Towards Lightweight and Robust Machine Learning for CDN CachingHotNets-XVII, November 15–16, 2018, Redmond, WA, USA.
Daniel S. Berger
Carnegie Mellon University
Recent advances in the field of reinforcement learning promise a general approach to optimize networking systems. This paper argues against the recent trend for generalization by introducing a case study where domain-specific modeling enables the application of lightweight and robust learning techniques. We study CDN caching systems, which make a good case for optimization as their performance directly affects operational costs, while currently relying on many hand-tuned parameters. In caching, reinforcement learning has been shown to perform suboptimally when compared to simple heuristics. A key challenge is that rewards (cache hits) manifest with large delays, which prevents timely feedback to the learning algorithm and introduces significant complexity. This paper shows how to significantly simplify this problem by explicitly modeling optimal caching decisions (OPT). While prior work considered deriving OPT impractical, recent theoretical modeling advances change this assumption. Modeling OPT enables even lightweight decision trees to outperform state-of-the-art CDN caching heuristics.
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