Task scheduling on computational grids using Gravitational Search Algorithm

Grid computing uses distributed interconnected computers and resources collectively to achieve higher performance computing and resource sharing. Task scheduling is one of the core steps to efficiently exploit the capabilities of Grid environment. Recently, heuristic algorithms have been successfully applied to solve task scheduling on computational Grids. In this paper, Gravitational Search Algorithm (GSA), as one of the latest population-based metaheuristic algorithms, is used for task scheduling on computational Grids. The proposed method employs GSA to find the best solution with the minimum makespan and flowtime. We evaluate this approach with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) method. The results demonstrate that the benefit of the GSA is its speed of convergence and the capability to obtain feasible schedules.

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