Communication-Efficient Task-Offloading in Mobile Edge Computing System: A Multi-Agent Multi-Armed Bandit Approach

doi:10.1109/TCC.2025.3620474

Communication-Efficient Task-Offloading in Mobile Edge Computing System: A Multi-Agent Multi-Armed Bandit Approach

2025-10-14

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DOI：10.1109/TCC.2025.3620474

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Mobile devices within the Mobile Edge Computing (MEC) system can offload tasks from the near server to reduce the retrieval latency. Combining collaborative learning for MEC will make progress in both speeding up the convergence of the task-offloading algorithms applied at the end devices side and relieving resources lacking pressure on servers at edge server or cloud server sides. However, stochastic bandit algorithms are incompetent in the presence of complex environments and rarely consider device-to-device communication among users, lacking flexibility under a centralized system. Thus, in this paper, we propose an Upper Confidence Bound (UCB) based Adaptive Probability Updating algorithm (APU) and apply it at users side guiding them offloading tasks efficiently. APU could let users manage their own probability-based distributed task offloading strategy and based on it select the most adaptive server. Drawing on a long line of research in network communication and traditional MAB frameworks, we build a multi-agent variant of APU and its corresponding MEC system. Furthermore, we take the regret analysis of the proposed algorithms by rigorous mathematical proof of the sub-linearity convergence, and at the end of this paper, we do some simulation experiments to demonstrate the effectiveness.

Keywords:

Edge computing

multi-agent

multi-armed bandits

Communication-Efficient Task-Offloading in Mobile Edge Computing System: A Multi-Agent Multi-Armed Bandit Approach

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