HALP: Heuristic Aided Learned Preference Eviction Policy for YouTube Content Delivery Network


Zhenyu Song, Princeton University; Kevin Chen, Νikhil Sarda, Deniz Altınbüken, Eugene Brevdo, Jimmy Coleman, Xiao Ju, Pawel Jurczyk, Richard Schooler, and Ramki Gummadi, Google


Video streaming services are among the largest web applications in production, and a large source of downstream internet traffic. A large-scale video streaming service at Google, YouTube, leverages a Content Delivery Network (CDN) to serve its users. A key consideration in providing a seamless service is cache efficiency. In this work, we demonstrate machine learning techniques to improve the efficiency of YouTube's CDN DRAM cache. While many recently proposed learning-based caching algorithms show promising results, we identify and address three challenges blocking deployment of such techniques in a large-scale production environment: computation overhead for learning, robust byte miss ratio improvement, and measuring impact under production noise. We propose a novel caching algorithm, HALP, which achieves low CPU overhead and robust byte miss ratio improvement by augmenting a heuristic policy with machine learning. We also propose a production measurement method, impact distribution analysis, that can accurately measure the impact distribution of a new caching algorithm deployment in a noisy production environment.

HALP has been running in YouTube CDN production as a DRAM level eviction algorithm since early 2022 and has reliably reduced the byte miss during peak by an average of 9.1% while expending a modest CPU overhead of 1.8%.

NSDI '23 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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@inproceedings {286447,
author = {Zhenyu Song and Kevin Chen and Νikhil Sarda and Deniz Alt{\i}nb{\"u}ken and Eugene Brevdo and Jimmy Coleman and Xiao Ju and Pawel Jurczyk and Richard Schooler and Ramki Gummadi},
title = {{HALP}: Heuristic Aided Learned Preference Eviction Policy for {YouTube} Content Delivery Network},
booktitle = {20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
year = {2023},
isbn = {978-1-939133-33-5},
address = {Boston, MA},
pages = {1149--1163},
url = {https://www.usenix.org/conference/nsdi23/presentation/song-zhenyu},
publisher = {USENIX Association},
month = apr,

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