DOI 10.1007/s11227-014-1289-4 Distributed optimization Grid resource discovery

Abstract Grid computing is a framework for large-scale resource sharing and index-ing that evolves with the goal of resource provisioning. In this paper, we develop a distributed learning automata (DLA) based on multi-swarm discrete particle swarm optimization (PSO) approach for Grid resource discovery, called distributed optimiza-tion grid (DOG) resource discovery algorithm. This algorithm makes use of swarms of particles for different computational resource metrics while a group of DLA is the control unit of each swarm of particles. The algorithm takes advantage of the PSO solution diversity to optimize the quality of delivered resource. Moreover, the rec-ommended algorithm uses DLA as a fully distributed model for imitating the Grid infrastructure topology. Our experimental results show that DOG is fast as well as efficient and accurate