A novel architecture for task scheduling based on Dynamic Queues and Particle Swarm Optimization in cloud computing

Abstract

Task scheduling is one of the most challenging aspects in cloud computing nowadays, which plays an important role to improve the overall performance and services of the cloud such as response time, cost, makespan, throughput etc. Mostly a non-optimal task scheduling algorithm can be a key tool in over utilization or under utilization of cloud resources. In order to solve these problems, this paper proposes a novel architecture to schedule the tasks in cloud computing on the basis of a new Dynamic Dispatch Queues Algorithm (DDQA) and Particle Swarm Optimization (PSO) algorithm. The proposed algorithm DDQA-PSO gives full consideration to the dynamic characteristics of the cloud computing environment. The experimental results based on CloudSim simulator show that the proposed architecture can effectively achieve good performance, load balancing, and improve the resource utilization.