SC17 Denver, CO

P65: CAPES: Unsupervised System Performance Tuning Using Neural Network-Based Deep Reinforcement Learning


Authors: Yan Li (University of California, Santa Cruz), Kenneth Chang (University of California, Santa Cruz), Oceane Bel (University of California, Santa Cruz), Ethan Miller (University of California, Santa Cruz), Darrell Long (University of California, Santa Cruz)

Abstract: Parameter tuning is an important task of storage performance optimization. Current practice usually involves numerous tweak-benchmark cycles that are slow and costly. To address this issue, we developed CAPES, a model-less deep reinforcement learning-based unsupervised parameter tuning system driven by a deep neural network (DNN). It is designed to find optimal values for computer systems that have tunable parameters when human tuning can be costly and often cannot achieve optimal performance. CAPES takes periodic measurements of a target computer system's state, and trains a DNN which uses Q-learning to suggest changes to the system's current parameter values. CAPES is minimally intrusive, and can be deployed into a production system to collect training data and suggest tuning actions during the system's daily operation. Evaluation of a prototype on a Lustre file system demonstrates an increase in I/O throughput up to 45% at saturation point.
Award: Best Poster Finalist (BP): no

Poster: pdf
Two-page extended abstract: pdf


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