Multi-Time Scale Service Caching and Pricing in MEC Systems with Dynamic Program Popularity

TL;DR

This paper proposes a two-time scale framework for dynamic service caching and pricing in MEC systems, using deep reinforcement learning and game theory to optimize performance amid changing program popularity.

Contribution

It introduces a novel joint optimization approach combining deep reinforcement learning and game-theoretic analysis for service caching and pricing in MEC.

Findings

The framework effectively adapts caching strategies to popularity changes.

The game-theoretic model finds equilibrium strategies for pricing and offloading.

Simulations show improved profit and user satisfaction.

Abstract

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.