Multi Objective Scheduling in Cloud Computing Using MOSSO

Nowadays, cloud computing and big data are changing the enterprise. Cloud computing, as a new business computing mode, distributes computing tasks across resource pools made up of a large number of computers for large-scale calculation. In the current research on the task assignment problem of cloud computing, most scholars consider single-objective programming, for example minimizing the cost or makespan. However, many other factors can influence the quality of the cloud computing service. Therefore, in order to adapt to the development of practical applications, multi-objective programming should be considered in the task scheduling problem of cloud computing. This paper developed a new algorithm (Multi-Objective Simplified Swarm Optimization, MOSSO) for multi-objective problems, based on the Multi-Objective Particle Swarm Optimization (MOPSO), using the simple and efficient update mechanism of a heuristic algorithm called Simplified Swarm Optimization (SSO). In order to increase the search ability of feasible solution space in this algorithm, this paper designs dynamitic parameters to make the mutation rate large at the early stage to enhance global search ability and the mutation rate small at late stage to enhance local search ability.