Adaptive Scheduling for Master-Worker Applications on the Computational Grid
We address the problem of how many workers should be allocated for executing a distributed application that follows the master-worker paradigm, and how to assign tasks to workers in order to maximize resource efficiency and minimize application execution time. We propose a simple but effective scheduling strategy that dynamically measures the execution times of tasks and uses this information to dynamically adjust the number of workers to achieve a desirable efficiency, minimizing the impact in loss of speedup. The scheduling strategy has been implemented using an extended version of MW, a runtime library that allows quick and easy development of master-worker computations on a computational grid. We report on an initial set of experiments that we have conducted on a Condor pool using our extended version of MW to evaluate the effectiveness of the scheduling strategy.
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