A Study into Cloud Computing Task Scheduling Based on BIAS Algorithm

Abstract

Aiming at the low efficiency of cloud computing resource task scheduling and uneven resource allocation, this paper proposes a cloud computing task scheduling strategy that integrates the Berger model into the improved Ant clony and SFLA-BIAS (Berger-Improve Ant Clony Optimization-Shuffled Frog Leaping Algorithm). Firstly, a cloud computing task scheduling model based on time and cost is constructed; secondly, the general balance function of Berger model is used in combination with the virtual machine for probability selection, and the feedback factor is used to optimize the path. Finally, in each individual iteration of ACO, the improved SFLA is introduced to update the individual. In the simulation experiment, BIAS can effectively improve the efficiency of cloud computing task allocation by comparing with the ACO and SFLA algorithms in the virtual machine load, execution time and consumption cost indicators.