Adaptive Contextual Caching for Mobile Edge Large Language Model Service

TL;DR

This paper introduces an adaptive caching framework for mobile edge LLMs that uses deep reinforcement learning to improve cache efficiency, reduce latency, and lower overhead in resource-limited environments.

Contribution

It proposes a novel DRL-based cache management method that anticipates user needs, outperforming traditional caching policies in edge LLM services.

Findings

Cache hit rate exceeds 80% after 11 episodes

Retrieval latency reduced by up to 40%

Local caching overhead decreased by up to 55%

Abstract

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80\% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40\%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55\%, enabling scalable, low-latency LLM services in resource-constrained edge environments.