Acceleration of Distributed Discrete Event Simulation with Intelligent Approximation of Look-Ahead

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One major problem of distributed discrete event simulation is its relative poor performance due to the huge overhead to maintain the order of causality, which guarantees the correctness of the simulation. As result, the execution time cannot be reduced significantly compared to sequential simulation. This holds especially when the processes are tightly coupled and the look-ahead is very short. On the other hand, there usually is a great amount of events in the simulation of a wide range of models, where a limited deviation in the execution time of these events has a very small influence on the correctness of the final simulation result. Thus the simulation does not need to be exact regarding the time stamp and the order of every single event. Acceptance of such deviations in a controlled way provides a mechanism to achieve the trade-off between simulation accuracy and execution time.The main objective of this project is to develop a methodology, so that the execution time of the simulation will be reduced by tolerating such deviations to a certain extent while the result still has acceptable accuracy. To achieve this objective, we propose a new approach of distributed simulation with semi-conservative look-ahead estimation. In this approach, we consider to estimate the look-ahead permitting limited over-estimation. The over-estimation might result in causality errors, which will be dismissed by a very efficient recovery procedure at the expense of errors in the simulation result. Acceptance of such over-estimation is a way to maximize the look-ahead that results in a reduction of the execution time of the simulation.