Optimized Grid Job Scheduling Using Hybrid SLFA-GA for Homogeneous Task

The grid computing is defined as using a set of resources from multiple places to reach a collectivecomputation goal. As the grid environments facilitate distributed computation, the grid job scheduling has become animportant issue in computing. A network planning system is implemented using efficient algorithms to allocate networkresources to the user s program, with the limitations required in optimization strategy and accordance with userrequest. In this research, the proposed method takes the advantages of both genetic algorithm and shuffled frog-leapingalgorithm (SFLA) to find a cost-efficient solution for the resource allocation problem. The main contribution of thisresearch is the enhancement of SFLA algorithm performance for job scheduling by adding the some features of GA intoits initial population creation. Meanwhile, a comparison between the performance of GA, SFLA and the proposedmodified SFLA is presented to schedule affiliated homogeneous tasks in the workflow model with aim to reduce thetasks finishing time. The simulation results that has been implemented in the MATLAB environment clearly show theability of our proposed algorithms in job scheduling problem.

کلیدواژه ها:

Optimization Hybrid Algorithms ، Schedule Computational Grid ، Genetic Algorithm ، Shuffled Frog Leaping Algorithm ، Resource Allocation ، Homogeneous Task