# Decentralized Computation Offloading Game For Mobile Cloud Computing

arXiv: https://arxiv.org/abs/1404.3200 · HTML: https://mlsanity.com/paper/1404.3200/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2014.

## Built on by (most cited first, 7 of 7)

- [Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications](https://mlsanity.com/paper/2212.14749/) (2022)
- [GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks](https://mlsanity.com/paper/2412.08296/) (2024)
- [Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions](https://mlsanity.com/paper/2204.02267/) (2022)
- [Digital Twin-Empowered Network Planning for Multi-Tier Computing](https://mlsanity.com/paper/2210.02616/) (2022)
- [Adaptive DNN Surgery for Selfish Inference Acceleration with On-demand Edge Resource](https://mlsanity.com/paper/2306.12185/) (2023)
- [Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application](https://mlsanity.com/paper/2208.04237/) (2022)
- [Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control](https://mlsanity.com/paper/2212.13141/) (2022)

## Abstract (arXiv)

Mobile cloud computing is envisioned as a promising approach to augment computation capabilities of mobile devices for emerging resource-hungry mobile applications. In this paper, we propose a game theoretic approach for achieving efficient computation offloading for mobile cloud computing. We formulate the decentralized computation offloading decision making problem among mobile device users as a decentralized computation offloading game. We analyze the structural property of the game and show that the game always admits a Nash equilibrium. We then design a decentralized computation offloading mechanism that can achieve a Nash equilibrium of the game and quantify its efficiency ratio over the centralized optimal solution. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.