In this paper we propose an adaptive scheduling approach designed to improve the performance of parallel applications in Computational Grid environments. A primary contribution of our work is that our design is modular and provides a separation of the scheduler itself from the application-specific components needed for the scheduling process. As part of the scheduler, we have also developed a search procedure which effectively and efficiently identifies desirable schedules. As test cases for our approach, we selected two applications from the class of iterative, mesh-based applications. For each of the test applications, we developed data mappers and performance models. We used a prototype of our approach in conjunction with these application-specific components to perform validation experiments in production Grid environments. Our results show that our scheduler provides significantly better application performance than conventional scheduling strategies. We also show that our scheduler gracefully handles degraded levels of availability of application and Grid resource information. Finally, we demonstrate that the overheads introduced by our methodology are reasonable. This work evolved in the context of the Grid Application Development Software Project (GrADS). Our scheduling approach is designed to be easily integrated with other GrADS program development tools.
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