Hybrid Gases Brownian Motion and Gravitational Search Algorithm for Resource Scheduling

Abstract

Grid computing provides efficient platform for users to access geographically distributed resources. To meet the quality requirements and timing efficiency constraints imposed by tasks running on Grid, the resources should be allocated efficiently. The plane of recent use is beyond distribution and sharing. The distributed shared resources are useful only if its resources are scheduled efficiently. Grid scheduling is mapping of incoming task to available heterogeneous resources. Here scheduling is NP-hard, henceforth a pile of effort to develop an efficient algorithm is vital to reduce makespan. Metaheuristic algorithms are simplest methods to solve efficiently complex problems. Hybridize metaheuristic algorithm is advanced; trend to improve quality of solution. In this paper Hybrid GSA-PSO (Gravitational Search Algorithm – Particle Swarm Optimization Algorithm); Hybrid GBMO-GSA (Gases Brownian Motion Optimization -Gravitational Search Algorithm) algorithms are amalgamated to provide optimal solution for task scheduling problem in computational grid. Hybrid algorithm implementation shows a significant result in minimizing makespan to meet user requirements comparatively.