An Adaptive Multisite Mapping for Computationally Intensive Grid Applications

The unemployed computational resources, usually available on the multi-owner time-shared computing nodes of a multisite grid, could be fruitfully exploited to execute the parallel tasks of computationally challenging applications. An efficient use of such resources requires an optimal task/node mapping which, already known as NP-complete on classical parallel computers, becomes much harder on grid systems where additional degrees of complexity are introduced. Since classical mapping algorithms result inadequate in such an environment, heuristic techniques turn out to be adopted to find near-optimal solutions. In this paper a software tool, based on a multiobjective differential evolution algorithm, is tested on some artificial mapping problems differing in applications and grid working conditions. The aim is to fulfill several optimization criteria such as optimization in the use time of grid resources achieving the minimization of application execution while, contemporaneously, complying with Quality of Service requirements. The findings obtained show the ability of the evolutionary approach proposed to cope with such a multisite grid mapping, i.e. a deployment not constrained to select nodes from one single site.