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Power-Efficient Immune Clonal Optimization and Dynamic Load Balancing for Low Energy Consumption and High Efficiency in Green Cloud Computing

Zhuqian Long and Wentian Ji
Hainan College of Software Technology, Qionghai 571400, China
Abstract—The energy consumption is considered as key factors of green cloud computing to achieve resource allocation. To address the issue of high energy consumption and low efficiency of cloud computing, this paper proposes a power-efficient immune clonal optimization algorithm (PEICO) based on dynamic load balancing strategy and immune clonal selection theory in green cloud computing. The experimental results show that PEICO performs much better than the clonal selection algorithms and differential evolution in terms of the quality of solution and computational cost.

Index Terms—Green cloud computing, global optimization, energy-efficient task scheduling, power-efficient immune clonal optimization algorithm, immune clonal algorithm

Cite: Zhuqian Long and Wentian Ji, “Power-Efficient Immune Clonal Optimization and Dynamic Load Balancing for Low Energy Consumption and High Efficiency in Green Cloud Computing," Journal of Communications, vol. 11, no. 6, pp. 558-563, 2016. Doi: 10.12720/jcm.11.6.558-563
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