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June 28, 2018

Real-Time Scheduling Policy Selection using Machine Learning by Raphael Camargo(Sao Paulo)

Category: Seminars Real-Time Scheduling Policy Selection using Machine Learning by Raphael Camargo(Sao Paulo)


June 28, 2018

We present a real-time scheduling policy selection algorithm, which takes as input the running queue job characteristics and machine states. We evaluated the use of logistic regression and support vector machines to perform the mapping from queue and machine state to selected scheduling policy. The machine learning algorithms are trained and evaluated using simulations configured using HPC platform traces. When selecting among 5 (five) scheduling policies and using SVM with polynomial kernel, we obtained an accuracy above 80%, when compared to the best possible selection. When simulating the online real-time selection of policies for a period of one year, the method had a performance between selecting the best possible selection of policies and random selection, when considering the mean queue waiting time.

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