Adaptive time warp simulation of timed Petri nets

Time warp (TW), although generally accepted as a potentially effective parallel and distributed simulation mechanism for timed Petri nets, can reveal deficiencies in certain model domains. Particularly, the unlimited optimism underlying TW can lead to excessive aggressiveness in memory consumption due to saving state histories, and waste of CPU cycles due to over-optimistically progressing simulations that eventually have to be “rolled back”. Furthermore, in TW simulations executing in distributed memory environments, the communication overhead induced by the roll-back mechanism can cause pathological overall simulation performance. In this work, an adaptive optimism control mechanism for TW is developed to overcome these shortcomings. By monitoring and statistically analyzing the arrival processes of synchronization messages, TW simulation progress is probabilistically throttled based on the forecasted time stamp of forthcoming messages. Two classes of arrival process characterizations are studied, reflecting that a natural trade-off exists among the computational and space complexity, and the respective prediction accuracy: While forecasts based on metrics of central tendency are computationally cheap but yield inadequate predictions for correlated arrivals (thus negatively affecting performance), time series based forecast methods give higher prediction accuracy, but at higher computational cost