Intelligent Task Scheduling Framework for Cloud Platforms Based on Improved Particle Swarm Optimization

Abstract

This paper analyzes the characteristics and goals of cloud platform task schedul- ing. Starting from task scheduling algorithms, it proposes an artificial intelligence method based on an improved particle swarm optimization (PSO) algorithm for the electric power scheduling automation system, and develops a cloud computing operation model. Based on this algorithm and the physical model, the control oper- ation takes into account the QoS requirements and the environmental load balance of the cloud platform residents, which can effectively improve the task scheduling efficiency of the cloud platform in the power scheduling automation system. Tak- ing the power automation cloud platform as the research object, the architecture is studied and the PSO algorithm is modified and combined with the structure of cloud resource scheduling model. A three-level data node system is established, and a cloud platform scheduling model based on the improved PSO algorithm is proposed to improve cloud resource allocation efficiency and service quality, solving the task scheduling problem of power scheduling automation systems.