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Applied Computational Intelligence and Soft Computing
Volume 2010 (2010), Article ID 505194, 10 pages
Research Article

A Distributed Bio-Inspired Method for Multisite Grid Mapping

1Institute of High Performance Computing and Networking, National Research Council of Italy, Via P. Castellino 111, 80131 Naples, Italy
2Natural Computation Laboratory, DIIIE, University of Salerno, Via Ponte don Melillo 1, 84084 Fisciano (SA), Italy

Received 31 July 2009; Revised 8 January 2010; Accepted 20 March 2010

Academic Editor: Chuan-Kang Ting

Copyright © 2010 I. De Falco et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Computational grids assemble multisite and multiowner resources and represent the most promising solutions for processing distributed computationally intensive applications, each composed by a collection of communicating tasks. The execution of an application on a grid presumes three successive steps: the localization of the available resources together with their characteristics and status; the mapping which selects the resources that, during the estimated running time, better support this execution and, at last, the scheduling of the tasks. These operations are very difficult both because the availability and workload of grid resources change dynamically and because, in many cases, multisite mapping must be adopted to exploit all the possible benefits. As the mapping problem in parallel systems, already known as NP-complete, becomes even harder in distributed heterogeneous environments as in grids, evolutionary techniques can be adopted to find near-optimal solutions. In this paper an effective and efficient multisite mapping, based on a distributed Differential Evolution algorithm, is proposed. The aim is to minimize the time required to complete the execution of the application, selecting from among all the potential ones the solution which reduces the use of the grid resources. The proposed mapper is tested on different scenarios.