When Fancy Eviction Fails: Rethinking Cache Replacement for LLM Prefix Reuse (arxiv.org)

In a recent study, researchers explored the complexities of prefix caching in long-running Large Language Model (LLM) applications, revealing that traditional cache eviction algorithms, particularly Least Recently Used (LRU), often outperform more sophisticated strategies. By analyzing production traces from two companies, the study highlights how the regular pacing of active sessions makes recency a strong predictor of prefix reuse. This insight is crucial as it challenges the belief that advanced eviction policies are necessary for optimal cache performance in scenarios involving extensive context handling. The researchers introduced concepts like the compute-savings ratio and offline oracles to better evaluate caching effectiveness, while also identifying new challenges, such as heavy-tailed session footprints and variable miss costs that arise with increasing sequence lengths. Moving forward, they propose that effective cache management should emphasize recency while incorporating quick demotion and compute-aware eviction methods to address these challenges. Their findings not only enhance our understanding of prefix reuse but also set the stage for future research, as the team plans to release their dataset and simulator for broader community use. This work significantly impacts how the AI/ML community approaches caching strategies in real-world applications.