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Mobile Networks and Applications

, Volume 21, Issue 5, pp 793–805 | Cite as

Adaptive VM Management with Two Phase Power Consumption Cost Models in Cloud Datacenter

  • Dong-Ki Kang
  • Fawaz Al-Hazemi
  • Seong-Hwan Kim
  • Min Chen
  • Limei Peng
  • Chan-Hyun Youn
Article

Abstract

As cloud computing models have evolved from clusters to large-scale data centers, reducing the energy consumption, which is a large part of the overall operating expense of data centers, has received much attention lately. From a cluster-level viewpoint, the most popular method for an energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers that do not have any virtual resources running. To maximize the energy efficiency with DRS, one of the primary adaptive resource management strategies is a Virtual Machine (VM) migration which consolidates VM instances into as few servers as possible. In this paper, we propose a Two Phase based Adaptive Resource Management (TP-ARM) scheme that migrates VM instances from under-utilized servers that are supposed to be turned off to sustainable ones based on their monitored resource utilizations in real time. In addition, we designed a Self-Adjusting Workload Prediction (SAWP) method to improve the forecasting accuracy of resource utilization even under irregular demand patterns. From the experimental results using real cloud servers, we show that our proposed schemes provide the superior performance of energy consumption, resource utilization and job completion time over existing resource allocation schemes.

Keywords

Cloud computing Virtual machine migration Dynamic right sizing Energy saving 

Notes

Acknowledgments

This work was supported by “The Cross-Ministry Giga KOREA Project” of the Ministry of Science, ICT and Future Planning, Korea [GK13P0100, Development of Tele-Experience Service SW Platform based on Giga Media].

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Dong-Ki Kang
    • 1
  • Fawaz Al-Hazemi
    • 1
  • Seong-Hwan Kim
    • 1
  • Min Chen
    • 2
  • Limei Peng
    • 3
  • Chan-Hyun Youn
    • 1
  1. 1.School of Electrical EngineeringKAISTDaejeonSouth Korea
  2. 2.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  3. 3.Department of Industrial EngineeringAjou UniversitySuwonSouth Korea

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