Parallel simulation of queueing petri nets

Abstract

Queueing Petri Nets (QPNs) are a powerful formalism to model the performance of software systems. Such models can be solved using analytical or simulation techniques. Analytical techniques suffer from scalability issues, whereas simulation techniques often require very long simulation runs. Existing simulation techniques for QPNs are strictly sequential and cannot exploit the parallelism provided by modern multi-core processors. In this paper, we present an approach to parallel discrete-event simulation of QPNs using a conservative synchronization algorithm. We consider the spatial decomposition of QPNs as well as the lookahead calculation for different scheduling strategies. Additionally, we propose techniques to reduce the synchronization overhead when simulating performance models describing systems with open workloads. The approach is evaluated in three case studies using performance models of real-world software systems. We observe speedups between 1.9 and 2.5 for these case studies. We also assessed the maximum speedup that can be achieved with our approach using synthetic models.