论文标题
优化内容交付网络缓存的替换策略：超越Belady，以达到看似无法实现的字节率比率
Optimizing Replacement Policies for Content Delivery Network Caching: Beyond Belady to Attain A Seemingly Unattainable Byte Miss Ratio
论文作者
论文摘要
当面对内容交付网络（CDN）中不同大小的对象/文件时，通过近似Belady不再确保最佳字节误差比（BMR）来追求最佳对象遗漏比率（OMR），从而造成有关如何实现CDN中出色的BMR的混乱。为了解决这个问题，我们从实验上观察到，存在一个时间窗口，以延迟对物体的驱逐，以最长的重复使用距离，以改善BMR而不增加OMR。结果，我们引入了深入的加固学习（RL）模型，以通过动态监视OMR和BMR的更改并在时间窗口中实施BMR友好的策略来捕获此时间窗口。基于此政策，我们提出了一个belady和大小驱逐（LRU基本）算法，在维持OMR的同时减少了BMR。为了提高LRU基础的高效和实用性，我们通过两管齐下的方法解决了RL的反馈延迟问题。一方面，我们观察到包含大多数驱逐候选者的LRU缓存队列的后部，允许LRU基本缩短决策区域。另一方面，CDN上的请求分布使得将学习区域分为多个子区域是可行的，它们每个区域都以减少的时间和提高准确性学习。在实际CDN系统中，与LRU相比，LRU基准可以平均将“备份到OS”流量和访问延迟分别减少30.05 \％和17.07 \％。模拟器上的结果证实，LRU基准的表现优于最先进的缓存替换策略，其中LRU-BASE的BMR平均分别比Belady和Belady和基于流动的离线最佳（PFOO）的BMR少0.63 \％和0.33 \％。此外，与学习放松的贝拉迪（LRB）相比，在面对工作负载漂移时，LRU基础可以产生相对稳定的性能。
When facing objects/files of differing sizes in content delivery networks (CDNs) caches, pursuing an optimal object miss ratio (OMR) by approximating Belady no longer ensures an optimal byte miss ratio (BMR), creating confusion about how to achieve a superior BMR in CDNs. To address this issue, we experimentally observe that there exists a time window to delay the eviction of the object with the longest reuse distance to improve BMR without increasing OMR. As a result, we introduce a deep reinforcement learning (RL) model to capture this time window by dynamically monitoring the changes in OMR and BMR, and implementing a BMR-friendly policy in the time window. Based on this policy, we propose a Belady and Size Eviction (LRU-BaSE) algorithm, reducing 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, our observation of a rear section of the LRU cache queue containing most of the eviction candidates allows LRU-BaSE to shorten the decision region. 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, compared to LRU, LRU-BaSE can reduce "backing to OS" traffic and access latency by 30.05\% and 17.07\%, respectively, on average. The results on the simulator confirm that LRU-BaSE outperforms the state-of-the-art cache replacement policies, where LRU-BaSE's BMR is 0.63\% and 0.33\% less than that of Belady and Practical Flow-based Offline Optimal (PFOO), respectively, on average. In addition, compared to Learning Relaxed Belady (LRB), LRU-BaSE can yield relatively stable performance when facing workload drift.
