Multi-objective Task Scheduling Optimization in Cloud Computing based on Genetic Algorithm and Differential Evolution Algorithm

Abstract

Reasonable task scheduling is a long-standing challenge in cloud computing. Scheduling process of cloud computing has the characteristics of dynamic nature, meanwhile the constraint of the target function from a single aspect cannot meet the needs of users. According to the above problem, a multi-objective task scheduling GA-DE algorithm based on Genetic Algorithm (GA) and Differential Evolution (DE) is proposed in this paper, in which total time, cost and virtual machine load balancing three aspects are taken into account simultaneously. In the phase of population initialization and crossover, diversity of the initial population is increased by introducing individual different factors, which can prevent cross-operation of similar individuals and satisfy laws of inbreeding of natural relatives. The introduction of the DE in the GA mutation stage can not only give full play to the advantages of the global search ability of GA but also accelerate the algorithm produce optimal solution by utilizing advantage of local search ability and fast convergence speed of DE. The algorithm proposed in this paper is compared with GA and DE by cloud computing simulation experiments on CloudSim platform. Experimental result shows that this algorithm can optimize both GA and DE in terms of quality of service and virtual machine load balancing under the same conditions, which is proved to be an efficient task scheduling algorithm in cloud computing environment.