Rethinking Caching for LLM Serving Systems: Beyond Traditional Heuristics
This addresses the challenge of meeting strict SLOs under computational constraints for LLM serving, representing a novel method rather than an incremental improvement.
The paper tackled the problem of inefficient caching in LLM serving systems by introducing SISO, a semantic caching system that achieved up to 1.71× higher hit ratios and improved SLO attainment compared to state-of-the-art systems.
Serving Large Language Models (LLMs) at scale requires meeting strict Service Level Objectives (SLOs) under severe computational and memory constraints. Nevertheless, traditional caching strategies fall short: exact-matching and prefix caches neglect query semantics, while state-of-the-art semantic caches remain confined to traditional intuitions, offering little conceptual departure. Building on this, we present SISO, a semantic caching system that redefines efficiency for LLM serving. SISO introduces centroid-based caching to maximize coverage with minimal memory, locality-aware replacement to preserve high-value entries, and dynamic thresholding to balance accuracy and latency under varying workloads. Across diverse datasets, SISO delivers up to 1.71$\times$ higher hit ratios and consistently stronger SLO attainment compared to state-of-the-art systems.