Skip to main content
SpringerLink
Log in

SLA-based service provisioning approach in vehicular cloud network

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Vehicular cloud network (VCN) enables the vehicles to act as servers in the cloud environment, considering the abundance of computing and storage resources. Service provisioning may be challenging due to the characteristic of VCN such as service pricing, resource variability, and resource mobility. In this regard, the most important challenges of service provisioning are quality of service (QoS), service availability, and fair/constant pricing. Previous research approaches to service provisioning in VCN involve one or two of the challenges above and none of these approaches are comprehensive. In this paper, we proposed a comprehensive approach to service provisioning in VCN, considering all the challenges above. The proposed approach consists of algorithms to improve the quality of service, service availability, and fair/constant pricing. The outcome of the research is an efficient service provisioning approach and a service level agreement (SLA) in the VCN environment. The proposed approach is evaluated by simulation. The results of simulating various scenarios indicate improvements in quality of service and service availability indicators. It also shows that a fair pricing mechanism has been used.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. The variable i is the provider identification number.

  2. Variables i is the provider’s identification and j is the requester’s identification.

References

  1. Guerrero-ibanez, J.A., Zeadally, S., Contreras-Castillo, J.: Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies. IEEE Wirel. Commun. 22, 122–128 (2015)

    Article  Google Scholar 

  2. Olariu, S., Khalil, I., Abuelela, M.: Taking VANET to the clouds. J. Pervasive Comput. Commun. 7, 7–21 (2011)

    Article  Google Scholar 

  3. Yu, R., Zhang, Y., Gjessing, S., Xia, W., Yang, K.: Toward cloud-based vehicular networks with efficient resource management. IEEE Netw. 27, 48–55 (2013)

    Article  Google Scholar 

  4. uz Zaman, S.K., Jehangiri, A.I., Maqsood, T., Ahmad, Z., Umar, A.I., Shuja, J., Alanazi, E., Alasmary, W.: Mobility-aware computational offloading in mobile edge networks: a survey. Clust. Comput. (2021). https://doi.org/10.1007/s10586-021-03268-6

    Article  Google Scholar 

  5. Salahuddin, M., Ala, A., Guizani, M.: Reinforcement learning for resource provisioning in the vehicular cloud. IEEE Wirel. Commun. 23, 128–135 (2016)

    Article  Google Scholar 

  6. Yu, Z., Xie, J., Tang, Y., Xiao, L.: SMDP based cross-area resource management for vehicular cloud networks. In: 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), pp. 1–5 (2019). https://doi.org/10.1109/VTCSpring.2019.8746421

  7. Bitam, S., Mellouk, A., Zeadally, S.: VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks. IEEE Wirel. Commun. 22, 96–102 (2015)

    Article  Google Scholar 

  8. Adhikary, T., Das, A.K., Razzaque, M.A., Almogren, A., Alrubaian, M., Hassan, M.M.: Quality of service aware reliable task scheduling in vehicular cloud computing. Mobile Netw. Appl. 21, 482–493 (2016)

    Article  Google Scholar 

  9. Boukerche, A., Meneguette, R.I.: Vehicular cloud network: a new challenge for resource management based systems. In: 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 159–164 (2017). https://doi.org/10.1109/IWCMC.2017.7986279

  10. Mekki, T., Jabri, I., Rachedi, A., Ben Jemaa, M.: Vehicular cloud networks: challenges, architectures, and future directions title. Vehicular Commun. 9, 268–280 (2017)

    Article  Google Scholar 

  11. Peng, X., Ota, K., Dong, M.: Multiattribute-based double auction toward resource allocation in vehicular fog computing. IEEE Internet Things J. 7, 3094–3103 (2020)

    Article  Google Scholar 

  12. Kaleibar, F.J., Abbaspour, M.: TOPVISOR: two-level controller-based approach for service advertisement and discovery in vehicular cloud network. Int. J. Commun. Syst. 33, e4197 (2020). https://doi.org/10.1002/dac.4197

    Article  Google Scholar 

  13. Dashora, C., Sudhagar, P.E., Marietta, J.: IoT based framework for the detection of vehicle accident. Clust. Comput. 23(1235–1250), 1235–1250 (2020). https://doi.org/10.1007/s10586-019-02989-z

    Article  Google Scholar 

  14. Aloqaily, I.M., Kantarci, B., Hussein, T.M.: A generalized framework for quality of experience (QoE)-based provisioning in a vehicular cloud. 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB), 1-5 (2015). https://doi.org/10.1109/ICUWB.2015.7324403

  15. Ridhawi, I., Aloqaily, I.M., Kantarci, B., Jararweh, Y., Mouftah, H.T.: A continuous diversified vehicular cloud service availability framework for smart cities. Comput. Netw. 145, 207–218 (2018). https://doi.org/10.1016/j.comnet.2018.08.023

    Article  Google Scholar 

  16. Arkian, H.R., Atani, R.E., Diyanat, A.: A cluster-based vehicular cloud architecture with learning-based resource management. J. Supercomput. 71, 1401–1426 (2015). https://doi.org/10.1007/s11227-014-1370-z

    Article  Google Scholar 

  17. Tamani, N., Brik, B., Lagraa, N., Ghamri-Doudane, Y.: On link stability metric and fuzzy quantification for service selection in mobile vehicular cloud. IEEE Trans. Intell. Transp. Syst. 21, 2050–2062 (2020). https://doi.org/10.1109/TITS.2019.2911860

    Article  Google Scholar 

  18. Brik, B., Ahmad Khan, J., Ghamri-Doudane, Y., Lagraa, N., Lakas, A.: GSS-VC: a game-theoretic approach for service selection in vehicular cloud. In: 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1-6 (2018). https://doi.org/10.1109/CCNC.2018.8319223

  19. Mishra, S., Mishra, S.K., Sahoo, B., Obaidat, M.S., Puthal, D.: First score auction for pricing-based resource selection in vehicular cloud. In: 2018 International Conference on Computer, Information and Telecommunication Systems (CITS), pp.  1–5 (2018). https://doi.org/10.1109/CITS.2018.8440180

  20. Sun, Y., Guo, X., Zhou, S., Jiang, Z., Liu, X., Niu, Z.: Learning-based task offloading for vehicular cloud computing systems. In: 2018 IEEE International Conference on Communications (ICC), pp. 1–7 (2018). https://doi.org/10.1109/ICC.2018.8422661

  21. Bhoi, S.K., Panda, S.K., Ray, S.R., Sethy, R.K., Sahoo, V.K., Sahu, B.P., Khilar, P.M.: TSP-HVC: a novel task scheduling policy for heterogeneous vehicular cloud environment. Int. J. Inf. Technol. 11, 853–858 (2019). https://doi.org/10.1007/s41870-018-0148-6

    Article  Google Scholar 

  22. Sookhtsaraei, R., Iraji, M., Artin, J., Iraji, M.S.: Increasing the quality of services and resource utilization in vehicular cloud computing using best host selection methods. Clust. Comput. (2020). https://doi.org/10.1007/s10586-020-03159-2

    Article  Google Scholar 

  23. Bondi, A.B.: Characteristics of scalability and their impact on performance. In: Proceedings of the 2nd international workshop on Software and performance, pp. 95–203 (2000). https://doi.org/10.1145/350391.350432

  24. Kashfi, H., Aliee, F.S.: Security challenges of vehicular cloud computing applications: from software architecture viewpoint. Comput. Model. New Technol. 21, 20–24 (2017)

    Google Scholar 

  25. Sharma, S., Chang, V., Tim, U.S., Wong, J., Gadia, S.: Cloud and IoT-based emerging services systems. Clust. Comput. 22, 71–91 (2019). https://doi.org/10.1007/s10586-018-2821-8

    Article  Google Scholar 

  26. Boukerche, A., Robson, E.: Vehicular cloud computing: architectures, applications, and mobility. Comput. Netw. 135, 171–189 (2018). https://doi.org/10.1016/j.comnet.2018.01.004

    Article  Google Scholar 

  27. McCanne, S., Floyd, S.: Network simulator NS-2 (1997). http://www.isi.edu/nsnam/ns/. Accessed 17 July 2021

  28. SUMO-Simulation of Urban Mobility. Centre for Applied Informatics, Institute of Transport Research, German Aerospace Centre http://sumo.sourceforge.net/. Accessed 17 July 2021

  29. Extensible editor for OpenStreetMap (OSM) for java. https://josm.openstreetmap.de. Accessed 17 July 2021

Download references

Author information

Authors and Affiliations

  1. Shahid Beheshti University, Tehran, Iran

    Farhoud Jafari Kaleibar & Maghsoud Abbaspour

Authors
  1. Farhoud Jafari Kaleibar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maghsoud Abbaspour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maghsoud Abbaspour.

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

Jafari Kaleibar, F., Abbaspour, M. SLA-based service provisioning approach in vehicular cloud network. Cluster Comput 24, 3693–3708 (2021). https://doi.org/10.1007/s10586-021-03357-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03357-6

Keywords

Search

Navigation