Evolutionary based hybrid GA for solving multi-objective grid scheduling problem

The grid computing aims at bringing computing capacities together in a manner that can be used to find solutions for complicated problems of science. Conventional algorithms like first come first serve (FCFS), shortest job first (SJF) has been used for solving grid scheduling problem (GSP), but the increased complexity and job size led to the poor performance of these algorithms especially in the grid environment due to its dynamic nature. Previously, researchers have used a genetic algorithm (GA) to schedule jobs in the grid environment. In this paper, a multi-objective GSP is solved and optimized using the proposed algorithm. The proposed algorithm enhances the way the genetic algorithm performs and incorporate significant changes in the initialization step of the algorithm. The proposed algorithm uses SJF during its initialization step for producing the initial population solution. The proposed GA has three key features which are discussed in this paper: It executes jobs with minimum job completion time. It performs load balancing and improves resource utilization. Lastly, it supports scalability. The proposed algorithm is tested using a standard workload (given by Czech National Grid Infrastructure named Metacentrum) which can be a benchmark for further research. A performance comparison shows that the proposed algorithm has got better scheduling results than other scheduling algorithms.