Abstract In the research on cloud workflow systems, the time efficiency optimization of the task execution is the emphasis. The energy consumption optimization of the task execution is often ignored. However, time-optimal task scheduling plans have different energy consumption. Therefore, how to solve energy-optimal task scheduling plans with time constraint are discussed in this paper. Firstly, the energy model of task execution is improved. Then, the fitness computation method of the task plan is designed to evaluate energy consumption. Finally, an adaptive inertia weight computation method is applied to adjust particle velocity accurately and a particle swarm optimization (PSO) algorithm is presented to solve the energy consumption optimization problem of task scheduling in cloud workflow systems. Experimental results show that the proposed algorithm has a stable convergence speed with low energy consumption.
About author:: LI Xuejun(Corresponding author), born in 1976. Ph.D., associate professor. His research interests include cloud computing and intelligent software.XU Jia, born in 1992, master student. His research interests include workflow systems.WANG Futian, born in 1981, Ph.D. candidate, lecturer. His research interests include distributed computing.ZHU Erzhou, born in 1981. Ph.D., associate professor. His research interests include virtualization and cloud computing.WU Lei, born in 1978, Ph.D. candidate, lecturer. Her research interests include software testing.)
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2016, Vol. 29 
