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

arXiv: https://arxiv.org/abs/2407.03804 · HTML: https://mlsanity.com/paper/2407.03804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Pricing-Driven Service Caching and Task Offloading in Mobile Edge Computing](https://mlsanity.com/paper/2011.02154/) (2020)
- [Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks](https://mlsanity.com/paper/1901.08946/) (2019)
- [Unifying Futures and Spot Market: Overbooking-Enabled Resource Trading in Mobile Edge Networks](https://mlsanity.com/paper/2108.07226/) (2021)
- [Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC](https://mlsanity.com/paper/2302.10558/) (2023)

## Built on by (most cited first, 1 of 1)

- [Service Placement in Small Cell Networks Using Distributed Best Arm Identification in Linear Bandits](https://mlsanity.com/paper/2506.22480/) (2025)

## Abstract (arXiv)

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.