A Faster-than-Real-Time Simulation Approach for Cluster Computing with Model as a Service
Traditional system simulation suffers from issues such as tight coupling between models and platforms, limited local computing power, and the inability to scale external computing resources. In large-scale scenarios, simulation efficiency is low, making it difficult to achieve faster-than-real-time rapid iteration. This paper proposes a Modelasa Service (MaaS)-based faster-than-real-time simulation method oriented toward cluster computing. By decoupling and encapsulating equipment models as independent services, the method leverages distributed parallel computing on a cluster to improve per-cycle computation efficiency, thereby breaking through the performance bottleneck of traditional centralized simulation. To address the temporal inconsistency caused by asynchronous computation of model services, a barrier synchronization mechanism is introduced, ensuring that all models complete their computation and return results for the current cycle before proceeding to the next simulation cycle, thus maintaining temporal consistency throughout the simulation. Experimental results demonstrate that the proposed method can effectively scale simulation computing power and achieve simulation acceleration while ensuring temporal consistency, making it suitable for large-scale system-of-systems simulation scenarios.