Dynamic Energy-Efficient Virtual Machine Placement Optimization for Virtualized Clouds

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 288)

Abstract

A virtual machine placement strategy based on the trade-off between energy consumption and SLA is presented. Aiming at dynamical changes of workload requirements, a self-adaptive placement strategy RLWR based on robust local weight regression is presented, which could decide the overload time of hosts dynamically. After detecting overloaded hosts, one virtual machine migration selection algorithm MNM is proposed. The MNM’s objective is to get minimal migration number. The migrated virtual machines are deployed using bin-packing algorithm PBFDH. The experimental results show that our algorithm has obvious advantages than other algorithms.

Keywords

Cloud computing Virtual machine placement Energy consumption 

References

  1. 1.
    Vaquero LM, Rodero-Merino L, Caceres J (2008) A break in the clouds: towards a cloud definition. SIGCOMM Comput Commun Rev 39(1):50–55CrossRefGoogle Scholar
  2. 2.
    Buyya R, Chee Shin Y, Venugopal S (2008) Market-oriented Cloud computing: vision, hype, and reality for delivering IT services as computing utilities. In: Proceedings of 10th IEEE conference on high performance computing and Communications, pp 5–13Google Scholar
  3. 3.
    Lin M, Wierman A, Andrew LLH (2012) Online algorithm for geographical load balancing. In: Proceedings of 3rd international green computing conference, pp 1–10Google Scholar
  4. 4.
    Lin M, Wierman A, Andrew LLH, Thereska E (2011) Dynamic right-sizing for power-proportional data centers. In: Proceedings of 30th IEEE international conference on computer communication, pp 1098–1106Google Scholar
  5. 5.
    Plaxton CG, Sun Y, Tiwari M (2006) Reconfigurable resource scheduling. In: Proceedings of 18th annual ACM symposium on parallelism in algorithm and architecture, pp 93–102Google Scholar
  6. 6.
    Irani S, Gupta R, Shukla S (2002) Competitive analysis of dynamic power management strategies for systems with multiple power savings states. In: Proceedings of design, automation and test in Europe, pp 117–123Google Scholar
  7. 7.
    Fan X, Weber W-D, Barroso L (2007) Power provisioning for a warehouse-sized computer. In: Proceedings of 34th annual international symposium on computer architecture, pp 13–23Google Scholar
  8. 8.
    Fang Y, Tang D, Ge J (2012) Energy-aware schedule strategy based on dynamic migration of virtual machines in cloud computing. J Comput Inf Syst 8(10):4201–4208Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Mathematics and Computer ScienceWuhan Polytechnic UniversityWuhanChina
  2. 2.G-CLOUD Technology Co. Ltd.DongguanChina
  3. 3.Chinese Academy of ScienceCloud Computing CenterDongguanChina

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