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A Novel Coalitional Game-Theoretic Approach for Energy-Aware Dynamic VM Consolidation in Heterogeneous Cloud Datacenters

  • Xuan Xiao
  • Yunni XiaEmail author
  • Feng Zeng
  • Wanbo ZhengEmail author
  • Xiaoning Sun
  • Qinglan Peng
  • Yu Guo
  • Xin Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11512)

Abstract

Server consolidation technique plays an important role in energy management and load-balancing of cloud computing systems. Dynamic virtual machine (VM) consolidation is a promising consolidation approach in this direction, which aims at using least active physical machines (PMs) through appropriately migrating VMs to reduce resource consumption. The resulting optimization problem is well-acknowledged to be NP-hard optimization problems. In this paper, we propose a novel merge-and-split-based coalitional game-theoretic approach for VM consolidation in heterogeneous clouds. The proposed approach first partitions PMs into different groups based on their load levels, then employs a coalitional-game-based VM consolidation algorithm (CGMS) in choosing members from such groups to form effective coalitions, performs VM migrations among the coalition members to maximize the payoff of every coalition, and close PMs with low energy-efficiency. Experimental results based on multiple cases clearly demonstrate that our proposed approach outperforms traditional ones in terms of energy-saving and level of load fairness.

Keywords

Energy-aware Dynamic VM consolidation Load fairness Merge-split method Coalitional game Heterogeneous clouds 

Notes

Acknowledgment

This work is supported in part by the International Joint Project funded jointly by the Royal Society of the UK and the National Natural Science Foundation of China under grant 61611130209, National Science Foundations of China under grants Nos.61472051/61702060, the Science Foundation of Chongqing under No. cstc2017jcyjA1276, China Postdoctoral Science Foundation No. 2015m570770, Chongqing Postdoctoral Science special Foundation No. Xm2015078, and Universities’ Sci-tech Achievements Transformation Project of Chongqing No. KJZH17104, Chongqing grand R&D projects Nos. cstc2017zdcy-zdyf0120 and cstc2017rgzn-zdyf0118. Yunni Xia and Wanbo Zheng are the corresponding authors of this work.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Software Theory and Technology Chongqing Key LabChongqing UniversityChongqingChina
  2. 2.Data Science Research Center, Faculty of ScienceKunming University of Science and TechnologyKunmingChina
  3. 3.School of Public AdministrationSichuan UniversityChengduChina
  4. 4.Discovery Technology (shenzhen) limitedShenzhenChina
  5. 5.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqingChina

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