Advertisement

An Energy Efficient and Interference Aware Virtual Machine Consolidation Algorithm Using Workload Classification

  • Rachael ShawEmail author
  • Enda Howley
  • Enda Barrett
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)

Abstract

Inefficient resource usage is one of the greatest causes of high energy consumption in cloud data centers. Virtual Machine (VM) consolidation is an effective method for improving energy related costs and environmental sustainability for modern data centers. While dynamic VM consolidation can improve energy efficiency, virtualisation technologies cannot guarantee performance isolation between co-located VMs resulting in interference issues. We address the problem by introducing a energy and interference aware VM consolidation algorithm. The proposed algorithm utilizes the predictive capabilities of a Machine Learning (ML) model in an attempt to classify VM workloads to make more informed consolidation decisions. Furthermore, using recent workload data from Microsoft Azure we present a comparative study of two popular classification algorithms and select the model with the best performance to incorporate into our proposed approach. Our empirical results demonstrate how our approach improves energy efficiency by 31% while also reducing service violations by 69%.

Keywords

Energy efficiency Interference aware Virtual machine consolidation Machine Learning Classification 

Notes

Acknowledgments

The primary author would like to acknowledge the ongoing financial support provided to her by the Irish Research Council.

References

  1. 1.
    Moreno, I.S., Yang, R., Xu, J., Wo, T.: Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement. In: 2013 IEEE Eleventh International Symposium on Autonomous Decentralized Systems (ISADS), pp. 1–8. IEEE (2013)Google Scholar
  2. 2.
    Shaw, R., Howley, E., Barrett, E.: Predicting the available bandwidth on intra cloud network links for deadline constrained workflow scheduling in public clouds. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 221–228. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69035-3_15CrossRefGoogle Scholar
  3. 3.
    Whitney, J., Delforge, P.: Scaling up energy efficiency across the data center industry: evaluating key drivers and barriers. Technical report, Natural Resources Defense Council (2014)Google Scholar
  4. 4.
    Lee, Y.C., Zomaya, A.Y.: Energy efficient utilisation of resources in cloud computing systems. J. Supercomput. 60, 268–280 (2012)CrossRefGoogle Scholar
  5. 5.
    Verma, A., Ahuja, P., Neogi, A.: pMapper: power and migration cost aware application placement in virtualized systems. In: Issarny, V., Schantz, R. (eds.) Middleware 2008. LNCS, vol. 5346, pp. 243–264. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-89856-6_13CrossRefGoogle Scholar
  6. 6.
    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28, 755–768 (2012)CrossRefGoogle Scholar
  7. 7.
    Shaw, R., Howley, E., Barrett, E.: An advanced reinforcement learning approach for energy-aware virtual machine consolidation in cloud data centers. In: Proceedings of the 12th International Conference for Internet Technology and Secured Transactions, pp. 61–66. IEEE, December 2017Google Scholar
  8. 8.
    Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., Hieu, N.T., Tenhunen, H.: Energy-aware VM consolidation in cloud data centers using utilisation prediction model. IEEE Trans. Cloud Comput. 7, 524–536 (2016) CrossRefGoogle Scholar
  9. 9.
    Nguyen, T.H., Di Francesco, M., Yla-Jaaski, A.: Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers. IEEE Trans. Serv. Comput. (2017).  https://doi.org/10.1109/TSC.2017.2648791
  10. 10.
    Sampaio, A.M., Barbosa, J.G., Prodan, R.: PIASA: a power and interference aware resource management strategy for heterogeneous workloads in cloud data centers. Simul. Model. Pract. Theory 57, 142–160 (2015)CrossRefGoogle Scholar
  11. 11.
    Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exp. 24, 1397–1420 (2012)CrossRefGoogle Scholar
  12. 12.
    Xu, F., Liu, F., Liu, L., Jin, H., Li, B., Li, B.: iAware: making live migration of virtual machines interference-aware in the cloud. IEEE Trans. Comput. 63, 3012–3025 (2014)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Jersak, L.C., Ferreto, T.: Performance-aware server consolidation with adjustable interference levels. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 420–425. ACM, April 2016Google Scholar
  14. 14.
    Verboven, S., Vanmechelen, K., Broeckhove, J.: Black box scheduling for resource intensive virtual machine workloads with interference models. Future Gener. Comput. Syst. 29(8), 1871–1884 (2013)CrossRefGoogle Scholar
  15. 15.
    Shaw, R., Howley, E., Barrett, E.: An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions. Simul. Model. Pract. Theory (2019).  https://doi.org/10.1016/j.simpat.2018.09.019CrossRefGoogle Scholar
  16. 16.
    Zhang, J., Figueiredo, R.J.: Application classification through monitoring and learning of resource consumption patterns. In: 20th International Parallel and Distributed Processing Symposium, IPDPS 2006, pp. 10–20. IEEE (2006)Google Scholar
  17. 17.
    Nikravesh, A.Y., Ajila, S.A., Lung, C.H.: Towards an autonomic auto-scaling prediction system for cloud resource provisioning. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 35–45. IEEE (2015)Google Scholar
  18. 18.
    Mason, K., Duggan, J., Howley, E.: Evolving multi-objective neural networks using differential evolution for dynamic economic emission dispatch. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1287–1294. ACM, July 2017Google Scholar
  19. 19.
    Meyer, D., Leisch, F., Hornik, K.: The support vector machine under test. Neurocomputing 55(1–2), 169–186 (2003)CrossRefGoogle Scholar
  20. 20.
    Dixon, S.J., Brereton, R.G.: Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure. Chemometr. Intell. Lab. Syst. 95(1), 1–17 (2009)CrossRefGoogle Scholar
  21. 21.
    Cortez, E., Bonde, A., Muzio, A., Russinovich, M., Fontoura, M., Bianchini, R.: Resource central: understanding and predicting workloads for improved resource management in large cloud platforms. In: Proceedings of the 26th Symposium on Operating Systems Principles, pp. 153–167. ACM, October 2017Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Engineering and InformaticsNational University of IrelandGalwayIreland

Personalised recommendations