An Energy-Saving Load Balancing Method in Cloud Data Centers

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


With the development of virtualization technology, data center virtualization in Cloud Computing gradually become a hot topic. In the premise of ensuring users’ SLA, this paper considers the utilization of server resources, whose objective is to minimize the number of opening servers. We propose an energy-saving strategy based on live virtual machines migration. Our ARMA-based load forecasting reduces the occurrence of virtual machines’ migration caused by instantaneous load peaks. Then we select migration virtual machines and destination servers based on our proposed algorithms. Finally, the data center reaches a load balancing state. The experiments show that the strategy can improve server resource utilization and reduce energy consumption.


Cloud computing Virtual resource scheduling Virtual machine migration Energy-saving 


  1. 1.
    Group of virtualization and cloud computing (2009) Virtualization and cloud computing. Publishing House of Electronics Industry, BeijingGoogle Scholar
  2. 2.
    Amazon web services introduction [EB/OL]. http//
  3. 3.
    Microsoft (2008) Azure service platform overview. INSIGHT (Microsoft) 2:1–23Google Scholar
  4. 4.
    Qian Q, Li C et al (2012) Virtual resources review of cloud data center. Appl Res Comput 29(7):2411–2415Google Scholar
  5. 5.
    Wood T (2007) Black-box and gray-box strategies for virtual machine migration. In Proceedings of the 4th international conference on networked systems design and implementation. [S. 1.]: IEEE (in press), pp 229–242Google Scholar
  6. 6.
    Nathuji R, Schwan K (2007) Virtual power. Coordinated power management in virtualized enterprise systems. In: Proceedings of twenty-first ACM SIGOPS symposium on operating systems principles, vol 21, pp 265–278Google Scholar
  7. 7.
    Liu Y, Gao Q, Chen Y (2010) A load balancing method of virtual machine resource in virtual computing environments. Comput Eng 36(16):30–32Google Scholar
  8. 8.
    Zhou W, Yang S et al (2010) VMC Tune a load balancing scheme for virtual machine cluster based on dynamic resource allocation. In: Proceedings of the 9th international conference on grid and cloud computing, pp 81–86Google Scholar
  9. 9.
    Liu S, Quan G, Ren SP (2011) On-line preemptive scheduling of real-time services with profit and penalty. In: Proceedings of IEEE southeast conference, pp 287–292Google Scholar
  10. 10.
    Yang W, Zhu Q et al (2006) Servers load prediction based on times series. Comput Eng 32(19):143–145, 148Google Scholar
  11. 11.
    Buyya R, Ranjan R, Calheiros RN (2009) Modeling and simulation of scalable cloud computing environments and the cloudsim Tkklkit. Challenges and opportunities. In: Proceedings of international conference on high performance computing and simulation, KochiGoogle Scholar
  12. 12.
    Liu Y, Wang X, Wang Z et al (2012) Virtual machine resource scheduling driven by energy efficiency and trust. Appl Res Comput 29(7):2479–2483Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Management Science and EngineeringShandong Normal UniversityJi’nanChina

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