Popularity-Aware Incentive-Compatible Dynamic Service Caching in Mobile Edge Computing

In mobile edge computing systems, base station (BS) equipped with edge servers can provide computing services to users to reduce their task durations. The BS prices the service programs based on user demand to maximize its own profits. Additionally, due to limited caching capacity and variations in service programs popularity, the BS has to dynamically select which service programs to cache. To address the conflict between high profits requirement and system instability, we propose a two time-scale framework to optimize service caching, pricing and task offloading. Under the small time scale, by modeling the interaction between the BS and users as a two-stage game, we derive the optimal offloading strategy and pricing algorithm. Then, we deduce the existence of equilibrium points. Under the large time scale, we propose a game-nested deep reinforcement learning algorithm to dynamically adjust the service caching according to estimated popularity information. Extensive data based simulations demonstrate the efficiency of the proposed approach.