Skip to main content
Log in

SDN-based offloading policy to reduce the delay in fog-vehicular networks

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

A Correction to this article was published on 09 March 2021

This article has been updated

Abstract

Integrating fog computing with vehicular networks led to the rapidly growing demands of vehicle applications regarding computation-intensive and low response time with meeting the request deadline. The limited resources of the fog node have made it unable to meet the demands of such applications. Offloading the requests to other Off-Load Destination (OLD) is a suitable solution for the fog node to deal with these demands. Nonetheless, this simultaneously faces two challenges. The first challenge is the offloading to a nearby fog node which stills not the fully efficient choice when this nearby fog node is busy. The second challenge is the selection decision of the optimal OLD where the fog node incurs additional burden through getting status information of all neighboring fog nodes, affecting the selection decision, which is why it may not fulfill the request deadline. To solve the first challenge, a new hybrid offloading architecture has been proposed, where the underutilized resources of Vehicular Fog Computing (VFC) are joined with the cloud to be an OLD, thus increase the processing chance of the offloaded requests. The second challenge has been solved by optimizing the selection decision of the fog node via taking the global network resources benefit of Software Defined Network (SDN) in the proposed offloading architecture to design an SDN-based offloading policy. The selection decision problem is formulated as a Binary-Linear Programming and solved by CPLEX software. The simulation results show that our proposed improves the performance of the fog node by providing less response time and significantly outperforming other offloading policies.

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
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Change history

References

  1. Biswas S, Tatchikou R, Dion F (2006) Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety. IEEE Commun Mag 44(1):74–82

    Article  Google Scholar 

  2. Saini M, Alelaiwi A, Saddik AE (2015) How close are we to realizing a pragmatic VANET solution? A meta-survey. ACM Computing Surveys (CSUR) 48(2):29

    Article  Google Scholar 

  3. Kai K, Cong W, Tao L (2016) Fog computing for vehicular ad-hoc networks: paradigms, scenarios, and issues. The Journal of China Universities of Posts and Telecommunications 23(2):56–96

    Article  Google Scholar 

  4. Stojmenovic I (2014) Fog computing: a cloud to the ground support for smart things and machine-to-machine networks. In: Telecommunication Networks and Applications Conference (ATNAC), 2014 Australasian, pp 117-122. IEEE

  5. Stojmenovic I, Wen S (2014) The fog computing paradigm: scenarios and security issues. In: Computer Science and Information Systems (FedCSIS), Federated Conference on 2014, pp 1-8. IEEE

  6. Hu P, Dhelim S, Ning H, Qiu T (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl 98:27–42

    Article  Google Scholar 

  7. Aazam M, Zeadally S, Harras KA (2018) Offloading in fog computing for IoT: review, enabling technologies, and research opportunities. Futur Gener Comput Syst 87:278–289

    Article  Google Scholar 

  8. Mukherjee M, Shu L, Wang D (2018) Survey of fog computing: fundamental, network applications, and research challenges. IEEE Commun Surv Tutor 20(3):1826–1857

    Article  Google Scholar 

  9. Mukherjee M, Kumar S, Zhang Q, Matam R, Mavromoustakis CX, Lv Y, Mastorakis G (2019) Task data offloading and resource allocation in fog computing with multi-task delay guarantee. IEEE Access 7:152911–152918

    Article  Google Scholar 

  10. Zhu C, Pastor G, Xiao Y, Ylajaaski A (2018) Vehicular fog computing for video crowdsourcing: applications, feasibility, and challenges. IEEE Commun Mag 56(10):58–63

    Article  Google Scholar 

  11. Yousefpour A, Patil A, Ishigaki G, Kim I, Wang X, Cankaya HC, Zhang Q, Xie W, Jue JP (2018) QoS-aware dynamic fog service provisioning. arXiv preprint, arXiv:1802.00800. [Online]. Available: https://arxiv.org/abs/1802.00800

  12. Mukherjee M, Kumar S, Mavromoustakis CX, Mastorakis G, Matam R, Kumar V, Zhang Q (2019) Latency-driven parallel task data offloading in fog computing networks for industrial applications. IEEE Transactions on Industrial Informatics

  13. Xiao Y, Krunz M (2017) QoE and power efficiency tradeoff for fog computing networks with fog node cooperation. In: INFOCOM 2017-IEEE Conference on Computer Communications, IEEE 2017, pp 1-9. IEEE

  14. Kadhim AJ, Seno SAH (2018) Maximizing the utilization of fog computing in internet of vehicle using SDN. IEEE Commun Lett 23(1):140–143

    Article  Google Scholar 

  15. Grover J, Jain A, Singhal S, Yadav A (2018) Real-time VANET applications using fog computing. In: Proceedings of First International Conference on Smart System, Innovations and Computing 2018, pp 683-691. Springer

  16. Hou X, Li Y, Chen M, Wu D, Jin D, Chen S (2016) Vehicular fog computing: a viewpoint of vehicles as the infrastructures. IEEE Trans Veh Technol 65(6):3860–3873

    Article  Google Scholar 

  17. Li C, Qin Z, Novak E, Li Q (2017) Securing SDN infrastructure of IoT–fog networks from MitM attacks. IEEE Internet Things J 4(5):1156–1164

    Article  Google Scholar 

  18. Wickboldt JA, De Jesus WP, Isolani PH, Both CB, Rochol J, Granville LZ (2015) Software-defined networking: management requirements and challenges. IEEE Commun Mag 53(1):278–285

    Article  Google Scholar 

  19. Tomovic S, Yoshigoe K, Maljevic I, Radusinovic I (2017) Software-defined fog network architecture for iot. Wirel Pers Commun 92(1):181–196

    Article  Google Scholar 

  20. Jiang C, Cheng X, Gao H, Zhou X, Wan J (2019) Toward computation offloading in edge computing: a survey. IEEE Access 7:131543–131558

    Article  Google Scholar 

  21. Zhang H, Zhang Q, Du X (2015) Toward vehicle-assisted cloud computing for smartphones. IEEE Trans Veh Technol 64(12):5610–5618

    Article  Google Scholar 

  22. Zhang W, Zhang Z, Chao H-C (2017) Cooperative fog computing for dealing with big data in the internet of vehicles: architecture and hierarchical resource management. IEEE Commun Mag 55(12):60–67

    Article  Google Scholar 

  23. He X, Ren Z, Shi C, Fang J (2016) A novel load balancing strategy of software-defined cloud/fog networking in the internet of vehicles. China Commun 13(Supplement2):140–149

    Article  Google Scholar 

  24. Yousefpour A, Ishigaki G, Gour R, Jue JP (2018) On reducing iot service delay via fog offloading. IEEE Internet Things J 5:998–1010

    Article  Google Scholar 

  25. Wang X, Ning Z, Wang L (2018) Offloading in internet of vehicles: a fog-enabled real-time traffic management system. IEEE Trans Industr Inform 14(10):4568–4578

    Article  Google Scholar 

  26. He Z, Zhang D, Liang J (2016) Cost-efficient sensory data transmission in heterogeneous software-defined vehicular networks. IEEE Sensors J 16(20):7342–7354

    Article  Google Scholar 

  27. LiWang M, Dai S, Gao Z, Du X, Guizani M, Dai H (2019) A computation offloading incentive mechanism with delay and cost constraints under 5G satellite-ground IoV architecture. IEEE Wirel Commun 26:124–132

    Article  Google Scholar 

  28. Du J, Yu FR, Chu X, Feng J, Lu G (2018) Computation offloading and resource allocation in vehicular networks based on dual-side cost minimization. IEEE Trans Veh Technol 68(2):1079–1092

    Article  Google Scholar 

  29. Zhang K, Mao Y, Leng S, He Y, Zhang Y (2017) Mobile-edge computing for vehicular networks: a promising network paradigm with predictive off-loading. IEEE Veh Technol Mag 12(2):36–44

    Article  Google Scholar 

  30. Liu L, Chang Z, Guo X, Mao S, Ristaniemi T (2018) Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J 5(1):283–294

    Article  Google Scholar 

  31. Pham X-Q, Nguyen T-D, Nguyen V, Huh E-N (2019) Joint node selection and resource allocation for task offloading in scalable vehicle-assisted multi-access edge computing. Symmetry 11(1):58

    Article  Google Scholar 

  32. Zhuang W, Ye Q, Lyu F, Cheng N, Ren J (2019) SDN/NFV-empowered future IoV with enhanced communication, computing, and caching. Proc IEEE 108(2):274–291

    Article  Google Scholar 

  33. Truong NB, Lee GM, Ghamri-Doudane Y (2015) Software defined networking-based vehicular adhoc network with fog computing. In: Integrated Network Management (IM), 2015 IFIP/IEEE International Symposium on 2015, pp 1202-1207. IEEE

  34. Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on Mobile cloud computing 2012, pp 13-16. ACM

  35. Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: A taxonomy, survey and future directions. In: Internet of everything, pp 103–130. Springer

  36. Wu Y, Wu J, Chen L, Yan J, Luo Y (2020) Efficient task scheduling for servers with dynamic states in vehicular edge computing. Comput Commun 150:245–253

    Article  Google Scholar 

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

    MATH  Google Scholar 

  38. Du J, Zhao L, Feng J, Chu X (2018) Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee. IEEE Trans Commun 66(4):1594–1608

    Article  Google Scholar 

  39. Guo H, Liu J (2018) Collaborative computation offloading for multiaccess edge computing over fiber–wireless networks. IEEE Trans Veh Technol 67(5):4514–4526

    Article  Google Scholar 

  40. Reis AB, Sargento S, Tonguz OK (2017) Parked cars are excellent roadside units. IEEE Trans Intell Transp Syst 18(9):2490–2502

    Article  Google Scholar 

  41. Xiao Y, Zhu C (2017) Vehicular fog computing: vision and challenges. In: Pervasive Computing and Communications Workshops (PerCom workshops), 2017 IEEE International Conference on 2017, pp 6-9. IEEE

  42. Menon VG, Joe Prathap P (2017) Moving from vehicular cloud computing to vehicular fog computing: issues and challenges. Int J Comput Sci Eng 9(2)

  43. Kim OTT, Nguyen V, Hong CS (2014) Which network simulation tool is better for simulating vehicular ad-hoc network? In: Proceedings of the Korean Information Science Society 2014, pp 930-932

  44. Hazewinkel M (2001) Greedy algorithm. Encyclopedia of Mathematics

  45. Gutin G, Yeo A, Zverovich A (2002) Traveling salesman should not be greedy: domination analysis of greedy-type heuristics for the TSP. Discret Appl Math 117(1–3):81–86

    Article  MathSciNet  Google Scholar 

  46. Albu-Salih AT, Seno SAH (2018) Energy-efficient data gathering framework-based clustering via multiple UAVs in deadline-based WSN applications. IEEE Access 6:72275–72286

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seyed Amin Hosseini Seno.

Additional information

Publisher’s note

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

The original online version of this article was revised: The corrections to the corresponding author and the name of the 2nd author were not carried-out in the published version.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khadir, A.A., Seno, S.A.H. SDN-based offloading policy to reduce the delay in fog-vehicular networks. Peer-to-Peer Netw. Appl. 14, 1261–1275 (2021). https://doi.org/10.1007/s12083-020-01066-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-01066-2

Keywords

Navigation