Task scheduling in a cloud computing environment using HGPSO algorithm

Abstract

Cloud computing delivers computing resources like software and hardware as a service to the users through a network. The main idea of cloud computing is to share the tremendous power of storage, computation and information to the scientific applications. In cloud computing, the user tasks are organized and executed with suitable resources to deliver the services effectively. There are plenty of task allocation techniques that are used to accomplish task scheduling. In order to enhance the task scheduling technique, an efficient task scheduling algorithm is proposed in this paper. Optimization techniques are very popular in solving NP-hard problems. In this proposed technique, user tasks are stored in the queue manager. The priority is calculated and suitable resources are allocated for the task if it is a repeated task. New tasks are analyzed and stored in the on-demand queue. The output of the on-demand queue is given to the Hybrid Genetic-Particle Swarm Optimization (HGPSO) algorithm. To implement HGPSO technique, genetic algorithm and particle swarm optimization algorithm are combined and used. HGPSO algorithm evaluates suitable resources for the user tasks which are in the on-demand queue.