Modern key-value stores, object stores, Internet proxy caches, as well as Content Delivery Networks (CDN) often manage objects of diverse sizes, e.g., blobs, video files of different lengths, images with varying resolution, 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 x3 compared to the best alternative, i.e., it imposes much lower CPU overhead.

中文翻译：

---

轻量级健壮的大小感知缓存管理

现代的键值存储，对象存储，Internet代理缓存以及内容传递网络（CDN）通常管理各种大小的对象，例如blob，不同长度的视频文件，分辨率不同的图像和小文档。在此类工作负载中，大小感知缓存策略的性能优于大小忽略算法。不幸的是，现有的尺寸感知算法往往过于复杂且计算昂贵。我们的工作遵循更平易近人的模式。我们扩展了流行的（大小可忽略的）TinyLFU缓存允许策略来处理大小可变的项目。在两个流行的缓存库中实现我们的方法仅需进行很小的更改。我们证明，与最先进的大小感知算法（例如AdaptSize，LHD，LRB和GDSF）相比，我们的算法产生的竞争命中率和字节命中率更高。