Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (7): 1965-1975.

Task Scheduling of an Improved Cuckoo Search Algorithm in Cloud Computing

Wenli Liu*, Cuiping Shi, Hongbo Yu, and Hanxiong Fang

1. Qiqihar University, Qiqihar, 161006, China
• Submitted on  ;
• Contact: * E-mail address: Qqhrlwl1982@sina.com
• About author:Wenli Liu is a lecturer at Qiqihar University. She received her master's degree from North University of China. Her research interests include cloud computing and signal and information.Cuiping Shi is an associate professor at Qiqihar University. She received her doctorate degree from Harbin Institute of Technology. Her research interests include cloud computing and signal and information.Hongbo Yu is an associate professor at Qiqihar University. He received his master's degree from Xi'an Polytechnic University. His research interests include cloud computing.Hanxiong Fang is a lecturer at Qiqihar University. She received her master's degree from Qiqihar University. Her research interests include cloud computing.
• Supported by:
This work is supported by the National Natural Science Foundation of China Youth Fund (No. 41701479), Heilongjiang Science Foundation Project (No. QC2018045), and Qiqihar University Project (No. 2014K-M31).

Abstract: In view of the low efficiency of task scheduling in cloud computing, this paper introduces the cuckoo algorithm to optimize task scheduling. Firstly, the cloud computing task scheduling model is established. Secondly, the particle swarm algorithm and quantum algorithm are introduced for the short search ability of the cuckoo algorithm and the low precision of optimization. The cuckoo is fixed as a "particle" in the search direction in three-dimensional space, so that it cannot be randomly offset. Through the binary algorithm, the particle can be made faster by having the Levy flight randomly generate the step size. The optimal solution direction moves, which speeds up the convergence speed of the algorithm and avoids the blindness in the search process. By using four classical benchmark functions, the simulation results show that the improved algorithm has better performance and improves the efficiency of task scheduling and scheduling under cloud computing.