CHAOS: Composable Highly Accurate OS-based power models

Proceedings - 2012 IEEE International Symposium on Workload Characterization, IISWC 2012

Volumn , Issue , 2012, Pages 153-163

  Davis, John D ^a   Rivoire, Suzanne ^b   Goldszmidt, Moises ^a   Ardestani, Ehsan K ^c  

^a MICROSOFT RESEARCH   (United States)

^b SONOMA STATE UNIVERSITY   (United States)

^c University of California Santa Cruz   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURATE PREDICTION; AUTOMATICALLY GENERATED; BOARD-LEVEL; CHAOS MODELS; CPU UTILIZATION; CROSS-PLATFORM; DATA CENTERS; DYNAMIC RANGE; ENERGY EFFICIENT; FEATURE SETS; HARDWARE COUNTERS; LOW POWER; MODEL COMPLEXITY; PERFORMANCE COUNTERS; POWER MODEL; POWER MODELING; PREDICTION ACCURACY; SOFTWARE FRAMEWORKS; SYSTEM BEHAVIORS;

COMPUTER PROGRAMMING; EMBEDDED SYSTEMS; LAPTOP COMPUTERS;

ENERGY EFFICIENCY;

EID: 84873478913     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IISWC.2012.6402920     Document Type: Conference Paper

References (41)

1. J. Hamilton, "Annual fully burdened cost of power," Online. Available: http://perspectives.mvdirona.com/2008/12/06/ AnnualFullyBurdenedCostOfPower.aspx, 2008.
2. J. Park, "Open Compute Project: Data center v.1.0," Online. Available: http://opencompute.org/wp/wpcontent/uploads/2011/07/DataCenter- Electrical-Specifications.pdf, April 2011.
3. J. D. Davis et al., "Accounting for variability in large-scale cluster power models," in Proc. Exascale Evaluation and Research Techniques Wkshp. (EXERT), 2011.
4. J. C. McCullough et al., "Evaluating the effectiveness of model-based power characterization," in Proc. USENIX Annu. Technical Conf., 2011.
5. K. Rajamani et al., "Power management solutions for computer systems and datacenters," in Proc. ISLPED, 2008.
6. D. G. Andersen et al., "FAWN: A fast array of wimpy nodes," in Proc. SOSP, 2009.
7. J. Hamilton, "Cooperative expendable micro-slice servers (CEMS): Low cost, low power servers for internet-scale services," in Proc. Biennial Conf. Innovative Data Systems (CIDR), 2009.
8. L. Keys et al., "The search for energy efficient building blocks for the data center," in Proc. Wkshp. on Energy-Efficient Design (WEED), 2010.
9. A. S. Szalay et al., "Low-power Amdahl-balanced blades for data intensive computing," in Proc. HotPower, 2009.
10. V. Vasudevan et al., "Energy-efficient cluster computing with FAWN: Workloads and implications," in Proc. e-Energy, 2010.
11. P. Ranganathan et al., "Ensemble-level power management for dense blade servers," in Proc. ISCA, 2006.
12. Y. Chen et al., "Managing server energy and operational costs in hosting centers," in Proc. SIGMETRICS, 2005.
13. A. Lewis et al., "Run-time energy consumption estimation based on workload in server systems," in Proc. HotPower, 2008.
14. S. Govindan et al., "Statistical profiling-based techniques for effective power provisioning in data centers," in Proc. EuroSys, 2009.
15. J. Choi et al., "Profiling, prediction, and capping of power consumption in consolidated environments," in Proc. MASCOTS, 2008.
16. K. Singh et al., "Real time power estimation and thread scheduling via performance counters," in Proc. dasCMP, 2008.
17. S. Rivoire et al., "A comparison of high-level full-system power models," in Proc. HotPower, 2008.
18. D. Meisner and T. F. Wenisch, "Peak power modeling for data center servers with switched-mode power supplies," in Proc. ISLPED, 2010.
19. D. Economou et al., "Full-system power analysis and modeling for server environments," in Proc. Wkshp. on Modeling, Benchmarking and Simulation (MoBS), 2006.
20. D. C. Snowdon et al., "Accurate on-line prediction of processor and memory energy usage under voltage scaling," in Proc. EMSOFT, 2007.
21. A. Lewis et al., "Chaotic attractor prediction for server runtime energy consumption," in Proc. HotPower, 2010.
22. T. Li and L. K. John, "Run-time modeling and estimation of operating system power consumption," in Proc. SIGMETRICS, 2003.
23. D. C. Snowdon et al., "Koala: A platform for OS-level power management," in Proc. EuroSys, 2009.
24. T. Heath et al., "Energy conservation in heterogeneous server clusters," in Proc. PPoPP, 2005.
25. X. Fan et al., "Power provisioning for a warehouse-sized computer," in Proc. ISCA, 2007.
26. S. Bird, "Fixing performance counters: Performance monitoring hardware for the datacenter," in Proc. Wkshp. on Architectural Concerns in Large Datacenters (ACLD), 2009.
27. V. M. Weaver and S. A. McKee, "Can hardware performance counters be trusted?" in Proc. IISWC, 2008.
28. A. Kansal et al., "Virtual machine power metering and provisioning," in Proc. Symp. Cloud Computing (SoCC), 2010.
29. W. Lang and J. M. Patel, "Energy management for MapReduce clusters," in Proc. VLDB, vol. 3, no. 1, 2010.
30. M. Isard et al., "Quincy: Fair scheduling for distributed computing clusters," in Proc. SOSP, 2009.
31. I. Park and R. Buch, "Improve debugging and performance tuning with ETW," MSDN Magazine, April 2007.
32. Microsoft, "Joulemeter," Online. Available: http://research.microsoft.com/en-us/downloads/fe9e10c5-5c5b-450c-a674- daf55565f794/, 2011.
33. M. Isard et al., "Dryad: Distributed data-parallel programs from sequential building blocks," in Proc. EuroSys, 2007.
34. "ClueWeb09 dataset," Online. Available: http://lemurproject. org/clueweb09/.
35. L. A. Barroso, "Warehouse-scale computing: Entering the teenage decade," plenary talk, Federated Computing Research Conf. (FCRC), 2011.
36. A. Gelman et al., Bayesian Data Analysis, 2nd ed. Chapman and Hall/CRC, 2003.
37. Microsoft, "Windows 2000 Resource Kit performance counters: Counters by object," Online. Available: http://msdn.microsoft.com/en-us/library/ ms803998.aspx.
38. T. Hastie et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer, 2009.
39. M. Y. Park and T. Hastie, "L1-regularization path algorithm for generalized linear models," Journal of the Royal Statistical Society: Series B, vol. 69, 2007.
40. L. Wasserman, All of Statistics: A Concise Course in Statistical Inference, 2nd ed. Springer, 2010.
41. J. H. Friedman, "Multivariate adaptive regression splines," Annals of Statistics, vol. 19, no. 1, pp. 1-67, 1991.