Cache on Track (CoT): Decentralized Elastic Caches for Cloud Environments
Cache on Track (CoT): Decentralized Elastic Caches for Cloud Environments
复制标题
DOI：
10.5441/002/edbt.2021.20
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Victor Zakhary;Lawrence Lim;D. Agrawal;A. E. Abbadi
中科院分区：
文献类型：
--
作者：
Victor Zakhary;Lawrence Lim;D. Agrawal;A. E. Abbadi
分布式缓存被广泛部署，为十亿用户规模的社交网络和 Web 应用程序提供服务。本文介绍了 Cache-on-Track (CoT)，这是一种适用于云环境的去中心化、弹性和预测性缓存框架。 CoT 提出了一种新的缓存替换策略，专门针对小型前端缓存而设计，这些缓存可以服务于更新百分比较小的倾斜工作负载。小型前端缓存主要用于缓解分布式缓存层中服务器之间的负载不平衡。前端服务器使用heavy hitter跟踪算法来持续跟踪top-k热键。 CoT 动态缓存跟踪键中的 Top-C 热键。与其他替换策略相比，CoT 的主要优势在于它能够动态调整跟踪器和缓存大小以响应工作负载分布变化。我们的实验表明，在不同倾斜工作负载上，对于相同的缓存大小，CoT 的替换策略始终优于 LRU、LFU 和 ARC 的命中率。此外，当两种策略配置相同的跟踪（历史）大小时，CoT 的命中率略优于 LRU-2。与其他替换策略相比，CoT 通过减少 50% 到 93.75% 的前端缓存来实现服务器大小负载平衡。最后，实验表明，CoT 的大小调整算法成功地自动配置跟踪器和缓存大小，以在工作负载分布发生变化的情况下实现后端负载平衡。
Distributed caches are widely deployed to serve social networks and web applications at billion-user scales. This paper presents Cache-on-Track (CoT), a decentralized, elastic, and predictive caching framework for cloud environments. CoT proposes a new cache replacement policy specifically tailored for small front-end caches that serve skewed workloads with small update percentage. Small front-end caches are mainly used to mitigate the load-imbalance across servers in the distributed caching layer. Front-end servers use a heavy hitter tracking algorithm to continuously track the top-k hot keys. CoT dynamically caches the top-C hot keys out of the tracked keys. CoT’s main advantage over other replacement policies is its ability to dynamically adapt its tracker and cache sizes in response to workload distribution changes. Our experiments show that CoT’s replacement policy consistently outperforms the hit-rates of LRU, LFU, and ARC for the same cache size on different skewed workloads. Also, CoT slightly outperforms the hit-rate of LRU-2 when both policies are configured with the same tracking (history) size. CoT achieves server size load-balance with 50% to 93.75% less front-end cache in comparison to other replacement policies. Finally, experiments show that CoT’s resizing algorithm successfully auto-configures the tracker and cache sizes to achieve back-end load-balance in the presence of workload distribution changes.