Context-Awareness Enhances 5G MEC Resource Allocation

  • Shihyang LinEmail author
  • Jieqin Wan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1080)


The concept of MEC is to smoothly integrate cloud capabilities into the mobile network architecture. It enables multi-operator operation for V2X mobiles/users to provide convenience service continuity across access network coverage and across borders of different operators’ networks. Based on the requirements of traffic prediction and adaptive traffic control system, some computational tasks can offload to MEC server. Considering the network bandwidth, storage capacity, and processor performance, we need a resource allocation mechanism to distribute tasks to MEC server in balance. Otherwise, some tasks may be queued on a MEC server for a moment because there are too many tasks allocated to the same MEC serve at the same time. In order to resolve the problem, the Context-Awareness MEC Resource Allocation (CARA) mechanism is proposed in this paper. The evaluated results show that the CARA can balance resource allocation to every MEC server.


5G MEC Offloading Resource allocation 


  1. 1.
    ETSI ISG.: MEC 002 V2.1.1 Multi-access Edge Computing (MEC); Phase 2: Use Cases and Requirements. Group specification (2018)Google Scholar
  2. 2.
    ETSI ISG.: MEC 003 V2.1.1 Multi-access Edge Computing (MEC); Framework and Reference Architecture. Group specification (2019)Google Scholar
  3. 3.
    ETSI ISG.: MEC-IEG 004 V1.1.1 Mobile-Edge Computing (MEC); Service Scenarios. Group specification (2015)Google Scholar
  4. 4.
    ETSI ISG.: MEC 010-1 V1.1.1 Mobile Edge Computing (MEC); Mobile Edge Management; Part 1: System host and platform management. Group specification (2017)Google Scholar
  5. 5.
    ETSI ISG.: MEC 010-2 V1.1.1 Mobile Edge Computing (MEC); Mobile Edge Management; Part 2: Application lifecycle, rules and requirements management. Group specification (2017)Google Scholar
  6. 6.
    ETSI ISG.: MEC 011 V1.1.1 Mobile Edge Computing (MEC); Mobile Edge Platform Application Enablement. Group specification (2017)Google Scholar
  7. 7.
    ETSI ISG.: MEC 012 V1.1.1 2Mobile Edge Computing (MEC); Radio Network Information. Group specification (2017)Google Scholar
  8. 8.
    ETSI ISG.: MEC 013 V1.1.1 Mobile Edge Computing (MEC); Location API. Group specification (2018)Google Scholar
  9. 9.
    ETSI ISG.: MEC 014 V1.1.1 Mobile Edge Computing (MEC); UE Identity API. Group specification (2018)Google Scholar
  10. 10.
    ETSI ISG.: MEC 015 V1.1.1 Mobile Edge Computing (MEC); Bandwidth Management API. Group specification (2017)Google Scholar
  11. 11.
    ETSI ISG.: MEC 016 V2.1.1 Multi-access Edge Computing (MEC); UE application interface. Group specification (2019)Google Scholar
  12. 12.
    ETSI ISG.: MEC 022 V2.1.1 Multi-access Edge Computing (MEC); Study on MEC Support for V2X Use Cases. Group report (2018)Google Scholar
  13. 13.
    Mach, P., Becvar, Z.: Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun. Surv. Tutor. 19(3), 1628–1656 (2017)CrossRefGoogle Scholar
  14. 14.
    FP7 European Project, Distributed Computing, Storage and Radio Resource Allocation Over Cooperative Femtocells (TROPIC).
  15. 15.
    Lobillo, F., et al.: An architecture for mobile computation offloading on cloud-enabled LTE small cells. In: IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Istanbul, pp. 1–6. IEEE Press (2014)Google Scholar
  16. 16.
    Wang, S., et al.: Mobile micro-cloud: application classification, mapping, and deployment. In: Fall Meeting ITA (AMITA), New York (2013)Google Scholar
  17. 17.
    Wang, K., Shen, M., Cho, J.: MobiScud: a fast moving personal cloud in the mobile network. In: 5th Workshop on All Things Cellular: Operations, Applications and Challenges, London, pp. 19–24. ACM (2015)Google Scholar
  18. 18.
    Taleb, T., Ksentini, A.: Follow me cloud: interworking federated clouds and distributed mobile networks. IEEE Netw. Mag. 27(5), 12–19 (2013)CrossRefGoogle Scholar
  19. 19.
    Taleb, T., Ksentini, A., Frangoudis, P.A.: Follow-me cloud: when cloud services follow mobile users. IEEE Trans. Cloud Comput. 7(2), 369–382 (2019)CrossRefGoogle Scholar
  20. 20.
    Liu, J., Zhao, T., Zhou, S., Cheng, Y., Niu, Z.: CONCERT: a cloud based architecture for next-generation cellular systems. IEEE Wirel. Commun. Mag. 21(6), 14–22 (2014)CrossRefGoogle Scholar
  21. 21.
    Dab, B., Aitsaadi, N., Langar, R.: A novel joint offloading and resource allocation scheme for mobile edge computing. In: 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas. IEEE Press (2019)Google Scholar
  22. 22.
    Jia, M., Cao, J., Yang, L.: Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing. In: 2014 IEEE Conference on Computer Communications Workshops, Toronto, pp. 352–357. IEEE Press (2014)Google Scholar
  23. 23.
    Bouet, M., Conan, V.: Mobile edge computing resources optimization: a geo-clustering approach. IEEE Trans. Netw. Serv. Manag. 15(2), 787–796 (2018)CrossRefGoogle Scholar
  24. 24.
    Du, J., Yu, F.R., Chu, X., Feng, J., Lu, G.: Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization. IEEE Trans. Veh. Technol. 68(2), 1079–1092 (2019)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Shandong University of TechnologyZiboChina

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