Summary of Multi-time Scale Service Caching and Pricing in Mec Systems with Dynamic Program Popularity, by Yiming Chen et al.
Multi-Time Scale Service Caching and Pricing in MEC Systems with Dynamic Program Popularity
by Yiming Chen, Xingyuan Hu, Bo Gu, Shimin Gong, Zhou Su
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors propose a novel framework for optimizing service caching, pricing, and task offloading in mobile edge computing systems. The framework is designed to address the conflict of interest between base stations (BSs) equipped with edge servers and users, who determine their offloading strategies based on prices to minimize costs. The authors develop a two-time scale approach that jointly optimizes these three components using game-nested deep reinforcement learning and a two-stage game model. Experimental results demonstrate the efficiency of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new framework for mobile edge computing that combines service caching, pricing, and task offloading. The authors develop an algorithm to dynamically adjust service caching based on estimated popularity information. They also model the interaction between BSs and users as a two-stage game to derive optimal pricing and offloading strategies. This approach is designed to reduce task execution time for users while maximizing profit for BSs. |
Keywords
* Artificial intelligence * Reinforcement learning
