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Title	Evaluation Of Multi-Armed Bandit Algorithms For Efficient Resource Allocation In Edge Platforms
ID_Doc	24947
Authors	Wang J.; Zuckerman S.; del Castillo J.A.L.
Year	2025
Published	Lecture Notes in Computer Science, 15386 LNCS
DOI	http://dx.doi.org/10.1007/978-3-031-90203-1_5
Abstract	As computational systems become more heterogeneous and the number of computing nodes increases, designing high-performance and energy-efficient scheduling policies for Edge/IoT platforms has become increasingly important. This work evaluates multi-armed bandit (MAB) strategies for efficient resource allocation in Edge platforms, like smart cities or smart buildings. Factors like parallel performance and energy usage optimization were taken into account. The resource allocation methods proposed in this paper extend beyond simulations, involving real tasks run on actual IoT devices. We find that MAB scheduling, adapted to the specifics of applications running on heterogeneous IoT devices, can effectively balance execution time and power consumption, thus achieving optimal task allocation and resource utilization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Author Keywords	Edge Computing; IoT; Multi-Armed Bandit Algorithms; Resource Allocation; Scheduling

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