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A Parallel Task Scheduling Optimization Algorithm Based on Clonal Operator in Green Cloud Computing

Yang Liu 1, Wanneng Shu 2, and Chrish Zhang 3
1. College of Information Science and Engineering, Hunan City University, Yiyang 413000, China
2. College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
3. Lawrence Berkeley National Laboratory, University of California, USA
Abstract—Computing resources in cloud computing with heterogeneous, dynamic, non-balancing and other features, how to allocate resources to fully improve resource utilization and reduce task execution time and energy consumption optimization is the key problem to be faced in cloud computing. According to the basic characteristics of task scheduling in cloud computing environment, this paper proposes an energy consumption optimization model for task scheduling, and proposes a green clonal scheduling optimization algorithm by taking advantage of the clonal operator of immune algorithm. Experimental results show that the proposed algorithm can not only effectively reduce the execution time and energy consumption, and can achieve resource load balancing, thus effectively improve the resource utilization and scheduling efficiency.

Index Terms—Green cloud computing, clonal operator, task scheduling, computing resources, energy consumption optimization

Cite: Yang Liu, Wanneng Shu, and Chrish Zhang, “A Parallel Task Scheduling Optimization Algorithm Based on Clonal Operator in Green Cloud Computing," Journal of Communications, vol. 11, no. 2, pp.185-191, 2016. Doi: 10.12720/jcm.11.2.185-191
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