Particle Swarm Ant Colony Optimization Task Scheduling Algorithm In Cloud Computing Environment

Abstract

Cloud computing refers to a collaborative Information Technology environment, which is deliberate with the aim of measurable and remotely spreading scalable IT resources for effective and green utilization. Task scheduling in cloud environment is responsible to assign user tasks effectively to appropriate resources for execution. Task scheduling problem has always been an important issue in cloud environment. In order to improve the task scheduling issue in cloud environment the researcher community proposed different task scheduling algorithm. The existing algorithms are trying to solve task scheduling problem based on different performance metrics but there are some problems are existed not solved by the existing algorithm need to improve the performance of the cloud resource based on different metrics, such as data center processing time, response time, task transfer cost, resource utilization and so on. In this study, we proposed a hybrid task scheduling algorithm based on amalgamation of particle swarm optimization and ant colony optimization to assign the users' tasks to multiple computing resources and balance the entire load of the system. The hybrid algorithm has been evaluated and compared with other meta-heuristic algorithms using CloudAnalyst simulator. The algorithm tested based on the resource information in heterogeneous environment. The result of the study shows that the proposed scheduling algorithm has a better response time and processing time than other basic meta-heuristic algorithm.