Int J Performability Eng ›› 2021, Vol. 17 ›› Issue (1): 85-94.doi: 10.23940/ijpe.21.01.p8.8594

• Orginal Article • Previous Articles     Next Articles

Cloud Computing Task Scheduling based on Improved Bird Swarm Algorithm

Xiaoxiang Fan*   

  1. Hefei Normal University, Heifei, 230601, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * Corresponding author. E-mail address:
  • About author:
    Xiaoxiang Fan is a lecturer at Hefei Normal University. She received her master's degree from Anhui University. Her research interests include game theory and its applications.
  • Supported by:
    the Higher School Natural Science Project of Anhui Province (NoKJ2020A0089), Special Project of Scientific Research Platform of Anhui Province, and Higher School Humanities and Social Sciences Project of Anhui Province (NoSK2020A0110)


An improved bird swarm algorithm (IBSA)-based task scheduling strategy was proposed to solve the problem of improved task scheduling and high equipment energy consumption in the cloud computing environment. Bernouilli shift chaotic mapping was used to initialize the population and improve its diversity. Levy flight feature was used to update the individual position to avoid the algorithm falling into local optimization. The bee colony algorithm was used to select the optimal individual for the next iteration. Simulation results showed that the time and energy consumption of the proposed algorithm were optimized under four different tasks. Compared with the ant colony algorithm, particle swarm algorithm, and IBSA, the algorithm presented in this paper has obvious advantages in scheduling effect and can effectively save time and reduce energy consumption.

Key words: cloud computing, bird swarm algorithm, chaotic mapping, levy flight, artificial bee colony