A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II

Scheduling a job on the grid is an NP Hard problem, and hence a number of models on optimizing one or other characteristic parameters have been proposed in the literature. It is expected from a computational grid to complete the job quickly in most reliable grid environment owing to the number of participants in the grid and the scarcity of the resources available. Genetic algorithm is an effective tool in solving problems that requires sub-optimal solutions and finds uses in multi-objective optimization problems. This paper addresses a multi-objective optimization problem by introducing a scheduling model for a modular job on a computational grid with a dual objective, minimizing the turnaround time and maximizing the reliability of the job execution using NSGA – II, a GA variant. The cost of execution on a node is measured on the basis of the node characteristics, the job attributes and the network properties. Simulation study and a comparison of the results with other similar models reveal the effectiveness of the model.