一句话：提出利用程序行为统计知识动态调整缓存大小的可变大小缓存，在真实负载离线评估中超越最优固定缓存 OPT。
缓存技术广泛应用于虚拟内存管理、服务器缓存和内存缓存等计算基础设施。固定大小缓存难以适应应用的动态需求，且精确预测程序行为通常很困难。本文基于两个观察：根据动态应用需求改变缓存大小可提升空间利用率；统计性地预测程序行为比精确预测更容易。论文提出一种可变大小缓存，利用程序行为的统计知识来最大化缓存性能。评估使用真实负载的数据访问 trace，包括来自 Facebook 的 Memcached trace 和 Microsoft Research 的存储 trace。在离线设置中，该缓存甚至超越 OPT——使用程序行为精确知识的最优固定大小缓存。
Caching techniques are widely used in today's computing infrastructure from virtual memory management to server cache and memory cache. This paper builds on two observations. First, the space utilization in cache can be improved by varying the cache size based on dynamic application demand. Second, it is easier to predict application behavior statistically than precisely. This paper presents a new variable-size cache that uses statistical knowledge of program behavior to maximize the cache performance. We measure performance using data access traces from real-world workloads, including Memcached traces from Facebook and storage traces from Microsoft Research. In an offline setting, the new cache is demonstrated to outperform even OPT, the optimal fixed-size cache which makes use of precise knowledge of program behavior.
@inproceedings{paperbot4861,
title = {Beating OPT with Statistical Clairvoyance and Variable Size Caching},
author = {Pengcheng Li and Colin Pronovost and William Wilson and Benjamin Tait and Jie Zhou and Chen Ding and John Criswell},
booktitle = {ASPLOS},
year = {2019},
doi = {10.1145/3297858.3304067}
}