Skip to main content
Log in

An analytical framework for URLLC in hybrid MEC environments

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

Abstract

The conventional mobile architecture is unlikely to cope with Ultra-Reliable Low-Latency Communications (URLLC) constraints, being a major cause for its fundamentals to remain elusive. Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) emerge as complementary solutions, offering fine-grained on-demand distributed resources closer to the User Equipment (UE). This work proposes a multipurpose analytical framework that evaluates a hybrid virtual MEC environment that combines VMs and Containers strengths to concomitantly meet URLLC constraints and cloud-like Virtual Network Functions (VNF) elasticity.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Feng D et al (2019) Toward ultrareliable low-latency communications: typical scenarios, possible solutions, and open issues. IEEE Veh Technol Mag 14(2):94–102. https://doi.org/10.1109/MVT.2019.2903657

    Article  Google Scholar 

  2. Antevski K, Bernardos C, Cominardi L, Oliva A, Mourad A (2020) On the integration of NFV and MEC technologies: architecture analysis and benefits for edge robotics. Comput Netwo 175:1286–1389. https://doi.org/10.1016/j.comnet.2020.107274

    Article  Google Scholar 

  3. 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 

  4. Sultan S, Ahmad I, Dimitriou T (2019) Container security: issues, challenges, and the road ahead. IEEE Access 7:52976–52996. https://doi.org/10.1109/ACCESS.2019.2911732

    Article  Google Scholar 

  5. Santoyo-Gonzlez A, Cervell-Pastor C (2018) Edge nodes infrastructure placement parameters for 5G networks. In: IEEE Conference on Standards for Communications and Networking (CSCN) pp. 1–6. https://doi.org/10.1109/CSCN.2018.8581749

  6. Li C, Cai Q, Zhang C et al (2021) Computation offloading and service allocation in mobile edge computing. J Supercomput. https://doi.org/10.1007/s11227-021-03749-w

    Article  Google Scholar 

  7. Yala L, Frangoudis PA, Ksentini A (2018) Latency and availability driven VNF placement in a MEC-NFV environment. In: IEEE Global Communications Conference (GLOBECOM), pp. 1–7. https://doi.org/10.1109/GLOCOM.2018.8647858.

  8. Farhadi V. et al (2019) Service placement and request scheduling for data-intensive applications in edge clouds. In: IEEE Conference on Computer Communications, pp. 1279–1287. https://doi.org/10.1109/INFOCOM.2019.8737368.

  9. Samanta A, Tang J (2020) Dyme: dynamic microservice scheduling in edge computing enabled IoT. IEEE Internet Things J 7(7):6164–6174. https://doi.org/10.1109/JIOT.2020.2981958

    Article  Google Scholar 

  10. Lee S, Lee S, Shin MK (2019) Low cost MEC server placement and association in 5G networks. In: International Conference on Information and Communication Technology Convergence (ICTC), pp. 879–882. https://doi.org/10.1109/ICTC46691.2019.8939566.

  11. 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 

  12. Tong Z, Zhang T, Zhu Y, Huang R (2020) Communication and computation resource allocation for end-to-end slicing in mobile networks. In: IEEE/CIC International Conference on Communications in China (ICCC), pp. 1286–1291. https://doi.org/10.1109/ICCC49849.2020.9238794.

  13. Sarrigiannis I, Ramantas K, Kartsakli E, Mekikis P, Antonopoulos A, Verikoukis C (2020) Online VNF lifecycle management in an MEC-enabled 5G IoT architecture. IEEE Internet Things J 7(5):4183–4194. https://doi.org/10.1109/JIOT.2019.2944695

    Article  Google Scholar 

  14. 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 Manag 16(2):459–474. https://doi.org/10.1109/TNSM.2019.2894955

    Article  Google Scholar 

  15. Ma S, Chen X, Li Z et al (2019) Performance evaluation of URLLC in 5G based on stochastic network calculus. Mobile Netw Appl. https://doi.org/10.1007/s11036-019-01344-1

    Article  Google Scholar 

  16. Ren Y, Phung-Duc T, Chen J, Yu Z (2016) Dynamic auto scaling algorithm (DASA) for 5G mobile networks. In: IEEE Global Communications Conference (GLOBECOM), pp. 1–6. https://doi.org/10.1109/GLOCOM.2016.7841759.

  17. 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.

  18. Anand A, de 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 

  19. Li W, Jin S (2021) Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity. J Supercomput. https://doi.org/10.1007/s11227-021-03781-w

    Article  Google Scholar 

  20. Kleinrock L (1975) Queueing systems theory, vol 1. Wiley, New York

    MATH  Google Scholar 

  21. Lal S, Ravidas S, Oliver I, Taleb T (2017) Assuring virtual network function image integrity and host sealing in Telco cloud. In: IEEE International Conference on Communications (ICC), pp. 1–6. https://doi.org/10.1109/ICC.2017.7997299.

Download references

Acknowledgements

This work was supported by the National Council for Scientific and Technological Development (CNPq) Project No. 433142/2018-9, 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

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marcos Falcao.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Falcao, M., Souza, C.B., Balieiro, A. et al. An analytical framework for URLLC in hybrid MEC environments. J Supercomput 78, 2245–2264 (2022). https://doi.org/10.1007/s11227-021-03945-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03945-8

Keywords

Navigation