Energy and Migration Cost-Aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacenters

• Published in: IEEE transactions on services computing Vol. 12; no. 4; pp. 550 - 563
• Main Authors: Wu, Quanwang, Ishikawa, Fuyuki, Zhu, Qingsheng, Xia, Yunni
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloud computing; Consolidation; Data centers; energy; Energy conservation; Energy consumption; Energy efficiency; Estimation; Genetic algorithms; grouping genetic algorithm; heterogeneity; Heuristic algorithms; Migration; migration cost; Optimization; Power efficiency; Upper bounds; Virtual environments; Virtual machine consolidation; Virtual machining
• ISSN: 1939-1374; 1939-1374; 2372-0204
• DOI: 10.1109/TSC.2016.2616868

Energy efficiency has become one of the major concerns for today's cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited migration costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a migration cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.