A hierarchical approach for job scheduling in grid computing based on resource prediction and meta-heuristic algorithms

Abstract

The computational grids as a distributed system are hardware and software infrastructures that are capable of solving large-scale issues, and they use heterogeneous or homogeneous resources scattered around the globe by a high-speed network. Scheduling is a critical and prominent issue in grid computing. An appropriate prediction method for allocating jobs to resources may significantly affect quality of service parameters. In this paper, a hierarchical approach is presented for job scheduling in computational grid utilizing a resource prediction method based on the scoring system. It is inspired by meta-heuristic algorithms in order to improve parameters such as makespan, load balancing and the rate of meeting deadlines. To evaluate the proposed method, GridSim toolkit is exploited. According to the simulation results and comparison with some recent wellknown methods, this approach has been successful in improving the mentioned parameters.