A Randomization Approach for Stochastic Workflow Scheduling in Clouds

In cloud systems consisting of heterogeneous distributed resources, scheduling plays a key role to obtain good performance when complex applications are run. However, there is unavoidable error in predicting individual task execution times and data transmission times. When this error is being not negligible, deterministic scheduling approaches (i.e., scheduling based on accurate time prediction) may suffer. In this paper, we assume the error in time predictions is modelled in stochastic manner, and a novel randomization approach making use of the properties of random variables is proposed to improve deterministic scheduling. The randomization approach is applied to a classic deterministic scheduling heuristic, but its applicability is not limited to this one heuristic. Evaluation results obtained from extensive simulation show that the randomized scheduling approach can significantly outperform its static counterpart and the extra overhead introduced is not only controllable but also acceptable.