10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
Authors - Hoa Tran-Dang, Dong-Seong Kim Abstract - This paper introduces a novel approach, Digital Twin-Aided Contextual Bandit Learning based Two-Sided Matching (DTCBL-TSM), to optimize computation offloading in dynamic fog computing networks (FCNs). Our proposed framework leverages digital twins (DTs) to enable precise monitoring and prediction of resource states of fog nodes (FNs). By integrating contextual bandit learning, our approach dynamically adapts to changing network conditions, learning optimal offloading strategies over time. Indeed, the two-sided matching mechanism ensures a balanced and fair allocation of tasks to fog nodes, considering both the task requirements and node capacities. Extensive simulations demonstrate that DTCBL-TSM outperforms existing methods in terms of task completion time, resource utilization, and adaptability to network dynamics.
