TY - GEN
T1 - Learning relaxed belady for content distribution network caching
AU - Song, Zhenyu
AU - Berger, Daniel S.
AU - Li, Kai
AU - Lloyd, Wyatt
N1 - Publisher Copyright:
© Proc. of the 17th USENIX Symposium on Networked Systems Design and Impl., NSDI 2020. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85091835547
T3 - Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020
SP - 529
EP - 544
BT - Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020
PB - USENIX Association
T2 - 17th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2020
Y2 - 25 February 2020 through 27 February 2020
ER -