A co-simulation may comprise several heterogeneous federates with diverse spatial and temporal execution characteristics. In an iterative time-stepped simulation, a federation exhibits the Bulk Synchronous Parallel (BSP) computation paradigm in which all federates perform local operations and synchronize with their peers before proceeding to the next round of computation. In this context, the lowest performing (i.e., slowest) federate dictates the progression of the federation logical time. One challenge in co-simulation is performance profiling for individual federates and entire federations. The computational resource assignment to the federates can have a large impact on federation performance. Furthermore, a federation may comprise federates located on different physical machines as is the case for cloud and edge computing environments. As such, distributed profiling and resource assignment to the federation is a major challenge for operationalizing the co-simulation execution at scale. This paper presents the Execution Performance Profiling and Optimization (EXPPO) methodology, which addresses these challenges by using execution performance profiling at each simulation execution step and for every federate in a federation. EXPPO uses profiling to learn performance models for each federate, and uses these models in its federation resource recommendation tool to solve an optimization problem that improves the execution performance of the co-simulation. Using an experimental testbed, the efficacy of EXPPO is validated to show the benefits of performance profiling and resource assignment in improving the execution runtimes of co-simulations while also minimizing the execution cost.

协同仿真可能包括多个具有不同空间和时间执行特性的异构联邦。在迭代时间步长模拟中，联邦展示了批量同步并行 (BSP) 计算范式，其中所有联邦执行本地操作并在进行下一轮计算之前与其对等方同步。在这种情况下，性能最低（即最慢）的联邦决定了联邦逻辑时间的进展。联合仿真中的一项挑战是单个联邦和整个联邦的性能分析。分配给联邦的计算资源会对联邦性能产生很大影响。此外，联邦可以包括位于不同物理机器上的联邦，就像云和边缘计算环境的情况一样。因此，对联合进行分布式分析和资源分配是大规模实施协同仿真的主要挑战。本文介绍了执行性能分析和优化 (EXPPO) 方法，该方法通过在每个模拟执行步骤和联邦中的每个联邦成员使用执行性能分析来解决这些挑战。EXPPO使用分析来学习每个联邦的性能模型，并在其联邦资源推荐工具中使用这些模型来解决优化问题，从而提高联合仿真的执行性能。使用实验测试台，EXPPO的功效得到验证，以显示性能分析和资源分配在改善协同仿真的执行运行时间同时最大限度地降低执行成本方面的好处。