An ACO inspired strategy to improve jobs scheduling in a grid environment

Abstract

Scheduling is one of the most crucial issue in a grid environment because it strongly affects the performance of the whole system. In literature there are several algorithms that try to obtain the best performance possible for the specified requirements; taking into account that the issue of allocating jobs on resources is a combinatorial optimization problem, NP-hard in most cases, several heuristics have been proposed to provide good performance. In this work an algorithm inspired to Ant Colony Optimization theory is proposed: this algorithm, named Aliened Ant Algorithm, is based on a different interpretation of pheromone trails. The goodness of the proposed algorithm, in term of load balancing and average queue waiting time, has been evaluated by mean of a vast campaign of simulations carried out on some real scenarios of a grid infrastructure. © 2008 Springer-Verlag Berlin Heidelberg.