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Resource allocation for UAV-enabled multi-access edge computing

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Abstract

In Ultrareliable and Low Latency Communications (URLLC), balancing trade-offs between energy consumption, service availability, and strict reliability and latency requirements is a significant challenge, especially in unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) environments. The constraints imposed by the size, weight and power limitations of UAVs further complicate this task. This study addresses optimizing resource allocation in such environments to meet URLLC demands while minimizing power consumption and maximizing service availability. We explore the virtualization layer of the network function virtualization (NFV)-MEC architecture, incorporating node availability and power consumption alongside conflicting URLLC reliability and latency demands. We introduce an energy-aware model based on continuous-time Markov chain (CTMC) with an embedded virtual resource scaling scheme for Dynamic Resource Allocation (DRA). To solve the optimization problem related to MEC-enabled UAV node dimensioning, we propose a genetic algorithm (GA)-based solution. Our results demonstrate that the proposed GA-based approach achieves a superior balance, with up to a 44% reduction in power consumption compared to the first fit with maximum resources strategy, while also improving service availability and meeting URLLC requirements. This work provides a comprehensive analysis of key virtualization parameters and their impact on critical services within a single NFV-MEC over a UAV node, offering a robust framework for future 6 G network applications.

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Acknowledgements

This work was supported by the National Council for Scientific and Technological Development (CNPq) Project No. 433142/2018-9, the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, the CNPq Research Productivity Fellowship (Grant No. 312831/2020-0) and the Pernambuco Research Foundation (FACEPE) (Grant No. IBPG-0096\(-\)1.03/16).

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  1. Marcos Falcão, Caio Bruno Souza, Andson Balieiro, and Kelvin Dias have contributed equally to this work.

Authors and Affiliations

  1. Centro de Informática (CIn), Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, Recife, Pernambuco, 50740560, Brazil

    Marcos Falcão, Caio Bruno Souza, Andson Balieiro & Kelvin Dias

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  1. Marcos Falcão
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This paper is equally contributed by each author as everyone wrote a section of it. Besides, there was collaborative efforts in brainstorming the idea of this paper, proofread and formatting of this paper.

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Correspondence to Marcos Falcão or Andson Balieiro.

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Falcão, M., Souza, C.B., Balieiro, A. et al. Resource allocation for UAV-enabled multi-access edge computing. J Supercomput 80, 22770–22802 (2024). https://doi.org/10.1007/s11227-024-06314-3

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