# Distributed Task Management in Fog Computing: A Socially Concave Bandit Game

arXiv: https://arxiv.org/abs/2203.14572 · HTML: https://mlsanity.com/paper/2203.14572/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Aggregated Momentum: Stability Through Passive Damping](https://mlsanity.com/paper/1804.00325/) (2018)
- [Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing](https://mlsanity.com/paper/1510.00888/) (2015)
- [Multi-armed Bandits with Application to 5G Small Cells](https://mlsanity.com/paper/1510.00627/) (2015)
- [Distributed Optimization for Energy-efficient Fog Computing in the Tactile Internet](https://mlsanity.com/paper/2001.10199/) (2020)
- [Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks with Uncertainty](https://mlsanity.com/paper/1904.06258/) (2019)
- [A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challenges](https://mlsanity.com/paper/1710.11001/) (2017)
- [A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks](https://mlsanity.com/paper/2007.10102/) (2020)
- [Joint Channel Selection and Power Control in Infrastructureless Wireless Networks: A Multi-Player Multi-Armed Bandit Framework](https://mlsanity.com/paper/1407.5447/) (2014)
- [Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game](https://mlsanity.com/paper/1710.06089/) (2017)
- [Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty?](https://mlsanity.com/paper/1812.03557/) (2018)
- [A Market-Based Framework for Multi-Resource Allocation in Fog Computing](https://mlsanity.com/paper/1807.09756/) (2018)
- [Computational Models of Human Decision-Making with Application to the Internet of Everything](https://mlsanity.com/paper/2008.11958/) (2020)
- [Distributed User Association in Energy Harvesting Small Cell Networks: A Competitive Market Model with Uncertainty](https://mlsanity.com/paper/1511.01976/) (2015)
- [Minority Games With Applications to Distributed Decision Making and Control in Wireless Networks](https://mlsanity.com/paper/1610.02131/) (2016)
- [Computation Offloading and Activation of Mobile Edge Computing Servers: A Minority Game](https://mlsanity.com/paper/1710.05499/) (2017)
- [Distributed Cooperation Under Uncertainty in Drone-Based Wireless Networks: A Bayesian Coalitional Game](https://mlsanity.com/paper/2009.00685/) (2020)

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

- [Decentralized Task Offloading and Load-Balancing for Mobile Edge Computing in Dense Networks](https://mlsanity.com/paper/2407.00080/) (2024)

## Abstract (arXiv)

Fog computing leverages the task offloading capabilities at the network's edge to improve efficiency and enable swift responses to application demands. However, the design of task allocation strategies in a fog computing network is still challenging because of the heterogeneity of fog nodes and uncertainties in system dynamics. We formulate the distributed task allocation problem as a social-concave game with bandit feedback and show that the game has a unique Nash equilibrium, which is implementable using no-regret learning strategies (regret with sublinear growth). We then develop two no-regret online decision-making strategies. One strategy, namely bandit gradient ascent with momentum, is an online convex optimization algorithm with bandit feedback. The other strategy, Lipschitz bandit with initialization, is an EXP3 multi-armed bandit algorithm. We establish regret bounds for both strategies and analyze their convergence characteristics. Moreover, we compare the proposed strategies with an allocation strategy named learning with linear rewards. Theoretical- and numerical analysis shows the superior performance of the proposed strategies for efficient task allocation compared to the state-of-the-art methods.