Multi-objective Optimization Approaches Using a CE-ACO Inspired Strategy to Improve Grid Jobs Scheduling

Grid scheduling is one of the most crucial issue in a grid environment because it strongly affects the performance of the whole system. Taking into account that the issue of allocating jobs on resources is a combinatorial optimization problem, a NP-complete problem, several heuristics have been proposed to provide good performance. In this paper, the proposed approach considers a stochastic optimization called the cross entropy method. The CE method is used to tackle efficiently the initialization sensitiveness problem associated with ant colony algorithm for multi-objective scheduling, which accelerates the convergence rate and improves the ability of searching an optimum solution. Simulation shows that it performs better than the ACO in the integrated performances.