To achieve improved performance, application schedulers are typically designed to satisfy the resource requirements of specific applications. Consequently, application characteristics and models are often embedded in the scheduler itself. Results have shown that this strategy is effective for achieving improved application performance. However, application-specific schedulers may not be easily retargeted for other applications. In this thesis, we propose a modular application scheduler design that employs detailed application performance models and mapping strategies that promote application performance, but does not embed such components within the scheduler itself. Our scheduler is both environment-sensitive and configurable. To ensure that schedules are properly targeted for conditions of the target execution environment at run-time, the scheduler can incorporate dynamic resource availability in scheduling decisions. The scheduler also supports a set of configurable scheduling policies that are easily tuned to control scheduler behavior. We implement a prototype scheduler and use the class of iterative, mesh-based applications to test the prototype. We implement two test applications, Jacobi and the Game of Life, and develop performance models and mapping strategies for each application. We present experimental results we obtained by applying our scheduling methodology to Jacobi and the Game of Life in Computational Grid environments. Our testbeds included up to 20 machines organized in 4 clusters at 3 geographically distributed sites. In these experiments, our approach consistently outperforms conventional scheduling approaches.
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