Research Of Task Scheduling For Cloud Computing Based On Multi-objective Optimization

As the most widely used commercial distributed computing technology nowadays, cloud computing has been supported by many IT companies. The physical resources in the cloud data center are integrated into a resource pool by using virtualization technology and users can pay for the cloud computing service. However, the traditional task scheduling under single-objective optimization can’t meet the diverse needs faced by cloud computing with its continuous development, therefore, the study of cloud computing task scheduling based on multi-objective optimization has the vital significance.In this paper, a hybrid cloud computing task model is proposed by analyzing its feature, and multiple optimization objectives are selected to optimize its scheduling process. By doing these, a cloud computing task scheduling model based on multi-objective optimization is established. The main works in this paper are as follows.(1) The basic concepts, architecture and technical features of cloud computing are introduced, and a hybrid cloud computing task model containing independent tasks and workflow tasks is proposed. Also, some basic concepts of multi-objective optimization are introduced.(2) Three optimization objectives are selected. The time consumption and the financial cost of processing the tasks submitted by cloud computing users are two of them, and the third objective is the load balancing of the cloud computing tasks on the virtual machines in the cloud data center. A cloud computing task scheduling model based on multi-objective optimization is established with the hybrid cloud computing task model and the three optimization objectives. In this process, the objective function is established, and the encoding describing the mapping relationships between cloud tasks and virtual machines is designed.(3) An improved NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm-Ⅱ) is applied to the cloud computing task scheduling model based on multi-objective optimization. In this process, the STOX (Similar Task Order Crossover) operator is applied to make the evolution more efficient while the LBSM (Load Balancing Shift Mutation) operator is applied to avoid the premature convergence and optimize the objective of load balancing at the same time. In addition, the self-adapting crowding distance operator is presented to improve the diversity of individuals in the Pareto-optimal front.(4) An improved NPGA (Niched Pareto Genetic Algorithm) is applied to the cloud computing task scheduling model based on multi-objective optimization. In this process, the STOX operator and the LBSM operator are also used to improve the algorithm. In addition, the self-adapting size of comparison set operator and the self-adapting niche radius operator make the evolution more efficient and improve the diversity of individuals in the Pareto-optimal front respectively.(5) Study the open source cloud computing simulation tool CloudSim and simulate the improved NSGA-Ⅱ with NSGA-Ⅱ and improved NPGA with NPGA respectively when they are applied to the cloud computing task scheduling model based on multi-objective optimization. The simulation results show that the improved algorithms perform better in the aspects of maintaining the diversity and the distribution of the Pareto-optimal individuals. In addition, the cloud computing task scheduling can get better solution corresponding to the individual in the Pareto-optimal front of the improved algorithms.