Stochastic vs. deterministic evolutionary algorithm-based allocation and scheduling for XMOS chips

We present an approach based on multi-objective evolutionary algorithms for the automatic scheduling and allocation of tasks in a multiprocessor multithreaded architecture, together with an assignment of the appropriate voltage and frequency of each processor in a way the overall energy consumed by the execution of the tasks is optimized and all task deadlines are met. We have implemented both a deterministic scheduling algorithm, where the execution time and the energy consumption of different tasks have a known deterministic value, and a stochastic scheduling algorithm, where the execution time and energy are treated as random variables with corresponding probability density functions, given that in reality these values can vary significantly due to numerous reasons. It is assumed that execution time and energy consumption estimations, both for the deterministic and the stochastic case, are obtained by a static analysis process. It has already been proven for the case of makespan optimization that the stochastic scheduling is underestimated by its deterministic counterpart, and that in many real world situations, the stochastic scheduler outperforms the deterministic one. In this work we prove that for the tested scenario the stochastic scheduler for energy optimization outperforms its deterministic counterpart improving energy consumption by 15.4% in the best case.