Lightweight Robust Size Aware Cache Management

Gil Einziger, Ohad Eytan, R. Friedman, Ben Manes
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引用次数: 5

Abstract

Modern key-value stores, object stores, Internet proxy caches, and Content Delivery Networks (CDN) often manage objects of diverse sizes, e.g., blobs, video files of different lengths, images with varying resolutions, and small documents. In such workloads, size-aware cache policies outperform size-oblivious algorithms. Unfortunately, existing size-aware algorithms tend to be overly complicated and computationally expensive. Our work follows a more approachable pattern; we extend the prevalent (size-oblivious) TinyLFU cache admission policy to handle variable-sized items. Implementing our approach inside two popular caching libraries only requires minor changes. We show that our algorithms yield competitive or better hit-ratios and byte hit-ratios compared to the state-of-the-art size-aware algorithms such as AdaptSize, LHD, LRB, and GDSF. Further, a runtime comparison indicates that our implementation is faster by up to 3× compared to the best alternative, i.e., it imposes a much lower CPU overhead.
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轻量级健壮的大小感知缓存管理
现代键值存储、对象存储、Internet代理缓存和内容交付网络(CDN)经常管理不同大小的对象,例如blob、不同长度的视频文件、不同分辨率的图像和小文档。在这种工作负载中,大小感知缓存策略优于大小无关算法。不幸的是,现有的大小感知算法往往过于复杂且计算成本高。我们的工作遵循一种更平易近人的模式;我们扩展了流行的(大小无关的)TinyLFU缓存允许策略来处理可变大小的项。在两个流行的缓存库中实现我们的方法只需要微小的更改。我们表明,与AdaptSize、LHD、LRB和GDSF等最先进的大小感知算法相比,我们的算法产生了具有竞争力或更好的命中率和字节命中率。此外,运行时比较表明,与最佳替代方案相比,我们的实现速度快了3倍,也就是说,它带来的CPU开销要低得多。
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