Path Structured Multimarginal Schr\"odinger Bridge for Probabilistic Learning of Hardware Resource Usage by Control Software

• URL: http://arxiv.org/abs/2310.00604v2
• Date: Tue, 3 Oct 2023 12:13:34 GMT
• Title: Path Structured Multimarginal Schr\"odinger Bridge for Probabilistic Learning of Hardware Resource Usage by Control Software
• Authors: Georgiy A. Bondar, Robert Gifford, Linh Thi Xuan Phan, Abhishek Halder
• Abstract summary: The solution of the path structured multimarginal Schr"odinger bridge problem (MSBP) is the most-likely measure-valued trajectory. We leverage recent algorithmic advances in solving such MSBPs for learning hardware resource usage by control software.
• Score: 1.7601096935307592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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