Learning from optimal caching for content delivery
Learning from optimal caching for content delivery
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DOI：
10.1145/3485983.3494855
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Gang Yan;Jian Li;D. Towsley
中科院分区：
文献类型：
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作者：
Gang Yan;Jian Li;D. Towsley
内容分发网络（CDN）通过缓存和服务用户请求的内容来分发当今的大部分互联网流量。CDN的一个主要目标是提高其缓存的命中概率，从而减少WAN流量和用户感知的延迟。在本文中，我们开发了一种新的CDN缓存方法，从最佳缓存中学习决策。为了实现这一目标，我们首先提出HRO计算上界的最佳缓存在一个在线的方式，然后利用HRO通知未来的内容准入和驱逐。我们称这种新的缓存设计为LHR。我们表明，LHR是有效的，因为它包括一个检测机制的模型更新，一个自动调整的基于阈值的模型，内容准入与一个简单的驱逐规则。我们已经实现了一个LHR模拟器，以及在Apache流量服务器和咖啡因的原型，分别。我们使用四个生产CDN跟踪的实验结果表明，LHR始终优于最先进的技术，与典型的生产CDN缓存相比，命中概率增加了9%，WAN流量减少了15%。我们对LHR原型的评估表明，它只会带来适度的开销，并且可以部署在当今的CDN服务器上。
Content delivery networks (CDNs) distribute much of today's Internet traffic by caching and serving users' contents requested. A major goal of a CDN is to improve hit probabilities of its caches, thereby reducing WAN traffic and user-perceived latency. In this paper, we develop a new approach for caching in CDNs that learns from optimal caching for decision making. To attain this goal, we first propose HRO to compute the upper bound on optimal caching in an online manner, and then leverage HRO to inform future content admission and eviction. We call this new cache design LHR. We show that LHR is efficient since it includes a detection mechanism for model update, an auto-tuned threshold-based model for content admission with a simple eviction rule. We have implemented an LHR simulator as well as a prototype within an Apache Traffic Server and the Caffeine, respectively. Our experimental results using four production CDN traces show that LHR consistently outperforms state of the arts with an increase in hit probability of up to 9% and a reduction in WAN traffic of up to 15% compared to a typical production CDN cache. Our evaluation of the LHR prototype shows that it only imposes a moderate overhead and can be deployed on today's CDN servers.