Self-Adapting Scheduling for Tasks with Dependencies in Stochastic Environments

This paper addresses dynamic load balancing algorithms for non-dedicated heterogeneous clusters of workstations. We propose an algorithm called self-adapting scheduling (SAS), targeted at nested loops with dependencies in a stochastic environment. This means that the load entering the system, not belonging to the parallel application under execution, follows an unpredictable pattern which can be modeled by a stochastic process. SAS takes into account the history of previous timing results and the load patterns in order to make accurate load balancing predictions. We study the performance of SAS in comparison with DTSS. We established in previous work that DTSS is the most efficient self-scheduling algorithm for loops with dependencies on heterogeneous clusters. We test our algorithm under the assumption that the interarrival times and life-times of incoming jobs are exponentially distributed. The experimental results show that SAS significantly outperforms DTSS especially with rapidly varying loads