Stochastic Petri nets applied to the performance evaluation of static task allocations in heterogeneous computing environments

Abstract

A stochastic Petri net (SPN) is systematically constructed from a task graph whose component subtasks are statically allocated onto the processor suite of a heterogeneous computing system (HCS). Given that subtask execution times are exponentially distributed an exponential distribution can be generated for the overall completion time. In particular the enabling functions and rate functions used to specify the SPN model provide needed versatility to integrate processor heterogeneity, task priorities, allocation schemes, communication costs, and other factors characteristic of a HCS into a comprehensive performance analysis. The manner in which these parameters are incorporated into the SPN allows the model to be transformed into a testbed for optimization schemes and heuristics. The proposed approach can be applied to arbitrary task graphs including non-series-parallel.