Impact of Virtual Machines Heterogeneity on Data Center Power Consumption in Data-Intensive Applications

  • Catalin Negru
  • Mariana Mocanu
  • Valentin Cristea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9438)


Cloud computing data centers consume large amounts of energy. Furthermore, most of the energy is used inefficiently. Computational resources such as CPU, storage, and network consume a lot of power. A good balance between the computing resources is mandatory. In the context of data-intensive applications, a significant portion of energy is consumed just to keep virtual machines or to move data around without performing useful computation. Power consumption optimization requires identification of the inefficiencies in the underlying system. Based on the relation between server load and energy consumption, in this paper we study the energy efficiency, and the penalties in terms of power consumption that are introduced by different degrees of heterogeneity for a cluster of heterogeneous virtual machines.


Cloud computing Energy-efficiency Virtualization 



The research presented in this paper is supported by the projects: CyberWater grant of the Romanian National Authority for Scientific Research, CNDI-UEFISCDI, project number 47/2012; clueFarm: Information system based on cloud services accessible through mobile devices, to increase product quality and business development farms - PN-II-PTPCCA-2013-4-0870. We would like to thank the reviewers for their time and expertise, constructive comments and valuable insight.


  1. 1.
    Natural Resources Defense Council, America’s Data Centers Consuming and Wasting Growing Amounts of Energy.
  2. 2.
    Barroso, L.A., Clidaras, J., Hlzle, U.: The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synth. Lect. Comput. Archit. 8(3), 1–154 (2013)CrossRefGoogle Scholar
  3. 3.
    Xiao, P., Hu, Z., Liu, D., Yan, G., Qu, X.: Virtual machine power measuring technique with bounded error in cloud environments. J. Netw. Comput. Appl. 36(2), 818–828 (2013)CrossRefGoogle Scholar
  4. 4.
    Enhanced Intel Speedstep Technology for the Intel Pentium M Processor.
  5. 5.
  6. 6.
    Cool ‘n’ Quiet Technology Installation Guide.
  7. 7.
  8. 8.
    Pillai, P., Shin, K.G.: Real-time dynamic voltage scaling for low-power embedded operating systems. In: ACM SIGOPS Operating Systems Review, vol. 35, no. 5, pp. 89–102. ACM, October 2001Google Scholar
  9. 9.
    Goudarzi, H., Pedram, M.: Energy-efficient virtual machine replication and placement in a cloud computing system. In: 2012 IEEE 5th International Conference on Cloud computing (CLOUD), pp. 750–757. IEEE, June 2012Google Scholar
  10. 10.
    Khosravi, A., Garg, S.K., Buyya, R.: Energy and carbon-efficient placement of virtual machines in distributed cloud data centers. In: Wolf, F., Mohr, B., an Mey, D. (eds.) Euro-Par 2013. LNCS, vol. 8097, pp. 317–328. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Sharifi, M., Salimi, H., Najafzadeh, M.: Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques. J. Supercomputing 61(1), 46–66 (2012)CrossRefGoogle Scholar
  13. 13.
    Lin, C.C., Liu, P., Wu, J.J.: Energy-aware virtual machine dynamic provision and scheduling for cloud computing. In: 2011 IEEE International Conference on Cloud computing (CLOUD), pp. 736–737. IEEE, July 2011Google Scholar
  14. 14.
    Feller, E., Rilling, L., Morin, C.: Energy-aware ant colony based workload placement in clouds. In: Proceedings of the 2011 IEEE/ACM 12th International Conference on Grid Computing, pp. 26–33. IEEE Computer Society, September 2011Google Scholar
  15. 15.
    Panigrahy, R., Talwar, K., Uyeda, L., Wieder, U.: Heuristics for vector bin packing. research. (2011)Google Scholar
  16. 16.
    Kou, L.T., Markowsky, G.: Multidimensional bin packing algorithms. IBM J. Res. dev. 21(5), 443–448 (1977). ISO 690MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Dorigo, M., Birattari, M.: Ant colony optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 36–39. Springer US, USA (2010)Google Scholar
  18. 18.
    Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L.: A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci. 79(8), 1230–1242 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Kolodziej, J., Khan, S.U., Xhafa, F.: Genetic algorithms for energy-aware scheduling in computational grids. In: 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pp. 17–24. IEEE, October 2011Google Scholar
  20. 20.
    Sfrent, A., Pop, F.: Asymptotic scheduling for many task computing in big data platforms. Inf. Sci. 319, 71–91 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Pop, F., Dobre, C., Cristea, V., Bessis, N., Xhafa, F., Barolli, L.: Deadline scheduling for aperiodic tasks in inter-cloud environments: a new approach to resource management. J. Supercomputing 71, 1–12 (2014)Google Scholar
  22. 22.
    Mobius, C., Dargie, W., Schill, A.: Power consumption estimation models for processors, virtual machines, and servers. IEEE Trans. Parallel Distrib. Syst. 25(6), 1600–1614 (2014)CrossRefGoogle Scholar
  23. 23.
    Figueiredo, J., Maciel, P., Callou, G., Tavares, E., Sousa, E., Silva, B.: Estimating reliability importance and total cost of acquisition for data center power infrastructures. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 421–426. IEEE, October 2011Google Scholar
  24. 24.
    Bohra, A.E., Chaudhary, V.: VMeter: power modelling for virtualized clouds. In: 2010 IEEE International Symposium on Parallel and Distributed Processing, Workshops and Phd Forum (IPDPSW), pp. 1–8. IEEE, April 2010Google Scholar
  25. 25.
    Berl, A., De Meer, H.: An energy consumption model for virtualized office environments. Future Gener. Comput. Syst. 27(8), 1047–1055 (2011)CrossRefGoogle Scholar
  26. 26.
    Lim, M. Y., Porterfield, A., Fowler, R.: SoftPower: fine-grain power estimations using performance counters. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, pp. 308–311. ACM, June 2010Google Scholar
  27. 27.
    Bircher, W.L., John, L.K.: Complete system power estimation using processor performance events. IEEE Trans. Comput. 61(4), 563–577 (2012)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Bertran, R., Becerra, Y., Carrera, D., Beltran, V., Gonzlez, M., Martorell, X., Ayguad, E.: Energy accounting for shared virtualized environments under DVFS using PMC-based power models. Future Gener. Comput. Syst. 28(2), 457–468 (2012). ChicagoCrossRefGoogle Scholar
  29. 29.
    Aroca, J.A., Anta, A.F., Mosteiro, M.A., Thraves, C., Wang, L.: Power-efficient assignment of virtual machines to physical machines. In: Pop, F., Potop-Butucaru, M. (eds.) ARMS-CC 2014. LNCS, vol. 8907, pp. 70–87. Springer, Heidelberg (2014)Google Scholar
  30. 30.
    Microsoft Azure cloud computing platform.
  31. 31.
    Mhedheb, Y., Jrad, F., Tao, J., Zhao, J., Kołodziej, J., Streit, A.: Load and thermal-aware VM scheduling on the cloud. In: Kołodziej, J., Di Martino, B., Talia, D., Xiong, K. (eds.) ICA3PP 2013, Part I. LNCS, vol. 8285, pp. 101–114. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  32. 32.
    Niewiadomska-Szynkiewicz, E., Sikora, A., Arabas, P., Kamola, M., Mincer, M.: Dynamic power management in energy-aware computer networks and data-intensive computing systems. Future Gener. Comput. Syst. 37, 284–296 (2014)CrossRefGoogle Scholar
  33. 33.
    Kolodziej, J., Szmajduch, M., Maqsood, T., Madani, S.A., Min-Allah, N., Khan, S.U.: Energy-aware grid scheduling of independent tasks and highly distributed data. In: 11th International Conference on Frontiers of Information Technology (FIT), pp. 211–216. IEEE, December 2013Google Scholar
  34. 34.
    Kolodziej, J., Szmajduch, M., Khan, S.U., et al.: Genetic-based solutions for independent batch scheduling in data grids. In: Proceedings of 27th European Conference on Modelling and Simulation, pp. 504–510 (2013)Google Scholar
  35. 35.
    Kolodziej, J., Khan, S.U.: Multi-level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment. Inf. Sci. 214, 1–19 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Catalin Negru
    • 1
  • Mariana Mocanu
    • 1
  • Valentin Cristea
    • 1
  1. 1.Computer Science DepartmentUniversity Politehnica of BucharestBucharestRomania

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