Skip to main content

Advertisement

SpringerLink
Log in

Optimal IoT Service Offloading with Uncertainty in SDN-Based Mobile Edge Computing

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

To solve the problem of limited computing ability in mobile devices, edge computing is adopted as a feasible solution which provides services for IoT devices in different geographical locations. However, due to the service uncertainties, including the network congestion and the performance degradation of edge nodes, novel offloading strategies must be developed to accommodate the uncertain situations. In view of this challenge, software-defined network (SDN) is integrated with edge computing to make service offloading more flexible. Technically, an optimal IoT service offloading (OSO) method with uncertainty is proposed. In OSO, the completion time and load balance variance are two optimization goals for developing offloading strategies, and then the non-dominated sorting genetic algorithm-II (NSGA-II) is fully investigated to improve the performance in completion time and load balance variance. Moreover, the optimal strategy is selected by using Simple Additive Weighting (SAW) and Multiple Criteria Decision Making (MCDW). Finally, the experimental evaluation is conducted by comparing OSO with other methods to verify the superiority of it.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (iot): A vision, architectural elements, and future directions. Fut Gener Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  2. Sun G, Chang V, Ramachandran M, Sun Z, Li G, Yu H, Liao D (2017) Efficient location privacy algorithm for internet of things (iot) services and applications. J Netw Comput Appl 89:3–13

    Article  Google Scholar 

  3. Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J 1(1):22–32

    Article  Google Scholar 

  4. Yang Z, Huang Y, Li X, Wang W, Wu F, Zhang X, Yao W, Zheng Z, Xiang L, Li W et al (2018) Efficient secure data provenance scheme in multimedia outsourcing and sharing. Comput Mater Cont 56(1):1–17

    Google Scholar 

  5. Xie X, Yuan T, Zhou X, Cheng X (2018) Research on trust model in container-based cloud service. Comput Mater Cont 56(2):273–283

    Google Scholar 

  6. Wang X, Yang LT, Xie X, Jin J, Deen MJ (2017) A cloud-edge computing framework for cyber-physical-social services. IEEE Commun Mag 55(11):80–85

    Article  Google Scholar 

  7. Shin M-K, Nam K-H, Kim H-J (2012) Software-defined networking (sdn): A reference architecture and open apis. In: 2012 International Conference on ICT Convergence (ICTC). IEEE, pp 360–361

  8. Qi L, Chen Y, Yuan Y, Fu S, Zhang X, Xu X (2019) A qos-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web:1–23

  9. Almohaimeed A, Asaduzzaman A (2019) Introducing edge controlling to software defined networking to reduce processing time. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, pp 0585–0590

  10. Mu noz R, Vilalta R, Yoshikane N, Casellas R, Martínez R, Tsuritani T, Morita I (2018) Integration of iot, transport sdn, and edge/cloud computing for dynamic distribution of iot analytics and efficient use of network resources. J Light Technol 36(7):1420–1428

    Article  Google Scholar 

  11. Meng S, Wang S, Wu T, Li D, Huang T, Wu X, Xu X, Dou W (2016) An uncertainty-aware evolutionary scheduling method for cloud service provisioning. In: 2016 IEEE International Conference on Web Services (ICWS). IEEE, pp 506–513

  12. Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: The communication perspective. IEEE Commun Surv Tutorials 19(4):2322–2358

    Article  Google Scholar 

  13. Ren P, Qiao X, Chen J, Dustdar S (2018) Mobile edge computing–a booster for the practical provisioning approach of web-based augmented reality. In: 2018 IEEE/ACM Symposium on Edge Computing (SEC). IEEE, pp 349–350

  14. Wang X, Yang LT, Kuang L, Liu X, Zhang Q, Deen MJ (2019) A tensor-based big-data-driven routing recommendation approach for heterogeneous networks. IEEE Netw 33(1):64–69

    Article  Google Scholar 

  15. Zhang J, Xie N, Zhang X, Yue K, Li W, Kumar D (2018) Machine learning based resource allocation of cloud computing in auction. Comput Mater Cont 56(1):123–135

    Google Scholar 

  16. Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  17. Lei L, Xu H, Xiong X, Zheng K, Xiang W (2019) Joint computation offloading and multi-user scheduling using approximate dynamic programming in nb-iot edge computing system. IEEE Internet Things J

  18. Li X, Liu S, Wu F, Kumari S, Rodrigues JJPC (2018) Privacy preserving data aggregation scheme for mobile edge computing assisted iot applications. IEEE Internet Things J

  19. Wang T, Zhang G, Liu A, Bhuiyan MZA, Jin Q (2018) A secure iot service architecture with an efficient balance dynamics based on cloud and edge computing. IEEE Internet Things J

  20. Chen L, Zhou S, Xu J (2018) Computation peer offloading for energy-constrained mobile edge computing in small-cell networks. IEEE/ACM Trans Netw 26(4):1619–1632

    Article  Google Scholar 

  21. Sarra M, Samia B, Khaled S, Mehammed D (2019) New caching system under uncertainty for mobile edge computing. In: 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, pp 129–134

  22. Li X, Li D, Wan J, Liu C, Imran M (2018) Adaptive transmission optimization in sdn-based industrial internet of things with edge computing. IEEE Internet Things J 5(3):1351–1360

    Article  Google Scholar 

  23. Sharma PK, Rathore S, Jeong Y-S, Park JH (2018) Softedgenet: Sdn based energy-efficient distributed network architecture for edge computing. IEEE Commun Mag 56(12):104–111

    Article  Google Scholar 

  24. Farris I, Taleb T, Khettab Y, Song J (2018) A survey on emerging sdn and nfv security mechanisms for iot systems. IEEE Commun Surv Tutorials 21(1):812–837

    Article  Google Scholar 

  25. Wang C, Zhang Y, Chen X, Liang K, Wang Z (2019) Sdn-based handover authentication scheme for mobile edge computing in cyber-physical systems. IEEE Internet Things J

  26. Sezer S, Scott-Hayward S, Chouhan PK, Fraser B, Lake D, Finnegan J, Viljoen N, Miller M, Rao N (2013) Are we ready for sdn? implementation challenges for software-defined networks. IEEE Commun Mag 51(7):36–43

    Article  Google Scholar 

  27. Qin Q, Poularakis K, Iosifidis G, Kompella S, Tassiulas L (2018) Sdn controller placement with delay-overhead balancing in wireless edge networks. IEEE Trans Netw Serv Manag 15(4):1446–1459

    Article  Google Scholar 

  28. Zhou W, Li L, Luo M, Chou W (2014) Rest api design patterns for sdn northbound api. In: 2014 28th international conference on advanced information networking and applications workshops. IEEE, pp 358–365

  29. Ros FJ, Ruiz PM (2014) Five nines of southbound reliability in software-defined networks. In: Proceedings of the third workshop on Hot topics in software defined networking. ACM, pp 31–36

  30. Qi L, Zhang X, Dou W, Hu C, Yang C, Chen J (2018) A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment. Futur Gener Comput Syst 88:636–643

    Article  Google Scholar 

  31. Wang X, Yang LT, Li H, Lin M, Han J, Apduhan BO (2019) Nqa: A nested anti-collision algorithm for rfid systems. ACM Trans Embedded Comput Syst (TECS) 18(4):32

    Google Scholar 

  32. Xu X, Xue Y, Qi L, Yuan Y, Zhang X, Umer T, Wan S (2019) An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles. Futur Gener Comput Syst 96:89–100

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by the National Science Foundation of China under grant no.61702277 and no.61872219. Besides, this work is also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund, and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET).

Author information

Authors and Affiliations

  1. School of Software Institute, Nanjing University, Nanjing, China

    Huizhen Hao & Qing Gu

  2. Department of Communication Engineering, Nanjing Institute of Technology, Nanjing, China

    Huizhen Hao

  3. State Key Laboratory for Novel Software, Nanjing University, Nanjing, China

    Jie Zhang & Qing Gu

Authors
  1. Huizhen Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qing Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qing Gu.

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

Hao, H., Zhang, J. & Gu, Q. Optimal IoT Service Offloading with Uncertainty in SDN-Based Mobile Edge Computing. Mobile Netw Appl 27, 2318–2327 (2022). https://doi.org/10.1007/s11036-021-01796-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-021-01796-4

Keywords

Search

Navigation