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Run-time modeling and estimation of operating system power consumption

Published:10 June 2003Publication History

ABSTRACT

The increasing constraints on power consumption in many computing systems point to the need for power modeling and estimation for all components of a system. The Operating System (OS) constitutes a major software component and dissipates a significant portion of total power in many modern application executions. Therefore, modeling OS power is imperative for accurate software power evaluation, as well as power management (e.g. dynamic thermal control and equal energy scheduling) in the light of OS-intensive workloads. This paper characterizes the power behavior of a commercial OS across a wide spectrum of applications to understand OS energy profiles and then proposes various models to cost-effectively estimate its run-time energy dissipation. The proposed models rely on a few simple parameters and have various degrees of complexity and accuracy. Experiments show that compared with cycle-accurate full-system simulation, the model can predict cumulative OS energy to within 1% accuracy for a set of benchmark programs evaluated on a high-end superscalar microprocessor. When applied to track run-time OS energy profiles, the proposed routine level OS power model offers superior accuracy than a simpler, flat OS power model, yielding per-routine estimation error of less than 6%. The most striking observation is the strong correlation between power consumption and the instructions per cycle (IPC) during OS routine executions. Since tools and methodology to measure IPC exist on modern microprocessors, the proposed models can estimate OS power for run-time dynamic thermal and energy management.

References

  1. A. R. Alameldeen and D. A. Wood, Variability in Architectural Simulations of Multi-threaded Workloads, In Proceedings of the International Symposium on High Performance Computer Architecture, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. K Baynes, C. Collins, E. Fiterman, B. Ganesh, P. Lohout, C. Smit, T. B. Zhang and B. Jacob, The Performance and Energy Consumption of Three Embedded Real-Time Operating Systems, In Proceedings of the International Conference on Compilers, Architecture and Synthesis for Embedded Systems, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. F. Bellosa, The Benefits of Event- driven Energy Accounting in Power-sensitive Systems, In Proceedings of 9 th ACM SIGOPS European Workshop, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. R. Berrendorf and B. Mohr, PCL - The Performance Counter Library Version 2. 2, http://www.fz- juelich.de/zam/PCL/, Jan. 2003.Google ScholarGoogle Scholar
  5. D. Brooks, V. Tiwari and M. Martonosi, Wattch: A Framework for Architectural-level Power Analysis and Optimizations, In Proceedings of the International Symposium on Computer Architecture, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Brooks and M. Martonosi, Dynamic Thermal Management for High-Performance Microprocessors, In Proceedings of the International Symposium on High-Performance Computer Architecture, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. W. Chen, M. Dubois and P. Stenström, Integrating Complete-System and User-level Performance/Power Simulators: The SimWattch Approach, In Proceedings of International Symposium on Performance Analysis of Systems and Software, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. R. P. Dick, G. Lakshminarayana, A. Raghunathan and N. K. Jha, Power Analysis of Embedded Operating Systems, In Proceedings of the Design Automation Conference, June 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. Gurumurthi, A. Sivasubramaniam, M. J. Irwin, N. Vijaykrishnan, M. Kandemir, T. Li and L. K. John, Using Complete Machine Simulation for Software Power Estimation: The SoftWatt Approach, In Proceedings of the International Symposium on High Performance Computer Architecture, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. Huang, J. Renau, S. M. Yoo and J. Torrellas, A Framework for Dynamic Energy Efficiency and Temperature Management, In Proceedings of the International Symposium on Microarchitecture, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Intel Pentium 4 Processors - Manuals, Intel Corporation, 2002.Google ScholarGoogle Scholar
  12. R. Joseph and M. Martonosi, Run-Time Power Estimation in High Performance Microprocessors, In Proceeding of the International Symposium on Low Power Electronic Device, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. R. Lebeck, X. B. Fan, H. Zeng and C. S. Ellis, Power Aware Page Allocation, In Proceedings of International Conference on Architectural Support for Programming Languages and Operating Systems, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. T. Li and L. K. John, Understanding Control Flow Transfer and its Predictability in Java Processing, In Proceedings of International Symposium on Performance Analysis of Systems and Software, 2001.Google ScholarGoogle Scholar
  15. T. Li, L. K. John, A. Sivasubramaniam, N. Vijaykrishnan and J. Rubio, Understanding and Improving Operating System Effects in Control Flow Prediction, In Proceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Manne, A. Klauser and D. Grunwald, Pipeline Gating: Speculation Control for Energy Reduction, In Proceedings of the International Symposium on Computer Architecture, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. D. Ofelt and J. L. Hennessy, Efficient Performance Prediction for Modern Microprocessors, In Proceedings of the International Conference on Measurement and Modeling of Computer Systems, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Ousterhout, Why aren't Operating Systems Getting Faster as Fast as Hardware?, In Proceedings of the Summer USENIX Conference, 1990.Google ScholarGoogle Scholar
  19. S. Palacharla, N. P. Jouppi and J. E. Smith, Quantifying the Complexity of Superscalar Processors, CS-TR-1996-1328, University of Wisconsin, Nov. 1996.Google ScholarGoogle Scholar
  20. "PostgreSQL", http://www.us.postgresql.org/Google ScholarGoogle Scholar
  21. G. Qu, N. Kawabe, K. Usami and M. Potkonjak, FunctionLevel Power Estimation Methodology for Microprocessors, In Proceedings of the Design Automation Conference, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. M. Rosenblum, S. A. Herrod, E. Witchel and A. Gupta, Complete Computer System Simulation: the SimOS Approach, IEEE Parallel and Distributed Technology: Systems and Applications, vol. 3, no. 4, Winter 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. T. Sherwood, E. Perelman, G. Hamerly and B. Calder, Automatically Characterizing Large Scale Program Behavior, In Proceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A. Sinha, A. Wang and A. P. Chandrakasan, Algorithmic Transforms for Efficient Energy Scalable Computation, In Proceedings of the International Symposium on Low Power Electronics and Design, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. SPEC JVM98 Benchmarks, http://www.spec.org/jvm98/.Google ScholarGoogle Scholar
  26. T. K. Tan , A. Raghunathan, G. Lakshminarayana and N. K. Jha, High-level Software Energy Macro-modeling, In Proceedings of the Design Automation Conference, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. T. K. Tan, A. Raghunathan and N. Jha, Embedded Operating System Energy Analysis and Macro-modeling, In Proceedings of the International Conference on Computer Design, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. T. K. Tan, A. Raghunathan and N. Jha, EMSIM: An Energy Simulation Framework for an Embedded Operating System, In the Proceedings of the International Conference on Circuits and Systems, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  29. V. Tiwari, S. Malik, A. Wolfe and M. T. C. Lee, Instruction Level Power Analysis and Optimization of Software, Journal of VLSI Signal Processing, 1--18, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Transaction Processing Council, The TPC-C Benchmark, http://www.tpc.org/tpcc/.Google ScholarGoogle Scholar
  31. M. Valluri and L. K. John, Is Compiling for Performance == Compiling for Power?, In Proceedings of the 5th Annual Workshop on Interaction between Compilers and Computer Architectures, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  32. C. Xia and J. Torrellas, Comprehensive Hardware and Software Support for Operating Systems to Exploit MP Memory Hierarchies, IEEE Transactions on Computers, May 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. W. Ye, N. Vijaykrishnan, M. Kandermir and M. J. Irwin, The Design and Use of SimplePower: A Cycle-accurate Energy Estimation Tool, In Proceedings of Design Automation Conference, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. H. Zeng, X. B. Fan, C. Ellis, A. Lebeck and A. Vahdat, ECOSystem: Managing Energy as a First Class Operating System Resource, In the Proceedings of the International Symposium on Architecture Support for Program Language and Operating System, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      SIGMETRICS '03: Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
      June 2003
      338 pages
      ISBN:1581136641
      DOI:10.1145/781027
      • cover image ACM SIGMETRICS Performance Evaluation Review
        ACM SIGMETRICS Performance Evaluation Review  Volume 31, Issue 1
        June 2003
        325 pages
        ISSN:0163-5999
        DOI:10.1145/885651
        Issue’s Table of Contents

      Copyright © 2003 ACM

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      Publication History

      • Published: 10 June 2003

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      Acceptance Rates

      SIGMETRICS '03 Paper Acceptance Rate26of222submissions,12%Overall Acceptance Rate459of2,691submissions,17%

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