Parallel Computation in Dynamic Fog Computing Networks: A Multi-Armed Bandit Learning-based Decentralized Matching Approach

초록

This paper presents a novel approach utilizing Multi-Armed Bandit (MAB) for parallel computation in dynamic fog computing networks. Fog computing, vital for processing data near the network edge, faces challenges in task allocation while maintaining performance. To address this, a decentralized matching approach leveraging MAB learning algorithms is proposed. Using Thomson sampling (TS), tasks are allocated based on resource availability and network conditions. This decentralized approach empowers fog nodes to autonomously make offloading decisions, enhancing adaptability. Extensive simulations demonstrate the effectiveness of the proposed method in reducing task completion time and optimizing resource utilization in dynamic fog computing environments.

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