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Power management of online data-intensive services

Published: 04 June 2011 Publication History

Abstract

Much of the success of the Internet services model can be attributed to the popularity of a class of workloads that we call Online Data-Intensive (OLDI) services. These workloads perform significant computing over massive data sets per user request but, unlike their offline counterparts (such as MapReduce computations), they require responsiveness in the sub-second time scale at high request rates. Large search products, online advertising, and machine translation are examples of workloads in this class. Although the load in OLDI services can vary widely during the day, their energy consumption sees little variance due to the lack of energy proportionality of the underlying machinery. The scale and latency sensitivity of OLDI workloads also make them a challenging target for power management techniques.
We investigate what, if anything, can be done to make OLDI systems more energy-proportional. Specifically, we evaluate the applicability of active and idle low-power modes to reduce the power consumed by the primary server components (processor, memory, and disk), while maintaining tight response time constraints, particularly on 95th-percentile latency. Using Web search as a representative example of this workload class, we first characterize a production Web search workload at cluster-wide scale. We provide a fine-grain characterization and expose the opportunity for power savings using low-power modes of each primary server component. Second, we develop and validate a performance model to evaluate the impact of processor- and memory-based low-power modes on the search latency distribution and consider the benefit of current and foreseeable low-power modes. Our results highlight the challenges of power management for this class of workloads. In contrast to other server workloads, for which idle low-power modes have shown great promise, for OLDI workloads we find that energy-proportionality with acceptable query latency can only be achieved using coordinated, full-system active low-power modes.

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References

[1]
"AMD Family 10h Server and Workstation Processor Power and Thermal Data Sheet Rev 3.15" 2010.
[2]
"Intel-Xeon Processor 5600 Series. Datasheet, Volume 1," 2010.
[3]
L. A. Barroso and U. Hölzle, The Datacenter as a Computer. Morgan Claypool, 2009.
[4]
L. A. Barroso, J. Dean, and U. Hölzle, "Web search for a planet: The google cluster architecture," IEEE Micro, vol. 23, no. 2, 2003.
[5]
L. A. Barroso and U. Hölzle, "The case for energy-proportional computing," Computer, vol. 40, no. 12, 2007.
[6]
D. Blaauw, S. Das, and Y. Lee, "Managing variations through adaptive design techniques," Tutorial at International Solid-State Circuits Conference, 2010.
[7]
E. V. Carrera, E. Pinheiro, and R. Bianchini, "Conserving disk energy in network servers," in Proceedings of the 17th annual international conference on Supercomputing, 2003.
[8]
J. S. Chase, D. C. Anderson, P. N. Thakar, A. M. Vahdat, and R. P. Doyle, "Managing energy and server resources in hosting centers," in Symposium on Operating System Principles, 2001.
[9]
V. Delaluz, M. Kandemir, N. Vijaykrishnan, A. Sivasubramaniam, and M. J. Irwin, "Hardware and software techniques for controlling DRAM power modes," IEEE Trans. Comput., vol. 50, no. 11, 2001.
[10]
Q. Deng, D. Meisner, T. F. Wenisch, and R. Bianchini, "MemScale: Active Low-Power Modes for Main Memory," in Architectural Support for Programming Languages and Operating Systems, 2011.
[11]
B. Diniz, D. Guedes, W. Meira, Jr., and R. Bianchini, "Limiting the power consumption of main memory," in phInternational Symposium on Computer Architecture, 2007.
[12]
M. Elnozahy, M. Kistler, and R. Rajamony, "Energy conservation policies for web servers," in phProceedings of the 4th USENIX Symposium on Internet Technologies and Systems, 2003.
[13]
X. Fan, C. S. Ellis, and A. R. Lebeck, "The synergy between power-aware memory systems and processor voltage scaling," in Workshop on Power-Aware Computing Systems, 2003.
[14]
X. Fan, W.-D. Weber, and L. A. Barroso, "Power provisioning for a warehouse-sized computer," in International Symposium on Computer Architecture, 2007.
[15]
S. Gurumurthi, A. Sivasubramaniam, M. Kandemir, and H. Franke, "DRPM: dynamic speed control for power management in server class disks," in International Symposium on Computer Architecture, 2010.
[16]
T. Heath, B. Diniz, E. V. Carrera, W. Meira, Jr., and R. Bianchini, "Energy conservation in heterogeneous server clusters," in Principles and Practice of Parallel Programming, 2005.
[17]
J. Janzen, "Calculating memory system power for DDR SDRAM," Micron DesignLine, vol. 10, no. 2, 2001.
[18]
S. Kaxiras and M. Martonosi, Computer Architecture Techniques for Power-Efficiency. Morgan Claypool, 2009.
[19]
K. Lim, P. Ranganathan, J. Chang, C. Patel, T. Mudge, and S. Reinhardt, "Understanding and designing new server architectures for emerging warehouse-computing environments," in International Symposium on Computer Architecture, 2008.
[20]
D. Meisner, B. T. Gold, and T. F. Wenisch, "PowerNap: Eliminating server idle power," in Architectural Support for Programming Languages and Operating Systems, 2009.
[21]
D. Meisner and T. F. Wenisch, "Stochastic Queuing Simulation for Data Center Workloads," in Exascale Evaluation and Research Techniques Workshop, 2010.
[22]
E. Pinheiro and R. Bianchini, "Energy conservation techniques for disk array-based servers," in International Conference on Supercomputing, 2004.
[23]
K. Rajamani, C. Lefurgy, S. Ghiasi, J. Rubio, H. Hanson, and T. W. Keller, "Power management solutions for computer systems and datacenters," in International Symposium on Low-Power Electronics and Design, 2008.
[24]
E. Schurman and J. Brutlag, "The user and business impact of server delays, additional bytes, and HTTP chunking in web search," Velocity, 2009.
[25]
D. Snowdon, S. Ruocco, and G. Heiser, "Power Management and Dynamic Voltage Scaling: Myths and Facts," in Workshop on Power Aware Real-time Computing, 2005.
[26]
S. Srinivasan, L. Zhao, B. Ganesh, B. Jacob, M. Espig, and R. Iyer, "Cmp memory modeling: How much does accuracy matter?" in Workshop on Modeling, Benchmarking and Simulation, 2009.
[27]
N. Tolia, Z. Wang, M. Marwah, C. Bash, P. Ranganathan, and X. Zhu, "Delivering energy proportionality with non energy-proportional systems -- optimizing the ensemble," in HotPower, 2008.
[28]
VJ Reddi, Benjamin Lee, Trishul Chilimbi, and Kushagra Vaid, "Web Search Using Mobile Cores: Quantifying and Mitigating the Price of Efficiency," in International Symposium on Computer Architecture, 2010.
[29]
Willis Lang and Jignesh M. Patel and Srinath Shankar, "Wimpy Node Clusters: What About Non-Wimpy Workloads?" in Workshop on Data Management on New Hardware, 2010.
[30]
F. Xie, M. Martonosi, and S. Malik, "Intraprogram dynamic voltage scaling: Bounding opportunities with analytic modeling," ACM Trans. Archit. Code Optim, vol. 1, p. 2004, 2004.

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Published In

cover image ACM Conferences
ISCA '11: Proceedings of the 38th annual international symposium on Computer architecture
June 2011
488 pages
ISBN:9781450304726
DOI:10.1145/2000064
  • cover image ACM SIGARCH Computer Architecture News
    ACM SIGARCH Computer Architecture News  Volume 39, Issue 3
    ISCA '11
    June 2011
    462 pages
    ISSN:0163-5964
    DOI:10.1145/2024723
    Issue’s Table of Contents
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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Published: 04 June 2011

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  1. power management
  2. servers

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  • (2024)Data Center Power and Energy Management: Past, Present, and FutureIEEE Micro10.1109/MM.2024.342647844:5(30-36)Online publication date: Sep-2024
  • (2024)Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy modelFuture Generation Computer Systems10.1016/j.future.2024.04.059159:C(379-394)Online publication date: 1-Oct-2024
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  • (2022)AgileWatts: An Energy-Efficient CPU Core Idle-State Architecture for Latency-Sensitive Server Applications2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO)10.1109/MICRO56248.2022.00063(835-850)Online publication date: Oct-2022
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