Scheduling Divisible Workloads Using the Adaptive Time Factoring Algorithm

In the past years a vast amount of work has been done in order to improve the basic scheduling algorithms for master/slave computations. One of the main results from this is that the workload of the tasks may be adapted during the execution, using either a fixed increment or decrement (e.g. based on an arithmetical or geometrical ratio) or a more sophisticated function to adapt the workload. Currently, the most efficient solutions are all based on some kind of evaluation of the slaves’ capacities done exclusively by the master. We propose in this paper the Adaptive Time Factoring scheduling algorithm, which uses a different approach distributing the scheduling between slaves and master. The master computes, using the Factoring algorithm, a time slice to be used by each slave for processing, and the slave predicts the correct workload size it should receive in order to accomplish this time slice. The prediction is based on a performance model located on each slave which is refined during the execution of the application in order to provide better predictions. We evaluated the proposed algorithm using a synthetic testbed and compared the obtained results with other scheduling algorithms.