Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing

Abstract

Task scheduling is one of the most critical issues on cloud platform. The number of users is huge and data volume is tremendous. Requests of asset sharing and reuse become more and more imperative. Efficient task scheduling mechanism should meet users' requirements and improve the resource utilization, so as to enhance the overall performance of the cloud computing environment. In order to solve this problem, considering the new characteristics of cloud computing and original adaptive genetic algorithm(AGA), a new scheduling algorithm based on double-fitness adaptive algorithm-job spanning time and load balancing genetic algorithm(JLGA) is established. This strategy not only works out a tasks scheduling sequence with shorter job and average job makespan, but also satisfies inter-nodes load balancing. At the same time, this paper adopts greedy algorithm to initialize the population, brings in variance to describe the load intensive among nodes, weights multi-fitness function. We then compare the performance of JLGA with AGA through simulations. It proves the validity of the scheduling algorithm and the effectiveness of the optimization method.