  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.
