A Niched Pareto GA approach for scheduling scientific workflows in wireless grids

We present a Niched Pareto Genetic Algorithm (NPGA) approach to the scheduling of scientific workflows in a wireless grid environment that connects computational resources, wired grids and wireless device resources such as cameras, microphones, network interfaces and so forth where the maximization of job completion ratio and minimization of lateness is crucial. Our approach supports handling uncertainty in the field of decision analysis, a rigorous technique for combining multiple objectives simultaneously. We made comparisons of our approach with respect to other scheduling policies; it performed significantly better than the majority of the cases, and in the worst cases, it was as good as the best of the others.