Hybrid Ant Particle Swarm Genetic Algorithm (APSGA) for Task Scheduling in Cloud Computing

Abstract

AbstractIn cloud computing technology, task scheduling is one of the research challenges. For these various algorithms, works such as particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO) and genetic algorithm (GA). PSO is inspired by the bird’s movement, and ACO is based on the behaviour of ants. GA works based on the natural evolution process. This paper presents the hybrid of PSO-ACO-GA for task scheduling on virtual machines of cloud computing known as ant particle swarm genetic algorithm (APSGA). Here, GA and PSO will perform iteration to get the task basis on fitness value and further ACO will distribute the task on specific virtual machines. This paper has achieved improved results for parameters such as CPU utilization, makespan and execution time. Our proposed algorithm has achieved makespan that is reduced by 27.1%, 19.45% and 21.24% with compare to PSO, ACO and GA, respectively. It has achieved maximum of CPU utilization and execution time.KeywordsTask schedulingCloud computingMakespanNature-inspired algorithmsResource utilizationSimulationCloudSim