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Selecting GVT interval for time-warp-based distributed simulation using reinforcement learning technique

In a Time-Warp-based distributed simulation system, a simulation process must save its states and events to handle rollbacks. Periodically, the global minimum of the timestamps of events and messages in the entire system is calculated. This value is known as the global virtual time (GVT), and it plays an important role in a Time Warp system. GVT is only computed periodically because of the computation overhead. An important problem is to determine the optimal interval between two GVT computations. In this paper we present a new approach that uses a simple Reinforcement Learning technique to select the optimal GVT interval. Used in a Time-Warp-based distributed VLSI simulation system, our method was successful in selecting good GVT interval and improving the system's performance.