Learning relaxed belady for content distribution network caching

Zhenyu Song, Daniel S. Berger, Kai Li, Wyatt Lloyd

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020
PublisherUSENIX Association
Pages529-544
Number of pages16
ISBN (Electronic)9781939133137
StatePublished - 2020
Event17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020 - Santa Clara, United States
Duration: Feb 25 2020Feb 27 2020

Publication series

NameProceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020

Conference

Conference17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020
Country/TerritoryUnited States
CitySanta Clara
Period2/25/202/27/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

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