Advertisement

Cluster Computing

, Volume 22, Supplement 6, pp 14073–14080 | Cite as

Energy efficient scheduling for cloud data centers using heuristic based migration

  • G. Ganesh KumarEmail author
  • P. Vivekanandan
Article
  • 88 Downloads

Abstract

Cloud computing has now become extremely fast spread in the various fields of research, industry and computing in that of the recent years. Being a part of the services that are offered there are identified some new possibilities for building applications and also for providing some services to that of the end user by means of virtualization by the internet. The energy efficiency is that global challenge in today’s world and virtualization will provide a promising approach for re-dividing the hardware and also the software more than the physical servers in their multiple applications which will be able to run on a similar physical server even while having different resources. Both the Heuristic and the metaheuristic-based techniques have proven to have achieved some near-optimal solutions in a reasonable time frame for various complex problems. In this work, a shuffled frog leaping algorithm (SFLA) has been proposed for enhancing the total time of execution, the number of migration and the consumption of energy than that of the previous work that is based on the particle swarm optimization (PSO) algorithm. The results show that the total simulation time (s) taken by the data center when the actual number of VMs is 100 using the SFLA is less and it achieves much better performance than the mechanism using PSO by about 17.8%.

Keywords

Computing Energy efficiency Virtualization Virtual machines (VMs) Particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) 

References

  1. 1.
    Liu, X. F., Zhan, Z. H., Du, K. J., Chen, W. N.: Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation. ACM. pp. 41–48, (2014)Google Scholar
  2. 2.
    Luo, J.P., Li, X., Chen, M.R.: Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers. Expert Syst. Appl. 41(13), 5804–5816 (2014)CrossRefGoogle Scholar
  3. 3.
    Kim, N., Cho, J., Seo, E.: Energy-credit scheduler: an energy-aware virtual machine scheduler for cloud systems. Future Generat. Comput. Syst. 32, 128–137 (2014)CrossRefGoogle Scholar
  4. 4.
    Xie, R., Jia, X., Yang, K., Zhang, B.: Energy saving virtual machine allocation in cloud computing. In: Proceedings of the Distributed Computing Systems workshops (ICDCSW), 2013 IEEE 33rd International Conference. IEEE. pp. 132–137, (2013)Google Scholar
  5. 5.
    Coffman Jr., E.G., Csirik, J., Galambos, G., Martello, S., Vigo, D.: Bin Packing Approximation Algorithms: Survey and Classification. Handbook of combinatorial optimization, pp. 455–531. Springer, New York (2013)CrossRefGoogle Scholar
  6. 6.
    Pacini, E., Mateos, C., García Garino, C.: Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments. CLEI Electron. J. 17(1), 3 (2014)CrossRefGoogle Scholar
  7. 7.
    Hu, X.: Adaptive optimization of cloud security resource dispatching SFLA algorithm. Int. J. Eng. Sci. (IJES) 4(3), 39–43 (2015)Google Scholar
  8. 8.
    Liu, H., Jin, H., Xu, C.Z., Liao, X.: Performance and energy modeling for live migration of virtual machines. Clust. Comput. 16(2), 249–264 (2013)CrossRefGoogle Scholar
  9. 9.
    Liaqat, M., Ninoriya, S., Shuja, J., Ahmad, R. W.,Gani, A.: Virtual machine migration enabled cloud resource management: a challenging task. arXiv preprint arXiv:1601.03854 (2016)
  10. 10.
    Chen, X., Huang, W.: Research of improved shuffled frog leaping algorithm in cloud computing resources. Int. J. Grid Distrib. Comput. 9(3), 71–82 (2016)CrossRefGoogle Scholar
  11. 11.
    Razali, R. A. M., Ab Rahman, R., Zaini, N.,Samad, M.: Virtual machine migration implementation in load balancing for Cloud computing. In: Proceedings of the Intelligent and Advanced Systems (ICIAS), 2014 5th International Conference. IEEE. pp. 1–4 (2014)Google Scholar
  12. 12.
    Pandey, S., Wu, L., Guru, S. M., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications. In: Proceedings of the 24th IEEE international conference on Advanced Information Networking and Applications (AINA) in cloud computing. IEEE. pp. 400–407, (2010)Google Scholar
  13. 13.
    Xie, X., Liu, R., Cheng, X., Hu, X., Ni, J.: Trust-driven and PSO-SFLA based job scheduling algorithm on Cloud. Intell. Autom. Soft Comput. 22(4), 561–566 (2016)CrossRefGoogle Scholar
  14. 14.
    Binitha, S., Sathya, S.S.: A survey of bio inspired optimization algorithms. Int. J. Soft Comput. Eng. 2(2), 137–151 (2012)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPark College of Engineering & TechnologyCoimbatoreIndia

Personalised recommendations