Dual-label aware service replacement for interaction quality improvement in heterogeneous MEC system


Interaction quality is an important factor for service provision to achieve better user experience. Mobile Edge Computing (MEC) is a promising paradigm to improve interaction quality by supporting near data computing at the edges. However, the limited resources at the edge nodes make it hard to response various services simultaneously, while the service load changes over time. Hence, it is important and challengeable to utilize the limited edge resources to host various service and reduce service response time to improve interaction quality. In this paper, we investigate the service replacement problem to adjust the edge resource utilization dynamically and then reduce the service response time. We first propose a priority placement (2P) algorithm to place the services among the edges by taking account the service priority, which indicates the influence for response time reduction. Then, we propose a dual-label aware service replacement (D-LASR) algorithm to achieve dynamic service placement to fit the service load variation. The replacement strategy works based on the delay sensitivity label and the load gradient label, which represent the features how the service location and service load affect the service response time. We conduct extensive simulations and the experimental results show that the D-LASR algorithm can reduce the average service response time by 40–60%, which indicates that the D-LASR algorithm has better performance in improving interaction quality for service provision in MEC system.

This is a preview of subscription content, access via your institution.

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


  1. De Cristofaro, E., Soriente, C.: Participatory privacy: enabling privacy in participatory sensing. IEEE Netw. 27(1), 32–36 (2013)

    Article  Google Scholar 

  2. Farhadi, V., Mehmeti, F., He, T., Porta, T.L., Khamfroush, H., Wang, S., Chan, K.S.: Service placement and request scheduling for data-intensive applications in edge clouds. In: IEEE INFOCOM 2019—IEEE Conference on Computer Communications, pp. 1279–1287 (2019)

  3. Ha, K., Abe, Y., Chen, Z., Hu, W., Amos, B., Pillai, P., Satyanarayanan, M.: Adaptive VM handoff across cloudlets. Technical Report CMU-CS-15-113 (2015)

  4. He, T., Khamfroush, H., Wang, S., La Porta, T., Stein, S.: It’s hard to share: Joint service placement and request scheduling in edge clouds with sharable and non-sharable resources. In: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pp. 365–375 (2018)

  5. Hu, B., Chen, J., Li, F.: A dynamic service allocation algorithm in mobile edge computing. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC), pp. 104–109 (2017)

  6. Islam, M., Razzaque, A., Islam, J.: A genetic algorithm for virtual machine migration in heterogeneous mobile cloud computing. In: 2016 International Conference on Networking Systems and Security (NSysS), IEEE, pp. 1–6 (2016)

  7. Ksentini, A., Taleb, T., Chen, M.: A markov decision process-based service migration procedure for follow me cloud. In: 2014 IEEE International Conference on Communications (ICC), pp. 1350–1354 (2014)

  8. Li, Y., Wang, S.: An energy-aware edge server placement algorithm in mobile edge computing. In: 2018 IEEE International Conference on Edge Computing (EDGE), pp. 66–73 (2018)

  9. Li, X., Wu, J., Tang, S., Lu, S.: Let’s stay together: towards traffic aware virtual machine placement in data centers. In: IEEE INFOCOM 2014—IEEE Conference on Computer Communications, pp. 1842–1850 (2014)

  10. Li, X., Lian, Z., Qin, X., Abawajyz, J.: Delay-aware resource allocation for data analysis in cloud-edge system, pp. 816–823 (2018)

  11. Lingjun, P., Lei Jiao, X., Chen, L.W., Xie, Q., Jingdong, X.: Online resource allocation, content placement and request routing for cost-efficient edge caching in cloud radio access networks. IEEE J. Select. Areas Commun. 36(8), 1751–1767 (2018)

    Article  Google Scholar 

  12. Pasteris, S., Wang, S., Herbster, M., He, T.: Service placement with provable guarantees in heterogeneous edge computing systems. In: IEEE INFOCOM 2019—IEEE Conference on Computer Communications, pp. 514–522 (2019)

  13. Perera, C., Qin, Y., Estrella, J.C., Reiff-Marganiec, ., Vasilakos, A.V.: Fog computing for sustainable smart cities: a survey. ACM Comput. Surv., 50(3), (2017)

  14. Poularakis, K., Llorca, J., Tulino, A. M., Taylor, I., Tassiulas L.: Joint service placement and request routing in multi-cell mobile edge computing networks. In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, pp. 10–18 (2019)

  15. Qiu, J., Li, X., Qin, X., Wang, H., Cheng, Yo.: Utility-aware edge server deployment in mobile edge computing. Presented at the (2019)

  16. Reiter, A., Prünster, B., Zefferer, T.: Hybrid mobile edge computing: Unleashing the full potential of edge computing in mobile device use cases. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 935–944 (2017)

  17. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)

    Article  Google Scholar 

  18. Taleb, T., Dutta, S., Ksentini, A., Iqbal, M., Flinck, H.: Mobile edge computing potential in making cities smarter. IEEE Commun. Mag. 55(3), 38–43 (2017)

    Article  Google Scholar 

  19. Teng, M., Li, X., Qin, X., Wu, J.: Priority based service placement strategy in heterogeneous mobile edge computing. Presented at the (2020)

  20. Wang, S., Zafer, M., Leung, K.K.: Online placement of multi-component applications in edge computing environments. IEEE Access 5, 2514–2533 (2017)

    Article  Google Scholar 

  21. Wang, L., Jiao, L., He, T., Li, J., Mühlhäuser, M.: Service entity placement for social virtual reality applications in edge computing. In: IEEE INFOCOM 2018—IEEE Conference on Computer Communications, pp. 468–476 (2018)

  22. Xu, J., Chen, L., Zhou, P.: Joint service caching and task offloading for mobile edge computing in dense networks. In: IEEE INFOCOM 2018—IEEE Conference on Computer Communications, pp. 207–215 (2018)

  23. Yu, R., Kilari, V.T., Xue, G., Yang, D.: Load balancing for interdependent iot microservices. In: IEEE INFOCOM 2019—IEEE Conference on Computer Communications, pp. 298–306 (2019)

  24. Zhang, X., Wu, C., Li, Z., Lau, F.C.M.: Online cost minimization for operating geo-distributed cloud cdns. In: 2015 IEEE 23rd International Symposium on Quality of Service (IWQoS), IEEE, pp. 21–30 (2015)

Download references


This work is supported in part by the National Key R&D Program of China under Grant 2019YFB2102002, in part by the National Natural Science Foundation of China under Grant 61802182, and in part by the Collaborative Innovation Center of Novel Software Technology and Industrialization.

Author information



Corresponding author

Correspondence to Xin Li.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, X., Teng, M., Wu, J. et al. Dual-label aware service replacement for interaction quality improvement in heterogeneous MEC system. CCF Trans. Pervasive Comp. Interact. 3, 129–146 (2021). https://doi.org/10.1007/s42486-021-00066-2

Download citation


  • Heterogeneity
  • MEC
  • Service replacement
  • Interaction quality