Similarity Caching in Dynamic Cooperative Edge Networks: An Adversarial Bandit Approach

Abstract

Unlike traditional edge caching paradigms, similarity edge caching enables the retrieval of similar content from local caches to fulfill user requests, reducing reliance on remote data centers and improving system performance. Although several pioneering works have contributed to similarity edge caching, most focus on single-edge nodes and/or static environment settings, which are impractical for real-world applications. To address this gap, we investigate the similarity caching problem in dynamic cooperative edge networks, where a set of edge nodes cooperatively serve requests generated from arbitrary distributions with similar content over fluctuating transmission links. This presents a significant challenge, as it requires balancing content similarity with delivery latency over the transmission network and learning the environment in real-time to optimize caching policies. We frame this problem within an adversarial Multi-Armed Bandit framework to accommodate the continuously changing operational environment. To solve this, we propose an online learning-based approach named MABSCP, which dynamically updates caching policies based on real-time feedback to minimize the service cost of edge caching networks. To enhance implementation efficiency, we devise both an offline compact strategy construction method and an online Gibbs sampling method. Finally, trace-driven simulation results demonstrate that our proposed approach outperforms several existing methods in terms of system performance.