A novel energy efficient virtual machine configuration and migration technique

  • Authors

    • L Srinivasa Rao Assistant professor in cse dept
    • I Raviprakash Reddy Professor&HOD of IT Dept ,GNITS Shaik pet, Hyderabad
    2018-09-17
    https://doi.org/10.14419/ijet.v7i4.13236
  • Energy Efficient VM Migration, Migration Technique, VM Components, VMM, VM Migration.

  • The recent growth in the data centre usage and the higher cost of managing virtual machines clearly demands focused research in reducing the cost of managing and migrating virtual machines. The cost of virtual machine management majorly includes the energy cost, thus the best available virtual machine management and migration techniques must have the lowest energy consumption. The management of virtual machine is solely dependent on the number of applications running on that virtual machine, where there is a very little scope for researchers to improve the energy. The second parameter is migration in order to balance the load, where a number of researches are been carried out to reduce the energy consumption. This work addresses the issue of energy consumption during virtual machine migration and proposes a novel virtual machine migration technique with improvement of energy consumption. The novel algorithm is been proposed in two enhancements as VM selection and VM migration, which demonstrates over 47% reduction in energy consumption.

     

     

  • References

    1. [1] J. Rao, Y. Wei, J. Gong and C.-Z. Xu “Qos guarantees and service differentiation for dynamic cloud applications", IEEE Trans. Netw. Serv. Manag., vol. 10, no. 1, pp.43 -55 2013 https://doi.org/10.1109/TNSM.2012.091012.120238.

      [2] Li, A. Raghunathan and N.K. Jha, &ldquo,Secure Virtual Machine Execution under an Untrusted Management OS,&rdquo, Proc. Int',l Conf. Cloud Computing, pp. 172-180, July 2010.

      [3] Intel VT-D, http://www.intel.com/technology/virtualization/technology.htm, 2012.

      [4] BYTEmark, http://www.tux.org/mayer/linux/byte/bdoc.pdf, 2012.

      [5] M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.H. Katz, A. Konwinski, G. Lee, D.A. Patterson, A. Rabkin, I. Stoica and M. Zaharia, &ldquo,Above the Clouds: A Berkeley View of Cloud Computing,&rdquo, Technical Report UCB/EECS-2009-28, http://www.eecs. berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.html, 2010.

      [6] T. Deshane, Z. Shepherd, J.N. Matthews, M. Ben-Yehuda, A. Shah and B. Rao, &ldquo,Quantitative Comparison of Xen and KVM,&rdquo, Xen Summit Boston 2008, http://xen.org/xensummit/xensummit_ summer_2008.html, 2010.

      [7] Y. Dong, X. Yang, X. Li, J. Li, K. Tian and H. Guan, &ldquo,High Performance Network Virtualization with SR-IOV,&rdquo, Proc. IEEE 16th Int',l Symp. High Performance Computer Architecture (HPCA), pp. 1-10, 2010.

      [8] P. Padala Automated management of virtualized data centers, 2010:Univ. of Michigan.

      [9] T. Patikirikorala, A. Colman, J. Han and L. Wang “A systematic survey on the design of self-adaptive software systems using control engineering approaches", Proc. Symp. Softw. Eng. Adaptive Self-Manag. Syst., pp.33 -42 2012.

      [10] X. Wang and Y. Wang “Coordinating power control and performance management for virtualized server clusters", IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 2, pp.245 -259 2011 https://doi.org/10.1109/TPDS.2010.91.

      [11] X. Wang, M. Chen and X. Fu “MIMO power control for high-density servers in an enclosure", IEEE Trans. Parallel Distrib. Syst., vol. 21, no. 10, pp.1412 -1426 2010 https://doi.org/10.1109/TPDS.2010.31.

      [12] T. Patikirikorala, A. Colman, J. Han and L. Wang “A multi-model framework to implement self-managing control systems for QoS management", Proc. Int. Symp. Softw. Eng. Adaptive Self-Manag. Syst., pp.218 -227 2011.

      [13] X. Liu, C. Wang, B. Zhou, J. Chen, T. Yang and A. Zomaya, "Priority-based consolidation of parallel workloads in the cloud", IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 9, pp. 1874-1883, 2013 https://doi.org/10.1109/TPDS.2012.262.

      [14] Carrera, M. Steindler, I. Whalley, J. Torres and E. Ayguad, "Autonomic placement of mixed batch and transactional workloads", IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 2, pp. 219-231, 2012 https://doi.org/10.1109/TPDS.2011.129.

      [15] Y. Lee and A. Zomaya, "Energy efficient utilization of resources in cloud computing systems", The J. Supercomput., vol. 60, no. 2, pp. 268-280, 2012 https://doi.org/10.1007/s11227-010-0421-3.

      [16] T. Ferret, M. Netto R. Calheiros and C. De Rose, "Server consolidation with migration control for virtualized data centers", Future Generation Comput. Syst., vol. 27, no. 8, pp. 1027-1034, 2011 https://doi.org/10.1016/j.future.2011.04.016.

      [17] K. Mills, J. Filliben and C. Dabrowski, "Comparing vm-placement algorithms for on-demand clouds", Proc. IEEE 3rd Int. Conf. Cloud Comput. Tech. Sci., pp. 91-98, 2011. https://doi.org/10.1109/CloudCom.2011.22.

  • Downloads

  • How to Cite

    Srinivasa Rao, L., & Raviprakash Reddy, I. (2018). A novel energy efficient virtual machine configuration and migration technique. International Journal of Engineering & Technology, 7(4), 2391-2396. https://doi.org/10.14419/ijet.v7i4.13236