A Distributed Bio-Inspired Method for Multisite Grid Mapping

Computational grids assemble multisite and multiowner resources and represent the most promising solutions for processingdistributed computationally intensive applications, each composed by a collection of communicating tasks. The execution of anapplication on a grid presumes three successive steps: the localization of the available resources together with their characteristicsand status; the mapping which selects the resources that, during the estimated running time, better support this execution and, atlast, the scheduling of the tasks. These operations are very di fficult both because the availability and workload of grid resourceschange dynamically and because, in many cases, multisite mapping must be adopted to exploit all the possible benefits. Asthe mapping problem in parallel systems, already known as NP-co mplete, becomes even harder in distributed heterogeneousenvironments as in grids, evolutionary techniques can be adopted to find near-optimal solutions. In this paper an e ffective andefficient multisite mapping, based on a distributed Differential Evolution algorithm, is proposed. The aim is to minimize the timerequired to complete the execution of the application, selecting from among all the potential ones the solution which reduces theuse of the grid resources. The proposed mapper is tested on di fferent scenarios.