Outperforming LRU with an adaptive replacement cache algorithm

The self-tuning, low-overhead, scan-resistant adaptive replacement cache algorithm outperforms the least-recently-used algorithm by dynamically responding to changing access patterns and continually balancing between workload recency and frequency features.

Abstract

The self-tuning, low-overhead, scan-resistant adaptive replacement cache algorithm outperforms the least-recently-used algorithm by dynamically responding to changing access patterns and continually balancing between workload recency and frequency features. Caching, a fundamental metaphor in modern computing, finds wide application in storage systems, databases, Web servers, middleware, processors, file systems, disk drives, redundant array of independent disks controllers, operating systems, and other applications such as data compression and list updating. In a two-level memory hierarchy, a cache performs faster than auxiliary storage, but it is more expensive. Cost concerns thus usually limit cache size to a fraction of the auxiliary memory's size.