Cloud Computing Task Scheduling based on Improved Bird Swarm Algorithm

doi:10.23940/ijpe.21.01.p8.8594

Abstract

Abstract:

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

Xiaoxiang Fan. Cloud Computing Task Scheduling based on Improved Bird Swarm Algorithm [J]. Int J Performability Eng, 2021, 17(1): 85-94.

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