Learning Relaxed Belady for Content Distribution Network Caching

This paper presents a new approach for caching in CDNs that uses machine learning to approximate the Belady MIN (oracle) algorithm. To accomplish this complex task, we propose a CDN cache design called Learning Relaxed Belady (LRB) to mimic a Relaxed Belady algorithm, using the concept of Belady boundary. We also propose a metric called good decision ratio to help us make better design decisions. In addition, the paper addresses several challenges to build an end-to-end machine learning caching prototype, including how to gather training data, limit memory overhead, and have lightweight training and prediction.

We have implemented an LRB simulator and a prototype within Apache Traffic Server. Our simulation results with 6 production CDN traces show that LRB reduces WAN traffic compared to a typical production CDN cache design by 4-25%, and consistently outperform other state-of-the-art methods. Our evaluation of the LRB prototype shows its overhead is modest and it can be deployed on today's CDN servers.