Exploring Intelligent Service Migration in Vehicular Networks

  • Onyekachukwu A. EzenwigboEmail author
  • Vishnu Vardhan Paranthaman
  • Glenford Mapp
  • Ramona Trestian
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 270)


Mobile edge clouds have great potential to address the challenges in vehicular networks by transferring storage and computing functions to the cloud. This brings many advantages of the cloud closer to the mobile user, by installing small cloud infrastructures at the network edge. However, it is still a challenge to efficiently utilize heterogeneous communication and edge computing architectures. In this paper, we investigate the impact of live service migration within a Vehicular Ad-hoc Network environment by making use of the results collected from a real experimental test-bed. A new proactive service migration model which considers both the mobility of the user and the service migration time for different services is introduced. Results collected from a real experimental test-bed of connected vehicles show that there is a need to explore proactive service migration based on the mobility of users. This can result in better resource usage and better Quality of Service for the mobile user. Additionally, a study on the performance of the transport protocol and its impact in the context of live service migration for highly mobile environments is presented with results in terms of latency, bandwidth, and burst and their potential effect on the time it takes to migrate services.


Edge Computing Service migration Vehicular Ad-hoc Network Quality of Service 


  1. 1.
    Barbarossa, S., Sardellitti, S., Lorenzo, P.D.: Communicating while computing: distributed mobile cloud computing over 5G heterogeneous networks. IEEE Signal Process. Mag. 31(6), 45–55 (2014). Scholar
  2. 2.
    Femminella, M., Pergolesi, M., Reali, G.: Performance evaluation of edge cloud computing system for big data applications. In: 2016 5th IEEE International Conference on Cloud Networking (Cloudnet), pp. 170–175, October 2016.
  3. 3.
    Dinh, H.T., Lee, C., Niyato, D., Wang, P.: A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 13, 1587–1611 (2013)CrossRefGoogle Scholar
  4. 4.
    Hussain, R., Son, J., Eun, H., Kim, S., Oh, H.: Rethinking vehicular communications: Merging VANET with cloud computing. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp. 606–609, December 2012.
  5. 5.
    Imagane, K., Kanai, K., Katto, J., Tsuda, T.: Evaluation and analysis of system latency of edge computing for multimedia data processing. In: 2016 IEEE 5th Global Conference on Consumer Electronics, pp. 1–2, October 2016.
  6. 6.
    Kamiyama, N., Nakano, Y., Shiomoto, K., Hasegawa, G., Murata, M., Miyahara, H.: Analyzing effect of edge computing on reduction of web response time. In: 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–6, December 2016.
  7. 7.
    Kikuchi, J., Wu, C., Ji, Y., Murase, T.: Mobile edge computing based VM migration for QoS improvement. In: 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), pp. 1–5, October 2017.
  8. 8.
    Kim, S.Y., de Foy, X., Reznik, A.: Practical service allocation in mobile edge computing systems. In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), pp. 1–6, November 2017.
  9. 9.
    Lertsinsrubtavee, A., Ali, A., Molina-Jimenez, C., Sathiaseelan, A., Crowcroft, J.: PiCasso: a lightweight edge computing platform. In: 2017 IEEE 6th International Conference on Cloud Networking (CloudNet), pp. 1–7, September 2017.
  10. 10.
    Li, H., Shou, G., Hu, Y., Guo, Z.: Mobile edge computing: progress and challenges. In: 2016 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), pp. 83–84, March 2016.
  11. 11.
    Mach, P., Becvar, Z.: Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun. Surv. Tutor. 19(3), 1628–1656 (2017). Scholar
  12. 12.
    Machen, A., Wang, S., Leung, K.K., Ko, B.J., Salonidis, T.: Live service migration in mobile edge clouds. IEEE Wirel. Commun. 25(1), 140–147 (2018). Scholar
  13. 13.
    Mapp, G., et al.: Exploiting location and contextual information to develop a comprehensive framework for proactive handover in heterogeneous environments. J. Comput. Netw. Commun. 2012, 1–17, Article ID 748163 (2012). Scholar
  14. 14.
    Mapp, G., Gosh, A., Paranthaman, V.V., Iniovosa, V.O., Loo, J., Vinel, A.: Exploring seamless connectivity and proactive handover techniques in VANET systems. In: Alam, M., Ferreira, J., Fonseca, J. (eds.) Intelligent Transportation Systems. SSDC, vol. 52, pp. 195–220. Springer, Cham (2016). Scholar
  15. 15.
    Paranthaman, V.V., et al.: Building a prototype VANET testbed to explore communication dynamics in highly mobile environments. In: Guo, S., Wei, G., Xiang, Y., Lin, X., Lorenz, P. (eds.) TridentCom 2016. LNICST, vol. 177, pp. 81–90. Springer, Cham (2017). Scholar
  16. 16.
    Khan, A.U.R., Othman, M., Madani, S.A., Khan, S.U.: A survey of mobile cloud computing application models. IEEE Commun. Surv. Tutor. 16(1), 393–413 (2014). Scholar
  17. 17.
    Ren, J., Guo, H., Xu, C., Zhang, Y.: Serving at the edge: a scalable IoT architecture based on transparent computing. IEEE Netw. 31(5), 96–105 (2017). Scholar
  18. 18.
  19. 19.
    Rimal, B.P., Van, D.P., Maier, M.: Mobile edge computing empowered fiber-wireless access networks in the 5G era. IEEE Commun. Mag. 55(2), 192–200 (2017). Scholar
  20. 20.
    Sardis, F.: Exploring traffic and QoS management mechanisms to support mobile cloud computing using service localisation in heterogeneous environments. Ph.D. thesis (2014)Google Scholar
  21. 21.
    Sardis, F., Mapp, G., Loo, J., Aiash, M., Vinel, A.: On the investigation of cloud-based mobile media environments with service-populating and QoS-aware mechanisms. IEEE Trans. Multimed. 15(4), 769–777 (2013). Scholar
  22. 22.
    Sun, X., Ansari, N.: EdgeIoT: mobile edge computing for the Internet of Things. IEEE Commun. Mag. 54(12), 22–29 (2016). Scholar
  23. 23.
    Tran, T.X., Hajisami, A., Pandey, P., Pompili, D.: Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun. Mag. 55(4), 54–61 (2017). Scholar
  24. 24.
    Yu, Y.: Mobile edge computing towards 5G: vision, recent progress, and open challenges. China Commun. 13(Suppl. 2), 89–99 (2016). Scholar
  25. 25.
    Zhang, K., Mao, Y., Leng, S., Vinel, A., Zhang, Y.: Delay constrained offloading for mobile edge computing in cloud-enabled vehicular networks. In: 2016 8th International Workshop on Resilient Networks Design and Modeling (RNDM), pp. 288–294, September 2016.

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Onyekachukwu A. Ezenwigbo
    • 1
    Email author
  • Vishnu Vardhan Paranthaman
    • 1
  • Glenford Mapp
    • 1
  • Ramona Trestian
    • 1
  1. 1.Faculty of Science and TechnologyMiddlesex UniversityLondonUK

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