Advertisement

Enhanced Algorithm for Load Rebalancing in Cloud Computing Environment

  • T. ThiruvenkadamEmail author
  • Teklay Teklu
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)

Abstract

Emergence of cloud computing has facilitated the provisioning of computing resources in an on-demand basis that can be swiftly allocated, released and reallocated with minimum management effort and cost. One important element of cloud is the virtual machine which encapsulates business services and acts as a resource carrier. An important task of cloud computing is to find an optimal placement scheme that can map the virtual machines to physical machines. With the increasing prevalence of large scale cloud computing environments and ever changing workloads of virtual machines, the best VM placement also needs to be re mapped for providing better utilization of resources and maintaining the performance of the cloud system. Hence this research work presents a Load Rebalancing Algorithm that can be used after the VM placement in Cloud Computing Systems to improve the efficiency of VMs running in the cloud system.

Keywords

Virtual machine Physical machine VM placement VM migration 

References

  1. 1.
    Abdelsamea, A., Hemayed, E.E., Eldeeb, H., Elazhary, H.: Virtual machine consolidation challenges: a review. Int. J. Innov. Appl. Stud. 8(4), 1504–1516 (2014)Google Scholar
  2. 2.
    Naone, E.: Conjuring clouds. Technol. Rev. 112(4), 54–56 (2009)Google Scholar
  3. 3.
    Ghribi, C.: Energy Efficient Resource Allocation in Cloud Computing Environments, For the degree of Docteur De Telecom Sudparis, Informatique, Telecommunications et Electronique de Paris, Joint Thesis between Telecom Sudparis and University of Paris (2014)Google Scholar
  4. 4.
    Blackburn, M. (ed.): The Green Grid Data Center Compute Efficiency Metric : DCcE, White Paper #34, The Green Grid Publications, pp. 1–15 (2010)Google Scholar
  5. 5.
    Pillai, P., Shin, K.G.: Real-time dynamic voltage scaling for low-power embedded operating systems. In: ACM Proceedings of the Eighteenth ACM Symposium on Operating Systems Principles, pp. 89–102 (2001)Google Scholar
  6. 6.
    Beloglazov, A., Buyya, R.: Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. In: Proceedings of the 8th ACM International Workshop on Middleware for Grids, Clouds and e-Science, vol. 4, pp. 1–8 (2010a)Google Scholar
  7. 7.
    Thiruvenkadam, T., Karthikeyani, V.: Multi dimensional host load aware and user constraints based algorithm for scheduling virtual machines. In: Int. J. Adv. Comput. Technol. (IJACT) Indexed Scopus, 7(1), 56–66 (2015).Inspec ISSNGoogle Scholar
  8. 8.
    Chen, G., et al.: Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: ACM Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, pp. 337–350 (2008)Google Scholar
  9. 9.
    Guenter, B., Jain, N., Williams, C.: Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning. In: Proceedings of the 30th Annual IEEE International Conference on Computer Communications, pp. 1332–1340 (2011)Google Scholar
  10. 10.
    Orgerie, A.C., Lefevre, L.: ERIDIS: energy-efficient reservation infrastructure for large-scale distributed systems. Parallel Process. Lett. 21(2), 133–154 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Zhang, L., Zhuang, Y., Zhu, W.: Constraint programming based virtual cloud resources allocation model. Int. J. Hybrid Inf. Technol. 6(6), 333–344 (2013)CrossRefGoogle Scholar
  12. 12.
    Srikantaiah, S., Kansal, A., Zhao, F.: Energy aware consolidation for cloud computing. In: Proceedings of the 2008 Conference on Power Aware Computing and Systems, HotPower 2008, pp. 1–10 (2008)Google Scholar
  13. 13.
    Thiruvenkadam, T., Kamalakkannan, P.: Energy efficient multi dimensional host load aware algorithm for virtual machine placement and optimization in cloud environment. Indian J. Sci. 8(17), 59140, (2015).Technol. Indexed Scopus, Thomson Reuters Web of Science, Elsevier B.V. ISSN (Print): 0974-6846 ISSN (Online): 0974-5645, pp. 1–11Google Scholar
  14. 14.
    Kivity, A., Kamay, Y., Laor, D., Lublin, U., Liguori, A.: KVM: the Linux virtual machine monitor. In: Proceedings of the Linux Symposium, vol. 1, pp. 225–230 (2007)Google Scholar
  15. 15.
    Thiruvengadam, T., Karthikeyani, V.: Efficient hybrid genetic based multi dimensional host load aware algorithm for scheduling and optimization of virtual machines. J. Telematics Inform. (JTI) 2(1), 29–42 (2014). ISSN: 2303-3703Google Scholar
  16. 16.
    Dupont, C., Giuliani, G., Hermenier, F., Schulze, T., Somov, A.: An energy aware framework for virtual machine placement in cloud federated data centres. In: Third IEEE International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet (e-Energy), pp. 1–10 (2012)Google Scholar
  17. 17.
    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 (2011)Google Scholar
  18. 18.
    Weisstein, E.W.: L1-norm, http://mathworld.wolfram.com/L1-Norm.html (2016). Accessed October 2016

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information Technology, College of Engineering and TechnologyAdigrat UniversityAdigratEthiopia

Personalised recommendations