scaling2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2018.00159(960-967)Online publication date: Jun-2018
  • (2018)Race to idle or notJournal of Combinatorial Optimization10.1007/s10878-017-0229-735:3(860-894)Online publication date: 1-Apr-2018
  • (2017)Fast Power and Energy Management for Future Many-Core SystemsACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/30865042:3(1-31)Online publication date: 5-Sep-2017
  • (2017)A Survey on Modeling Energy Consumption of Cloud Applications: Deconstruction, State of the Art, and Trade-Off DebatesIEEE Transactions on Sustainable Computing10.1109/TSUSC.2017.27228222:3(255-274)Online publication date: 1-Jul-2017
  • (2017)DEMM: A Dynamic Energy-Saving Mechanism for Multicore Memories2017 IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)10.1109/MASCOTS.2017.16(210-220)Online publication date: Sep-2017
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