In mobile edge computing systems, base stations (BSs) equipped with edge servers can provide computing services to users to reduce their task execution time. However, there is always a conflict of interest between the BS and users. The BS prices the service programs based on user demand to maximize its own profit, while the users determine their offloading strategies based on the prices to minimize their costs. Moreover, service programs need to be pre-cached to meet immediate computing needs. Due to the limited caching capacity and variations in service program popularity, the BS must dynamically select which service programs to cache. Since service caching and pricing have different needs for adjustment time granularities, we propose a two-time scale framework to jointly optimize service caching, pricing and task offloading. For the large time scale, we propose a game-nested deep reinforcement learning algorithm to dynamically adjust service caching according to the estimated popularity information. For the small time scale, by modeling the interaction between the BS and users as a two-stage game, we prove the existence of the equilibrium under incomplete information and then derive the optimal pricing and offloading strategies. Extensive simulations based on a real-world dataset demonstrate the efficiency of the proposed approach.

翻译：在移动边缘计算系统中，配备边缘服务器的基站可通过向用户提供计算服务来减少其任务执行时间。然而，基站与用户之间始终存在利益冲突：基站根据用户需求对服务程序进行定价以实现自身利润最大化，而用户则根据价格确定卸载策略以最小化自身成本。此外，为满足即时计算需求，服务程序需进行预缓存。由于缓存容量有限且服务程序流行度动态变化，基站必须动态选择需缓存的服务程序。考虑到服务缓存与定价对调整时间粒度的需求不同，本文提出一种双时间尺度框架来联合优化服务缓存、定价与任务卸载。在宏观时间尺度上，我们提出一种博弈嵌套深度强化学习算法，根据估计的流行度信息动态调整服务缓存策略。在微观时间尺度上，通过将基站与用户的交互建模为两阶段博弈，我们证明了不完全信息下均衡解的存在性，并推导出最优定价与卸载策略。基于真实数据集的广泛仿真验证了所提方法的有效性。