# Decentralized Task Offloading and Load-Balancing for Mobile Edge Computing in Dense Networks

arXiv: https://arxiv.org/abs/2407.00080 · HTML: https://mlsanity.com/paper/2407.00080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 24 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks with Uncertainty](https://mlsanity.com/paper/1904.06258/) (2019)
- [Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach](https://mlsanity.com/paper/2112.11818/) (2021)
- [Distributed Cell Association for Energy Harvesting IoT Devices in Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach](https://mlsanity.com/paper/1605.00057/) (2016)
- [On the Application of Uplink/Downlink Decoupled Access in Heterogeneous Mobile Edge Computing](https://mlsanity.com/paper/2110.02747/) (2021)
- [Distributed Task Management in Fog Computing: A Socially Concave Bandit Game](https://mlsanity.com/paper/2203.14572/) (2022)

## 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)

We study the problem of decentralized task offloading and load-balancing in a dense network with numerous devices and a set of edge servers. Solving this problem optimally is complicated due to the unknown network information and random task sizes. The shared network resources also influence the users' decisions and resource distribution. Our solution combines the mean field multi-agent multi-armed bandit (MAB) game with a load-balancing technique that adjusts the servers' rewards to achieve a target population profile despite the distributed user decision-making. Numerical results demonstrate the efficacy of our approach and the convergence to the target load distribution.