Adaptive Contextual Caching for Mobile Edge Large Language Model Service (2501.09383v1)

Abstract: Mobile edge 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.

Adaptive Contextual Caching for Mobile Edge LLM Service

The academic paper titled "Adaptive Contextual Caching for Mobile Edge LLM Service" addresses an essential challenge in deploying LLMs at the mobile edge. The constraints of limited computational resources and network bandwidth at the edge motivate the research discussed in this paper. While Retrieval-Augmented Generation (RAG) techniques offer a partial solution by leveraging external knowledge bases, they still suffer from inefficiencies in cache management that can lead to high retrieval latency. This paper proposes an Adaptive Contextual Caching (ACC) framework that anticipates user needs by employing proactive caching of semantically relevant data for mobile-edge LLMs.

The paper establishes the importance of efficient cache management in enhancing the performance and responsiveness of LLMs deployed at the mobile edge. It introduces the ACC framework, which employs a deep reinforcement learning (DRL) module to refine cache replacement policies. This approach balances factors such as user context, document similarity, and cache miss overhead, demonstrating a significant improvement over traditional caching methods like FIFO (First In, First Out), LRU (Least Recently Used), and semantic-only caching strategies.

Key Findings and Numerical Results

The paper presents experimental results evidencing that the ACC framework achieves over 80% cache hit rates after just 11 training episodes. This represents a marked improvement compared to existing caching strategies. Additionally, the ACC framework reduces retrieval latency by up to 40%. These results underscore the framework's efficiency in managing dynamic and resource-constrained environments typical of mobile edge scenarios. It is noteworthy that the ACC framework also cuts down local caching overhead — the cost associated with updating the cache upon a miss — by as much as 55%.

Theoretical and Practical Implications

The introduction of a proactive caching mechanism tailored to mobile edge applications is a significant contribution to the field. The ACC framework stands to enhance the practical applicability of LLMs in mobile-edge environments by mitigating typical constraints such as limited computational power and network resources. From a theoretical standpoint, the application of DRL to optimize cache management policies represents an advancement in adaptive systems, providing a model that can dynamically adjust to contextual and environmental changes.

Future Research Directions

While the paper provides a robust framework for adaptive caching, it leaves open several avenues for future exploration. Hierarchical caching architectures could be investigated further, distributing caching functionalities across multiple layers such as user devices, edge servers, and cloud infrastructure. This would potentially enhance the system's scalability and load balancing capabilities. Additionally, the handling of multimodal data — encompassing diverse inputs such as text, images, and video — remains an area for further paper, as does the integration of real-time dynamic indexing strategies to support rapid updates to the cached data.

To summarize, this paper provides substantive contributions to the efficient deployment of LLMs at the mobile edge, offering a novel framework that leverages proactive and adaptive caching techniques to significantly enhance system performance and user experience. These advancements are poised to facilitate the broader application of LLMs in resource-constrained and dynamically evolving environments.