RAC: Relation-Aware Cache Replacement for Large Language Models
The scaling of Large Language Model (LLM) services faces significant cost and latency challenges, making effective caching under tight capacity crucial. Existing cache replacement policies, from heuristics to learning-based methods, predominantly rely on limited-window statistics such as recency and frequency. We show these signals are not robust for real-world LLM workloads, which exhibit long reuse distances and sparse local recurrence. To address these limitations, we propose Relation-Aware Cache (RAC), an online eviction strategy that leverages semantic relations among requests to guide eviction decisions. RAC synthesizes two relation-aware signals: (1) Topical Prevalence, which aggregates access evidence at the topic level to capture long-horizon reuse; and (2) Structural Importance, which leverages local intra-topic dependency structure to discriminate entries by their future reuse value. Extensive evaluations show that RAC maintains high effectiveness across diverse workloads, consistently surpassing state-of-the-art baselines by 20%--30% in cache hit ratio.