Georgiy A. Bondar;Robert Gifford;Linh Thi Xuan Phan;Abhishek Halder
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引用次数: 0
Abstract
We propose to learn the time-varying stochastic computational resource usage of software as a graph-structured Schrödinger bridge problem (SBP). In general, learning the computational resource usage from data is challenging because resources, such as the number of CPU instructions and the number of last level cache requests are both time-varying and statistically correlated. Our proposed method enables learning the joint time-varying stochasticity in computational resource usage from the measured profile snapshots in a nonparametric manner. The method can be used to predict the most-likely time-varying distribution of computational resource availability at a desired time. We provide detailed algorithms for stochastic learning in both single-core and multicore cases, discuss the convergence guarantees, computational complexities, and demonstrate their practical use in two case studies: a single-core nonlinear model predictive controller (NMPC) and a synthetic multicore software.
期刊介绍:
The IEEE Transactions on Control Systems Technology publishes high quality technical papers on technological advances in control engineering. The word technology is from the Greek technologia. The modern meaning is a scientific method to achieve a practical purpose. Control Systems Technology includes all aspects of control engineering needed to implement practical control systems, from analysis and design, through simulation and hardware. A primary purpose of the IEEE Transactions on Control Systems Technology is to have an archival publication which will bridge the gap between theory and practice. Papers are published in the IEEE Transactions on Control System Technology which disclose significant new knowledge, exploratory developments, or practical applications in all aspects of technology needed to implement control systems, from analysis and design through simulation, and hardware.