Genetic-Based Solutions For Independent Batch Scheduling In Data Grids

Scheduling in traditional distributed systems has been mainly studied for system performance parameters without data transmission requirements. With the emergence of Data Grids (DGs) and Data Centers, data-aware scheduling has become a major research issue. In this work we present two implementations of classical genetic-based data-aware schedulers of independent tasks submitted to the grid environment. The results of a simple empirical analysis confirm the high effectiveness of the genetic algorithms in solving very complex data intensive combinatorial optimization problems.

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