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Combining global regression and local approximation in server power modeling

  • Xiaoming DuEmail author
  • Cong Li
Special Issue Paper
  • 124 Downloads

Abstract

To evaluate energy use in green clusters, power models take the resource utilization data as the input to predict server power consumption. We propose a novel method in power modeling combining a global linear model and a local approximation model. The new model enjoys high accuracy by compensating the global linear model with local approximation and exhibits robustness with the generalization capability of the global regression model. Empirical evaluation demonstrates that the new approach outperforms the two existing approaches to server power modeling, the linear model and the k-nearest neighbor regression model.

Keywords

Server power modeling Global regression Local approximation Linear model Spatial interpolation 

Notes

Acknowledgements

We thank Rahul Khanna, Honesty Young, and Shilin Wang for their comments on an early draft of the paper.

References

  1. 1.
    Gurumurthi S, Sivasubramaniam A, Irwin MJ, Vijaykrishnan N, Kandemir M, Li T, John LK (2002) Using complete machine simulation for software power estimation: the SoftWatt approach. In: Proceedings of the eighth international symposium on high-performance computer architecture (HPCA-2002). Washington, pp 141Google Scholar
  2. 2.
    Economou D, Rivoire S, Kozyrakis C, Ranganathan P (2006) Full-system power analysis and modeling for server environments. In: Proceedings of the workshop on modeling, benchmarking and simulation (MoBS-2006), BostonGoogle Scholar
  3. 3.
    Dalton D, Vadher A, Laoghaire D, McCarthy A, Steger C (2012) Power profiling and auditing consumption systems and methods, United States Patent Application Publication, Pub. No.: US 2012/0011378Google Scholar
  4. 4.
    Fan X, Weber W, Barroso L (2007) Power provisioning for a warehouse-sized computer. In: Proceedings of the thirty-fourth international symposium on computer architecture (ISCA-2007). San Diego, pp 13–23Google Scholar
  5. 5.
    Kansal A, Zhao F, Liu J, Kothari N, Bhattacharya A (2010) Virtual machine power metering and provisioning. In: Proceedings of the first ACM symposium on cloud computing (SoCC-2010). Indianapolis, pp 39–50Google Scholar
  6. 6.
    Mitchell T (1997) Machine learning. McGraw-Hill Inc, New YorkzbMATHGoogle Scholar
  7. 7.
    Ng AY (2004) Feature selection, \(L_1\) vs. \(L_2\) regularization, and rotational invariance. In: Proceedings of the twenty-first international conference on machine learning (ICML-2004), BanffGoogle Scholar
  8. 8.
    Iba W, Langley P (1992) Induction of one-level decision trees. In: Proceedings of the nineth international conference on machine learning (ICML-1992). San Francisco, pp 233–240Google Scholar
  9. 9.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  10. 10.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ShanghaiPeople’s Republic of China

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