Demystifying and Improving Lazy Promotion in Cache Eviction

2026-08-30

AI Summary

研究通过生产系统追踪评估五种Lazy Promotion策略，提出两种新方法D-FR和AGE以减少LRU中的不必要提升，同时保持或降低缺失率。

Abstract

Cache eviction algorithms play a critical role in the performance of modern data systems, yet their scalability is often limited by the high computational overhead associated with object promotions. Lazy Promotion techniques have emerged as relaxations of traditional Least-Recently-Used (LRU) methods, designed to alleviate lock contention and increase throughput. This work uses production traces from real-world systems to benchmark five Lazy Promotion strategies: Probabilistic-LRU, Batch-LRU, Delay-LRU, FIFO-reinsertion, and Random-LRU. We evaluate these techniques across miss ratio, scalability, promotion count, and a novel metric called promotion efficiency, which measures the number of hits per promotion. Our results reveal that Delay-LRU and FIFO-reinsertion significantly improve promotion efficiency, whereas Batch-LRU and Probabilistic-LRU struggle to reduce promotions without significantly increasing miss ratio. We further explore the impact of lazy promotion in advanced algorithms such as ARC and 2Q and make a similar observation. Moreover, we uncover substantial optimization potential, showing that most cache promotions are unnecessary when equipped with oracle knowledge. To further reduce promotions in LRU, we propose two novel enhancements-Delayed FIFO-reinsertion (D-FR) and Age-Guided Eviction (AGE)-that reduce promotions by 20-60% while achieving a similar or lower miss ratio.

Problem

• Cache Eviction
• Lazy Promotion
• LRU
• Promotion Efficiency
• Miss Ratio

Innovation

• Delayed FIFO-reinsertion
• Age-Guided Eviction
• promotion efficiency