TinyLFU

Abstract

This article proposes to use a frequency-based cache admission policy in order to boost the effectiveness of caches subject to skewed access distributions. Given a newly accessed item and an eviction candidate from the cache, our scheme decides, based on the recent access history, whether it is worth admitting the new item into the cache at the expense of the eviction candidate. This concept is enabled through a novel approximate LFU structure called TinyLFU , which maintains an approximate representation of the access frequency of a large sample of recently accessed items. TinyLFU is very compact and lightweight as it builds upon Bloom filter theory. We study the properties of TinyLFU through simulations of both synthetic workloads and multiple real traces from several sources. These simulations demonstrate the performance boost obtained by enhancing various replacement policies with the TinyLFU admission policy. Also, a new combined replacement and eviction policy scheme nicknamed W-TinyLFU is presented. W-TinyLFU is demonstrated to obtain equal or better hit ratios than other state-of-the-art replacement policies on these traces. It is the only scheme to obtain such good results on all traces.

Automation of application‐level caching in a seamless way

“…In addition to the traditionally explored access history and cache-related statistics in existing admission solutions [12,29,28], such as frequency and recency, our approach also considers caching metadata retrieved from the application during its execution, such as cost to retrieve, user sessions, cache size and data size. Such metadata are in fact information that developers use while designing and implementing application-level caching, and thus enrich the application model with valuable application-specific information regarding the applicability of caching.…”

“…Also focusing on filtering content, TinyLFU [28] uses an approximate LFU structure, which maintains a representation of the access frequency of recently accessed contents, to boost the admission effectiveness of caches. TinyLFU acts reactively when the cache is full and decides whether it is worthwhile admitting content, considering the cost of an eviction and the usefulness of the new content.…”

Ditto: An Elastic and Adaptive Memory-Disaggregated Caching System

“…Caching Algorithms. Caching algorithms distinguish the hotness of objects using recency [73,86], frequency [21] and other access information [11], or combining various information together [7,[10][11][12] to get higher hit rates. Recently, there are many machine-learning-based adaptive caching algorithms [6,47,59,74].…”

Spatially Correlated Placement Policies for Wireless Content Caching Networks

“…The conventional approach to caching, the Most Popular Content (MPC) policy, fills each cache with the most popular items [4]. In the case when the popularity of content is unknown, policies, such as Least Recently Used (LRU) [5], Least Frequently Used (LFU) [6] and Time-To-Live (TTL) [7], have been designed to achieve a placement that performs closely to the one of MPC. Otherwise, if the popularity is known or predicted, there are policies [8]- [17] that outperform MPC in networks.…”

“…The authors of [8] proved that P ICP is a concave function of caching probability vector {q j } j∈[n] , and give the hit probability under ICP by solving a concave maximization problem with constraint (6) ICP: Maximize:…”