Beyond Belady to Attain a Seemingly Unattainable Byte Miss Ratio for Content Delivery Networks

Abstract

Reducing the byte miss ratio (BMR) in the Content Delivery Network (CDN) caches can help providers save on the cost of paying for traffic. When evicting objects or files of different sizes in the caches of CDNs, it is no longer sufficient to pursue an optimal object miss ratio (OMR) by approximating Belady to ensure an optimal BMR. Our experimental observations suggest that there are multiple request sequence windows. In these windows, a replacement policy prioritizes the eviction of objects with large sizes and ultimately evicts the object with the longest reuse distance, lowering the BMR without increasing the OMR. To accurately capture those windows, we monitor the changes in OMR and BMR using a deep reinforcement learning (RL) model and then implement a BMR-friendly replacement algorithm in these windows. Based on this policy, we propose a Belady and Size Eviction (LRU-BaSE) algorithm that reduces BMR while maintaining OMR. To make LRU-BaSE efficient and practical, we address the feedback delay problem of RL with a two-pronged approach. On the one hand, we shorten the LRU-base decision region based on the observation that the rear section of the cache queue contains most of the eviction candidates. On the other hand, the request distribution on CDNs makes it feasible to divide the learning region into multiple sub-regions that are each learned with reduced time and increased accuracy. In real CDN systems, LRU-BaSE outperforms LRU by reducing “backing to OS” traffic and access latency by 30.05% and 17.07%, respectively, on average. In simulator tests, LRU-BaSE outperforms state-of-the-art cache replacement policies. On average, LRU-BaSE's BMR is 0.63% and 0.33% less than that of Belady and Practical Flow-based Offline Optimal (PFOO), respectively. In addition, compared to Learning Relaxed Belady (LRB), LRU-BaSE can yield relatively stable performance when facing workload drift.