CHIME: Cost-Constrained Hybrid Popularity-Aware Intelligent Service Caching Framework for MEC
Mobile edge computing (MEC) has emerged as a promising paradigm to support latency-sensitive applications by deploying computing and storage resources closer to end users. Service caching at the network edge is an effective approach to further reduce response latency and improve quality of service (QoS). However, the increasing scale and dynamic nature of user requests pose significant challenges to efficient service caching and updating under limited edge resources and cost budgets. In this article, we investigate the joint optimization problem of service caching and dynamic updating in a cloud–edge–end collaborative architecture, with the objective of minimizing the average service access latency of users. We first construct a comprehensive system model that captures communication latency, service response delay, and the costs associated with service caching and updates. The problem is formulated as a nonlinear integer programming problem and addressed via a two-stage solution framework. Specifically, in the initial caching stage, we employ Lagrangian relaxation to reduce the high-dimensional knapsack problem and design an efficient algorithm integrated with Tabu Search. In the dynamic updating stage, we develop a Monte Carlo tree search (MCTS)-based algorithm enhanced by service popularity awareness, along with a novel service replacement strategy that considers user preference and service reliability. Extensive experiments under various system scales demonstrate that the proposed approach significantly outperforms state-of-the-art methods in terms of average service latency, cost budget satisfaction, and update efficiency.