The solution of the path structured multimarginal Schr\"{o}dinger bridge problem (MSBP) is the most-likely measure-valued trajectory consistent with a sequence of observed probability measures or distributional snapshots. We leverage recent algorithmic advances in solving such structured MSBPs for learning stochastic hardware resource usage by control software. The solution enables predicting the time-varying distribution of hardware resource availability at a desired time with guaranteed linear convergence. We demonstrate the efficacy of our probabilistic learning approach in a model predictive control software execution case study. The method exhibits rapid convergence to an accurate prediction of hardware resource utilization of the controller. The method can be broadly applied to any software to predict cyber-physical context-dependent performance at arbitrary time.

翻译：路径结构化多边际薛定谔桥问题（MSBP）的解，是与一系列观测概率测度或分布快照序列最吻合的测度值轨迹。我们利用解决此类结构化MSBP的最新算法进展，对控制软件的随机硬件资源使用进行学习。该解能在保证线性收敛的条件下，预测所需时刻硬件资源可用性的时变分布。我们通过一个模型预测控制软件执行的案例研究，验证了该概率学习方法的有效性。该方法能快速收敛至控制器硬件资源利用率的精确预测，并可广泛应用于任意软件，在任意时刻预测信息物理系统上下文相关的性能。