Modeling the Collaborative Edge Data Caching Problem via a Dynamic DCOP

The Collaborative Edge Data Caching (CEDC) problem poses a significant challenge in Mobile Edge Computing (MEC). It's a research focus to address the problem from the service providers' perspective that requires the optimal caching strategy for service providers to maximize their caching revenue, subject to capacity and latency constraints. However, current research primarily focuses on centralized methods, neglecting the distributed and dynamic nature of CEDC. Accordingly, we first propose to use a Dynamic Distributed Constraint Optimization Problem (D-DCOP) to model the problem in a distributed manner, where capacity, latency constraints and caching revenue are dynamically mapped into local hard constraints and constraint utilities between edge servers according to changes in user requests. The proposed model enables each edge server to make its caching strategy through information exchange with neighboring edge servers. We further present a local search framework for CEDC to handle local hard constraints in the model and apply it to two classic local search algorithms, DSA and MGM, along with specific modifications to avoid repetitive computation. We empirically confirm the superiority of our distributed model and algorithms over state-of-the-art centralized solvers for CEDC.