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

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%.

• Distributed Systems and Parallel Computing

• Machine Intelligence