CSO-GA: A New Scheduling Technique for Cloud Computing Systems Based on Cat Swarm Optimization and Genetic Algorithm

Shahram ROUHI, Ebrahim Behrouzian NEJAD
2.067 646


Abstract. With the advent of cloud computing technology, one of the important issues that had been raised in this debate is Task scheduling techniques. The main challenge in scheduling is to reduce Makespan, which is the required time to finish all the running tasks. Scheduling is a NP-hard problem and with a given number of jobs and the machines, it is difficult to find a definitive solution to this problem. Many researchers have tried to solve this crucial problem in cloud computing. This article tries to presents a new meta heuristic scheduling technique Using a combination of Cat Swarm Optimization (CSO) and Genetic Algorithm (GA). Using a combination of CSO and GA, a new hybrid optimization algorithm is introduced, which is called CSO-GA. Evaluation and simulation results show that this proposed scheduling technique is able to reduce Makespan in comparison with other techniques.


 Scheduling, Makespan, Meta heuristic, Cat Swarm Optimization (CSO), Genetic Algorithm (GA)

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