Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

SpringerLink
Log in

Resource allocation for UAV-enabled multi-access edge computing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

Data availability under request

Code availability

Code availability under request

References

  1. Prathyusha Y, Sheu T-L (2023) Resource allocations for coexisting embb and urllc services in multi-uav aided communication networks for cellular offloading. IEEE Trans Veh Technol. https://doi.org/10.1109/TVT.2023.3340654

    Article  Google Scholar 

  2. Ali R, Zikria YB, Bashir AK, Garg S, Kim HS (2024) Urllc for 5g and beyond: requirements, enabling incumbent technologies and network intelligence. IEEE Access 9:67064–67095. https://doi.org/10.1109/ACCESS.2021.3073806

    Article  Google Scholar 

  3. Zhao L, Zhou G, Zheng G, Chih-Lin I, You X, Hanzo LH (2021) Open-source multi-access edge computing for 6g: Opportunities and challenges. ArXiv abs/2111.11354

  4. Ranjha A, Javed MA, Piran MJ, Asif M, Hussien M, Zeadally S, Frnda J (2023) Towards facilitating power efficient urllc systems in uav networks under jittering. IEEE Trans Consum Electron. https://doi.org/10.1109/TCE.2023.3305550

    Article  Google Scholar 

  5. Tian M, Li C, Hui Y, Cheng N, Yue W, Fu Y, Han Z (2023) On-demand multiplexing of embb/urllc traffic in a multi-uav relay network. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2023.3332022

    Article  Google Scholar 

  6. Zhang Y, Zhao L, Zheng G, Chu X, Ding Z, Chen K-C (2021) Resource allocation for open-loop ultra-reliable and low-latency uplink communications in vehicular networks. IEEE Trans Veh Technol 70(3):2590–2604. https://doi.org/10.1109/TVT.2021.3061582

    Article  Google Scholar 

  7. Falcao M, Souza CB, Balieiro A, Dias K (2021) An analytical framework for urllc in hybrid mec environments. J Supercomput 78:2245–2264. https://doi.org/10.1007/s11227-021-03945-8

    Article  Google Scholar 

  8. Di H, Zhu X, Liu Z, Tu X (2024) Joint blocklength and trajectory optimizations for urllc-enabled uav relay system. IEEE Commun Lett 28(1):118–122. https://doi.org/10.1109/LCOMM.2023.3335655

    Article  Google Scholar 

  9. Morabito R (2015) Power consumption of virtualization technologies: An empirical investigation. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pp. 522–527. https://doi.org/10.1109/UCC.2015.93

  10. Falcão M, Souza C, Balieiro A, Dias K (2023) Dynamic resource allocation for urllc in uav-enabled multi-access edge computing. In: 2023 Joint European Conference on Networks and Communications and 6G Summit (EuCNC/6G Summit), pp. 293–298. https://doi.org/10.1109/EuCNC/6GSummit58263.2023.10188346

  11. Xue D, Guo Y, Li N et al (2023) Cross-domain coordination of resource allocation and route planning for the edge computing-enabled multi-connected vehicles. J Cloud Comput 12(1):33. https://doi.org/10.1186/s13677-023-00415-9

    Article  Google Scholar 

  12. Elgendy IA, Meshoul S, Hammad M (2023) Joint task offloading, resource allocation, and load-balancing optimization in multi-uav-aided mec systems. Appl Sci 13(4):2625. https://doi.org/10.3390/app13042625

    Article  Google Scholar 

  13. Chen J, Cao X, Yang P, Xiao M, Ren S, Zhao Z, Wu DO (2023) Deep reinforcement learning based resource allocation in multi-uav-aided mec networks. IEEE Trans Commun 71(1):296–309. https://doi.org/10.1109/TCOMM.2022.3226193

    Article  Google Scholar 

  14. Safwat NE-D, Hafez IM, Newagy F (2022) 3d placement of a new tethered uav to uav relay system for coverage maximization. Electronics 11(3):385

    Article  Google Scholar 

  15. Zhao M-M, Shi Q, Zhao M-J (2020) Efficiency maximization for uav-enabled mobile relaying systems with laser charging. IEEE Trans Wireless Commun 19(5):3257–3272. https://doi.org/10.1109/TWC.2020.2971987

    Article  MathSciNet  Google Scholar 

  16. Cai Y, Jiang X, Liu M, Zhao N, Chen Y, Wang X (2022) Resource allocation for urllc-oriented two-way uav relaying. IEEE Trans Veh Technol 71(3):3344–3349. https://doi.org/10.1109/TVT.2022.3143174

    Article  Google Scholar 

  17. Iliev TB, Ivanova EP, Stoyanov IS, Mihaylov GY, Beloev IH (2021) Artificial intelligence in wireless communications - evolution towards 6g mobile networks. 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO) https://doi.org/10.23919/MIPRO52101.2021.9597147

  18. Do QV, Pham Q-V, Hwang W-J (2022) Deep reinforcement learning for energy-efficient federated learning in uav-enabled wireless powered networks. IEEE Commun Lett 26(1):99–103. https://doi.org/10.1109/LCOMM.2021.3122129

    Article  Google Scholar 

  19. Pham Q-V, Zeng M, Ruby R, Huynh-The T, Hwang W-J (2021) Uav communications for sustainable federated learning. IEEE Trans Veh Technol 70(4):3944–3948. https://doi.org/10.1109/TVT.2021.3065084

    Article  Google Scholar 

  20. Hou X, Wang J, Jiang C, Zhang X, Ren Y, Debbah M (2023) Uav-enabled covert federated learning. IEEE Trans Wireless Commun 22(10):6793–6809. https://doi.org/10.1109/TWC.2023.3245621

    Article  Google Scholar 

  21. Lu Y, Huang X, Zhang K, Maharjan S, Zhang Y (2020) Blockchain empowered asynchronous federated learning for secure data sharing in internet of vehicles. IEEE Trans Veh Technol 69(4):4298–4311. https://doi.org/10.1109/TVT.2020.2973651

    Article  Google Scholar 

  22. Moriai S (2019) Privacy-preserving deep learning via additively homomorphic encryption. In: 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), pp. 198–198. https://doi.org/10.1109/ARITH.2019.00047

  23. Costanzo F, Lorenzo PD, Barbarossa S (2020) Dynamic resource optimization and altitude selection in uav-based multi-access edge computing. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4985–4989. https://doi.org/10.1109/ICASSP40776.2020.9053594

  24. Yang Z, Pan C, Wang K, Shikh-Bahaei M (2019) Energy efficient resource allocation in uav-enabled mobile edge computing networks. IEEE Trans Wireless Commun 18(9):4576–4589. https://doi.org/10.1109/TWC.2019.2927313

    Article  Google Scholar 

  25. Bekkouche O, Samdanis K, Bagaa M, Taleb T (2020) A service-based architecture for enabling uav enhanced network services. IEEE Netw 34(4):328–335. https://doi.org/10.1109/MNET.001.1900556

    Article  Google Scholar 

  26. Emara M, ElSawy H, Filippou MC, Bauch G (2021) Spatiotemporal dependable task execution services in mec-enabled wireless systems. IEEE Wirel Commun Lett 10(2):211–215. https://doi.org/10.1109/LWC.2020.3024749

    Article  Google Scholar 

  27. Kherraf N, Alameddine HA, Sharafeddine S, Assi CM, Ghrayeb A (2019) Optimized provisioning of edge computing resources with heterogeneous workload in iot networks. IEEE Trans Netw Serv Manage 16(2):459–474. https://doi.org/10.1109/TNSM.2019.2894955

    Article  Google Scholar 

  28. Fautrel T, George L, Fauberteau F, Grandpierre T (2019) An hypervisor approach for mixed critical real-time uav applications. In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 985–991. https://doi.org/10.1109/PERCOMW.2019.8730705

  29. Mavridis I, Karatza HD (2019) Combining containers and virtual machines to enhance isolation and extend functionality on cloud computing. Future Gener Comput Syst 94:674–696

    Article  Google Scholar 

  30. Felter W, Ferreira A, Rajamony R, Rubio J (2015) An updated performance comparison of virtual machines and linux containers. In: 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 171–172. IEEE, ???

  31. Pahl C, Brogi A, Soldani J, Jamshidi P (2017) Cloud container technologies: a state-of-the-art review. IEEE Trans Cloud Comput 7(3):677–692

    Article  Google Scholar 

  32. Kulkarni SG, Liu G, Ramakrishnan KK, Arumaithurai M, Wood T, Fu X (2020) Reinforce: achieving efficient failure resiliency for network function virtualization-based services. IEEE/ACM Trans Netw 28(2):695–708. https://doi.org/10.1109/TNET.2020.2969961

    Article  Google Scholar 

  33. Kaur K, Dhand T, Kumar N, Zeadally S (2017) Container-as-a-service at the edge: Trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wirel Commun 24(3):48–56. https://doi.org/10.1109/MWC.2017.1600427

    Article  Google Scholar 

  34. Anand A, Veciana G (2018) Resource allocation and harq optimization for urllc traffic in 5g wireless networks. IEEE J Sel Areas Commun 36(11):2411–2421. https://doi.org/10.1109/JSAC.2018.2874122

    Article  Google Scholar 

  35. Balieiro AM, Falcão M, Dias KL (2019) An evolutionary scheme for secondary virtual networks mapping onto cognitive radio substrate. Wirel Commun Mob Comput 2019:1872765–1187276519. https://doi.org/10.1155/2019/1872765

    Article  Google Scholar 

  36. Chen S, Newman TR, Evans JB, Wyglinski AM (2010) Genetic algorithm-based optimization for cognitive radio networks. In: 2010 IEEE Sarnoff Symposium, pp. 1–6. https://doi.org/10.1109/SARNOF.2010.5469780

  37. Benamer AR, Boussetta K, Hadj-Alouane NB (2021) A genetic algorithm for the placement of latency-sensitive multiplayer game servers in the fog. In: 2021 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. https://doi.org/10.1109/GLOBECOM46510.2021.9685952

  38. Ruiz L, Durán RJ, Miguel I, Khodashenas PS, Pedreno-Manresa J-J, Merayo N, Aguado JC, Pavón-Mariño P, Siddiqui S, Mata J, Fernández P, Lorenzo RM, Abril EJ (2018) A genetic algorithm for vnf provisioning in nfv-enabled cloud/mec ran architectures. Applied Sciences

  39. Balieiro A, Yoshioka P, Dias K, Cavalcanti D, Cordeiro C (2014) A multi-objective genetic optimization for spectrum sensing in cognitive radio. Expert Syst Appl 41(8):3640–3650. https://doi.org/10.1016/j.eswa.2013.12.010

    Article  Google Scholar 

Download references

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).

Author information

Author notes

  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

Authors
  1. Marcos Falcão
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Caio Bruno Souza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andson Balieiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kelvin Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding authors

Correspondence to Marcos Falcão or Andson Balieiro.

Ethics declarations

Conflict of interest

The authors declare there is no conflict of interest.

Ethical approval

Ethics approval and consent to participate

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-024-06314-3

Keywords

Search

Navigation