• 理论与算法 •

### 模糊云资源调度问题的RIOPSO算法

1. 哈尔滨理工大学 计算机科学与技术学院，哈尔滨 150080
• 出版日期:2021-08-01 发布日期:2021-08-02

### RIOPSO Algorithm for Fuzzy Cloud Resource Scheduling Problem

LI Chengyan, SONG Yue, MA Jintao

1. School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
• Online:2021-08-01 Published:2021-08-02

Abstract:

To solve the cloud resource scheduling problem under time-cost constraints, a triangular fuzzy number is used to represent the uncertain task execution time, and a fuzzy cloud resource scheduling model is established. The objective function of scheduling model is to reduce the total execution time and total cost consumption of the task, and the decision variables are the mapping relationship between tasks and virtual machines. The re-randomization inertia weight orthogonal initialization particle swarm optimization algorithm (RIOPSO) is proposed to solve the fuzzy cloud resource scheduling. This algorithm uses the method of orthogonal initialization particle swarm optimiza-tion to improve the quality of the initial exploration of the optimal scheduling scheme. In the process of particle search, re-randomization is used to control the search range of particles, and real-time updating of inertia weight is used to control the speed of particles, and to obtain the optimal scheduling scheme. The randomly generated simula-tion data on the Cloudsim simulation platform are used to verify the problem model and optimization algorithm proposed in this paper, which proves the reliability of the model. The experimental results show that RIOPSO algorithm can reduce the total execution time and cost in cloud resource scheduling, and it has good performance in convergence speed and solving ability.