Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (8): 2081-2090.doi: 10.23940/ijpe.19.08.p7.20812090

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Scheduling Algorithm for a Task under Cloud Computing

Yan Lia,* and Yao Yaob   

  1. a Henan Technical College of Construction, Zhengzhou, 450064, China
    b Zhengzhou Institute of Technology, Zhengzhou, 450044, China
  • Received:2019-03-18 Online:2019-08-20 Published:2019-09-10
  • Contact: * E-mail address: lalei1984@aliyun.com
  • About author:Li Yan is currently a lecturer in the Department of Construction Information Engineering at Henan Technical College of Construction. She received her master's degree in computer software and theory from the Department of Information Engineering at Zhengzhou University in 2008. Her research interests include cloud computing and data mining. Yao Yao is currently an associate professor in the School of Information Engineering at Zhengzhou Institute of Technology. She received her master's degree in computer applications from the Department of Information Engineering at the University of Zhengzhou in 2008. Her research interests include high-performance computing and web mining.
  • Supported by:
    This work is supported by the Henan Higher Education School Young Backbone Teacher Training Program (No. 2016ggjs-201).

Abstract: Aiming at the problem of low efficiency of task scheduling in cloud computing, this paper first analyzes the task of cloud computing task scheduling. Secondly, the pheromone setting quality function of the ant colony algorithm and the empirical feedback factor of selection probability are improved. The new method is adopted for the colonial calculation method and boundary value processing in the imperial competition algorithm. Finally, the two algorithms are merged to obtain a cloud computing task scheduling algorithm based on the ant colony algorithm-empire competition algorithm. In the simulation experiment, the algorithm demonstrates certain advantages in terms of task execution time, execution cost, and load rate.

Key words: cloud computing, task scheduling, ant colony algorithm, imperialist competitive algorithm