Task Scheduling based on Fruit Fly Optimization Algorithm in Mobile Cloud Computing

doi:10.23940/ijpe.20.04.p13.618628

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (4): 618-628.doi: 10.23940/ijpe.20.04.p13.618628

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Task Scheduling based on Fruit Fly Optimization Algorithm in Mobile Cloud Computing

Xuan Chen^*, Zhengjiang Song, Hongfeng Zheng, and Zhiping Wan

Zhejiang Industry Polytechnic College, Shaoxing, 312000, China

Submitted on ; Revised on ; Accepted on
Contact: Chen Xuan
About author:Xuan Chen is an Associate Professor at Zhejiang Industry Polytechnic College, Shaoxing, China. He received his master's degree from the University of Electronic Science and Technology of China. His research interests include cloud computing and algorithm research.
Zhenjiang Song is an Associate Professor at Zhejiang Industry Polytechnic College, Shaoxing, China. He received his master's degree from Zhejiang University of Technology. His research interests include cloud computing and algorithm research.
Hongfeng Zheng is a Professor at Zhejiang Industry Polytechnic College, Shaoxing, China. He received his master's degree from Huazhong University of Science and Technology. His research interests include power electronics and applications, intelligent control, and algorithm research.
ZhiPing Wan is an Associate Professor at Zhejiang Industry Polytechnic College, Shaoxing, China. He received his master's degree from the Fudan University. His research interests include electronic engineering, control engineering and algorithm research.
Supported by:
This work is supported by Public welfare technology research project of science and technology department of Zhejiang Province, Research on Key Technologies for Power Lithium-ion Battery Second Use in Energy Storage System (LGG19E070006).

Abstract

Abstract:

To solve the problems of time consuming and high energy consumption of task scheduling in mobile cloud computing environment, a task scheduling strategy based on fruit fly optimization algorithm was proposed. First, establish a mobile cloud computing task scheduling model; second, in the fruit fly optimization algorithm, orthogonal arrays and quantization techniques are used to initialize the population. The exploration step is used to dynamically adjust to avoid individuals falling into a local optimum. Finally, in each iteration of the fruit fly optimization algorithm, a global search update is performed by introducing an artificial bee colony algorithm. In the simulation experiments, compared with the basic fruit fly optimization algorithm, the improved particle swarm algorithm, and the improved artificial bee colony algorithm, the algorithm in this paper has certain advantages in the comparison of the four indicators: completion time, cost, bandwidth and energy consumption. Besides, this algorithm can effectively improve the task scheduling efficiency under mobile cloud computing.

Key words: fruit fly optimization algorithm, artificial bee colony, mobile cloud computing, task scheduling

Xuan Chen, Zhengjiang Song, Hongfeng Zheng, and Zhiping Wan. Task Scheduling based on Fruit Fly Optimization Algorithm in Mobile Cloud Computing [J]. Int J Performability Eng, 2020, 16(4): 618-628.

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Task Scheduling based on Fruit Fly Optimization Algorithm in Mobile Cloud Computing

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