An efficient task scheduling in cloud computing based on ACO algorithm

Resource allocation as a NP-hard problem is a very important part of cloud computing and is examined in the form of scheduling algorithms. An Ant Colony Optimization (ACO) algorithm was proposed in this study to improve the load balancing performance and makespan time parameters. Most of the tasks scheduling algorithms have been proposed to improve one of the service quality parameters for service providers or users and do not address the needs of both at the same time. Since an appropriate scheduling algorithm should be able to consider the quality requirements of users and service providers simultaneously, for this purpose in this paper we have proposed a new algorithm for scheduling tasks in cloud environment. The proposed algorithm is based on the ACO algorithm and studied in comparison to a Particle Swarm Optimization (PSO) algorithm, a Genetic Algorithm (GA) and also another research based on ACO. The proposed algorithm has showed the significant improvements concerning the makespan time, load balancing, execution time and resource utilization against the compared algorithms.