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Machine Learning Models for Predicting Timely Virtual Machine Live Migration

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Computer Performance Engineering (EPEW 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10497))

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Abstract

Virtual machine (VM) consolidation is among the key strategic approaches that can be employed to reduce energy consumption in large computing infrastructure. However, live migration of VMs is not a trivial operation and consequently not all VMs can be easily consolidated in all circumstances. In this paper we present experiments attempting to live migrate the Kernel-based VM (KVM) executing workload form the SPECjvm2008 benchmark. In order to understand what factors influence live migration we investigate three machine learning models to predict successful live migration using different training and evaluation sets drawn from our experimental data.

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References

  1. Clark, C., Fraser, K., Hand, S., Hansen, J.G., Jul, E., Limpach, C., Pratt, I., Warfield, A: Live migration of virtual machines. In: NSDI (2005)

    Google Scholar 

  2. KVM. http://www.linux-kvm.org. Accessed 5 May 2017

  3. SPECjvm2008. http://www.spec.org/jvm2008. Accessed 5 May 2017

  4. Strunk, A., Dargie, W.: Does live migration of virtual machines cost energy? In: IEEE AINA (2013)

    Google Scholar 

  5. Rybina, K., Patni, A., Schill, A.: Analysing the migration time of live migration of multiple virtual machines. In: CLOSER, pp. 590–597 (2014)

    Google Scholar 

  6. Akoush, S., Sohan, R., Rice, A., Moore, A.W., Hopper, A.: Predicting the performance of virtual machine migration. In: IEEE MASCOTS, pp. 37–46 (2010)

    Google Scholar 

  7. Hu, W., Hicks, A., Zhang, L., Dow, E.M., Soni, V., Jiang, H., Bull, R., Matthews, J.N.: A quantitative study of virtual machine live migration. In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, p. 11. ACM (2013)

    Google Scholar 

  8. McGough, A.S., Moubayed, N.A., Forshaw, M.: Using machine learning in trace-driven energy-aware simulations of high-throughput computing systems. In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion, pp. 55–60. ACM (2017)

    Google Scholar 

  9. Uriarte, R.B., Tiezzi, F., Tsaftaris, S.A.: Supporting autonomic management of clouds: service clustering with random forest. IEEE Trans. Netw. Serv. Manage. 13(3), 595–607 (2016)

    Article  Google Scholar 

  10. Openfiler. http://www.openfiler.com. Accessed 5 May 2017

  11. Alrajeh, O.: VM live migration script. http://www.github.com/oalrajeh/VM_Live_Migration. Accessed 5 May 2017

  12. Memusg. https://gist.github.com/netj/526585. Accessed 5 May 2017

  13. Kuhn, M.: Caret package. J. Stat. Softw. 28(5), 1–26 (2008)

    Article  Google Scholar 

  14. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2016)

    Google Scholar 

  15. Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  16. Ridgeway, G.: Generalized boosted models: a guide to the GBM package. Update 1(1), 2007 (2007)

    Google Scholar 

  17. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  18. Feller, W.: An Introduction to Probability Theory and Its Applications. Vol. 1, vol. 3. Wiley, New York (1968)

    MATH  Google Scholar 

  19. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  20. Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)

    Article  Google Scholar 

  21. Ling, C.X., Huang, J., Zhang, H.: AUC: a better measure than accuracy in comparing learning algorithms. In: Xiang, Y., Chaib-draa, B. (eds.) AI 2003. LNCS, vol. 2671, pp. 329–341. Springer, Heidelberg (2003). doi:10.1007/3-540-44886-1_25

    Chapter  Google Scholar 

  22. Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence, vol. 14, pp. 1137–1145, Stanford, CA (1995)

    Google Scholar 

  23. Kuhn, M.: Building predictive models in R using the caret package. J. Stat. Softw. 28(1), 1–26 (2008)

    MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing Science, Newcastle University, Newcastle upon Tyne, UK

    Osama Alrajeh, Matthew Forshaw & Nigel Thomas

Authors
  1. Osama Alrajeh
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  2. Matthew Forshaw
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  3. Nigel Thomas
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Corresponding author

Correspondence to Osama Alrajeh .

Editor information

Editors and Affiliations

  1. Bristol, Germany

    Philipp Reinecke

  2. University of L’Aquila, L’Aquila, Italy

    Antinisca Di Marco

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Alrajeh, O., Forshaw, M., Thomas, N. (2017). Machine Learning Models for Predicting Timely Virtual Machine Live Migration. In: Reinecke, P., Di Marco, A. (eds) Computer Performance Engineering. EPEW 2017. Lecture Notes in Computer Science(), vol 10497. Springer, Cham. https://doi.org/10.1007/978-3-319-66583-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-66583-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66582-5

  • Online ISBN: 978-3-319-66583-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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