Task assignment in grid computing, where both processing and bandwidth constraints at multiple heterogeneous devices need\r\nto be considered, is a challenging problem. Moreover, targeting the optimization of multiple objectives makes it even more challenging.\r\nThis paper presents a task assignment strategy based on genetic algorithms in whichmultiple and conflicting objectives are\r\nsimultaneously optimized. Specifically, we maximize task execution quality while minimizing energy and bandwidth consumption.\r\nMoreover, in our video processing scenario; we consider transcoding to lower spatial/temporal resolutions to tradeoff between\r\nvideo quality; processing, and bandwidth demands. The task execution quality is then determined by the number of successfully\r\nprocessed streams and the spatial-temporal resolution at which they are processed. The results show that the proposed algorithm\r\noffers a range of Pareto optimal solutions that outperforms all other reference strategies.
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