Resource Deployment and Task Scheduling Based on Cloud Computing

Abstract

Cloud computing is a computing model developed from parallel computing and distributed computing. The rapid development of cloud computing technology not only expands the scale of data in the database, but also consumes a lot of resources for data processing, resulting in huge energy costs. In addition, scheduling policies that do not conform to the actual situation will cause uneven task distribution, cause serious waste of resources, and increase cloud data management operating costs. In cloud computing related research, resource deployment and task scheduling have a great impact on the overall performance of the system. In order to solve the above problems, more and more scholars have conducted in-depth research on this problem. Reduce the energy consumption (EC) of cloud data processing, improve data processing efficiency, propose cloud computing architecture, build resource deployment (RD) model and task scheduling (TS) model on this basis. The usefulness of the model is discussed in depth. Aiming at low EC and high-efficiency resource allocation tasks, a TS algorithm based on improved particle swarm optimization (PSO) algorithm is proposed to further improve the performance of cloud computing systems. The experimental results show that the resource deployment and task scheduling model constructed in this paper can consume bad particles, maximize resource utilization, and reduce the EC of cloud computing (CC) systems after being optimized by particle swarm optimization. Compared with the traditional PSO algorithm, the improved PSO algorithm in this paper can effectively avoid the problem of user query resource allocation lag, improve the task execution efficiency, and enhance the stability of the CC system.