A Multi-Objective Cloud Workflow Scheduling Optimization Based on Evolutionary Multi-objective Algorithm with Decomposition

Abstract In the cloud computing environment, cost-effective workflow task scheduling is the key problem that cloud computing service providers need to solve. However, previous scheduling methods only consider one-sided demands, such as minimizing running time or running cost. In this paper, the cloud workflow scheduling model including two minimizing time and execution cost are established, and then the MOEA/D algorithm based on weight vector adjustment and local search is proposed, and the algorithm is applied in the model solving process. Firstly, the weight vector adjustment method is employed to obtain more evenly distributed solutions; and in order to obtain more evenly distributed solutions and hope to speed up the convergence speed of the solution process, this paper adds local search operators into the solution process of evolutionary algorithm, and proposes MOEA/D algorithm based on local search and weight vector adjustment as an improved multi-objective optimization algorithm to solve the cloud workflow scheduling model based on time and execution cost, it can be turned out that MOEA/D algorithm based on local search and weight vector adjustment can obtain more evenly distributed solutions than MOEA/D algorithm and NSGA-II algorithm on the basis of faster convergence speed, which provides decision support for cloud workflow scheduling decision-makers.