Skip to main content
SpringerLink
Log in

A QoS Based Reliable Routing Mechanism for Service Customization

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Due to the rapid development of the Internet technology such as 5G/6G and artificial intelligence, more and more new network applications appear. Customers using these applications may have different individual demands and such a trend causes great challenges to the traditional integrated service and routing model. In order to satisfy the individual demands of customers, the service customization should be considered, during which the cost of Internet Service Provider (ISP) naturally increases. Hence, how to reach a balance between the customer satisfaction and the ISP profit becomes vitally important. Targeting at addressing this critical problem, this work proposes a service customization oriented reliable routing mechanism, which includes two modules, that is, the service customization module and the routing module. In particular, the former (i.e., the service customization module) is responsible for classifying services by analyzing and processing the customer's demands. After that, the IPv6 protocol is used to implement the service customization, since it naturally supports differentiated services via the extended header fields. The latter is responsible for transforming the customized services into specific routing policies. Specifically, the Nash equilibrium based economic model is firstly introduced to make a perfect balance between the user satisfaction and the ISP profits, which could finally produce a win-win solution. After that, based on the customized service policies, an optimized grey wolf algorithm is designed to establish the routing path, during which the routing reliability is formulated and calculated. Finally, the experiments are carried out and the proposed mechanism is evaluated. The results indicate that the proposed service customization and routing mechanism improves the routing reliability, user satisfaction and ISP satisfaction by about 8.42%, 15.5% and 17.75% respectively compared with the classical open shortest path first algorithm and the function learning based algorithm.

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

Similar content being viewed by others

References

  1. Guck J W, Bemten A V, Reisslein M, Kellerer W. Unicast QoS routing algorithms for SDN: A comprehensive survey and performance evaluation. IEEE Communications Surveys & Tutorials, 2018, 20(1): 388-415. https://doi.org/10.1109/COMST.2017.2749760.

    Article  Google Scholar 

  2. Kreutz D, Ramos F M V, Veríssimo P E, Rothenberg C E, Azodolmolky S, Uhlig S. Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 2015, 103(1): 14-76. https://doi.org/10.1109/JPROC.2014.2371999.

    Article  Google Scholar 

  3. Yi B, Wang X, Li K, Sajal D, Huang M. A comprehensive survey of network function virtualization. Computer Networks, 2018, 133: 212-262. https://doi.org/10.1016/j.comnet.2018.01.021.

    Article  Google Scholar 

  4. Sun G, Xiong K, Boateng G O, Ayepah-Mensah D, Jiang W. Autonomous resource provisioning and resource customization for mixed traffics in virtualized radio access network. IEEE System Journal, 2019, 13(3): 2454-2465. https://doi.org/10.1109/JSYST.2019.2918005.

    Article  Google Scholar 

  5. Liu S, Joe-Wong C, Chen J, Brinton C G, Zheng L. Economic viability of a virtual ISP. IEEE/ACM Trans- actions on Networking, 2020, 28(2): 902-917. https://doi.org/10.1109/TNET.2020.2977198.

    Article  Google Scholar 

  6. Li J, Shi W, Yang P, Shen X. On dynamic mapping and scheduling of service function chains in SDN/NFVenabled networks. In Proc. the 2019 IEEE Global Communications Conference, Dec. 2019. https://doi.org/10.1109/GLOBECOM38437.2019.9013429.

  7. Gharbaoui M, Contoli C, Davoli G, Cuffaro G, Castoldi P. Demonstration of latency-aware and self-adaptive service chaining in 5G/SDN/NFV infrastructures. In Proc. the 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks, Nov. 2018. https://doi.org/10.1109/NFV-SDN.2018.8725645.

  8. Yu X, Ye C, Li B, Zhou H, Huang M. A deep Q-learning network for dynamic constraint-satisfied service composition. International Journal of Web Services Research, 2020, 17(4): 55-75. https://doi.org/10.4018/IJWSR.2020100104.

    Article  Google Scholar 

  9. Tomovic S, Radusinovic I. Toward a scalable, robust, and QoS-aware virtual-link provisioning in SDNbased ISP networks. IEEE Transactions on Network and Service Management, 2019, 16(3): 1032-1045. https://doi.org/10.1109/TNSM.2019.2929161.

    Article  Google Scholar 

  10. Hu G, Xu K, Wu J, Cui Y, Shi F. A general framework of source address validation and traceback for IPv4/IPv6 transition scenarios. IEEE Network, 2013, 27(6): 66-73. https://doi.org/10.1109/MNET.2013.6678929.

    Article  Google Scholar 

  11. Li T, He T, Wang Z, Zhang Y. An approach to IoT service optimal composition for mass customization on cloud manufacturing. IEEE Access, 2018, 6: 50572-50586. https://doi.org/10.1109/ACCESS.2018.2869275.

    Article  Google Scholar 

  12. Kumara I, Han J, Colman A, Heuvel W V D, Tamburri D. FM4SN: A feature-oriented approach to tenant-driven customization of multi-tenant service networks. In Proc. the 2019 IEEE International Conference on Services Computing, Jul. 2019, pp.108-115. https://doi.org/10.1109/SCC.2019.00028.

  13. Bu C, Wang X, Zhang S, Huang M. Data-driven routing service composition via requirement chain. In Proc. the 9th IEEE International Conference on Communication Software and Networks, May 2017, pp.202-206. https://doi.org/10.1109/ICCSN.2017.8230106.

  14. Masruroh S U, Robby F, Hakiem N. Performance evaluation of routing protocols RIPng, OSPFv3, and EIGRP in an IPv6 network. In Proc. the 2016 International Conference on Informatics and Computing, Oct. 2016, pp.111-116. https://doi.org/10.1109/IAC.2016.7905699.

  15. Han L, Qu Y, Dong L, Li R. Flow-level QoS assurance via IPv6 in-band signalling. In Proc. the 27th Wireless and Optical Communication Conference, April 30-May 1, 2018. 10.1109/WOCC.2018.8372726.

  16. Bhattacharya A, Sen S, Sarkar A, Debnath N C. Hierarchical graph based approach for service composition. In Proc. the 2016 IEEE International Conference on Industrial Technology, March 2016, pp.1718-1722. https://doi.org/10.1109/ICIT.2016.7475022.

  17. Xiao A, Liu Y, Li Y et al. An in-depth study of commercial MVNO: Measurement and optimization. In Proc. the 17th Annual International Conference on Mobile Systems, Applications, and Services, Jun. 2019, pp.457-469. https://doi.org/10.1145/3307334.3326070.

  18. Li Y, Zheng J, Li Z, Liu Y, Qian F, Bai S, Liu Y, Xin X. Understanding the ecosystem and addressing the fundamental concerns of commercial MVNO. IEEE/ACM Transactions on Networking, 2020, 28(3): 1364-1378. https://doi.org/10.1109/TNET.2020.2981514.

    Article  Google Scholar 

  19. Moravejosharieh A, Ahmadi K, Ahmad S. A fuzzy logic approach to increase quality of service in software defined networking. In Proc. the 2018 International Conference on Advances in Computing, Communication Control and Networking, Oct. 2018, pp.68-73. https://doi.org/10.1109/ICACCCN.2018.8748678.

  20. Yan L, Mei Y, Ma H, Zhang M. Evolutionary web service composition: A graph-based memetic algorithm. In Proc. the 2016 IEEE Congress on Evolutionary Computation, Jul. 2016, pp.201-208. https://doi.org/10.1109/CEC.2016.7743796.

  21. Yang Y, Xu M, Li Q. Fast rerouting against multilink failures without topology constraint. IEEE/ACM Transactions on Networking, 2018, 26(1): 384-397. https://doi.org/10.1109/TNET.2017.2780852.

    Article  Google Scholar 

  22. Bera S, Misra S, Vasilakos A V. Software-defined networking for Internet of Things: A survey. IEEE Internet of Things Journal, 2017, 4(6): 1994-2008. https://doi.org/10.1109/JIOT.2017.2746186.

    Article  Google Scholar 

  23. Papán J, Segeč P, Drozdová M, Mikuš L, Moravčık M, Hrabovský J. The IPFRR mechanism inspired by BIER algorithm. In Proc. the 2016 International Conference on Emerging eLearning Technologies and Applications, Nov. 2016, pp.257-262. https://doi.org/10.1109/ICETA.2016.7802053.

  24. Nobakht N, Kashi S S, Zokaei S. A reliable and delay-aware routing in RPL. In Proc. the 5th Conference on Knowledge Based Engineering and Innovation, Feb. 28-Mar. 1, 2019, pp.102-107. https://doi.org/10.1109/KBEI.2019.8734996.

  25. Behinfaraz R, Ghiasi A R. A survey on reliability analysis in controller design. In Proc. the 14th IEEE International Colloquium on Signal Processing & Its Applications, Mar. 2018, pp.198-202. https://doi.org/10.1109/CSPA.2018.8368712.

  26. Martínez-Peñas U, Kschischang F R. Reliable and secure multishot network coding using linearized reed-solomon codes. In Proc. the 56th Annual Allerton Conference on Communication, Control, and Computing, Oct. 2018, pp.702-709. https://doi.org/10.1109/ALLERTON.2018.8635644.

  27. Vignesh V, Premalatha K. Multi-QoS and interference concerned reliable routing in military information system. In Advances in Big Data and Cloud Computing, Rajsingh E B, Veerasamy J, Alavi A H, Peter J D (eds.), Springer, 2018, pp.351-360. https://doi.org/10.1007/978-981-10-7200-0_32.

  28. Milolidakis A, Fontugne R, Dimitropoulos X. Detecting network disruptions at colocation facilities. In Proc. the 2019 IEEE Conference on Computer Communications, April 29-May 2, 2019, pp.2161-2169. https://doi.org/10.1109/INFOCOM.2019.8737615.

  29. Wang H, Gu M, Qi Y, Fei H, Li J, Yong T. Large-scale and adaptive service composition using deep reinforcement learning. In Proc. the 15th International Conference on Service-Oriented Computing, Nov. 2017, pp.383-391. https://doi.org/10.1007/978-3-319-69035-3_27.

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China

    Bo Yi & Xing-Wei Wang

  2. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Min Huang

  3. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China

    Qiang He

Authors
  1. Bo Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xing-Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiang He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xing-Wei Wang.

Supplementary Information

ESM 1

(PDF 375 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yi, B., Wang, XW., Huang, M. et al. A QoS Based Reliable Routing Mechanism for Service Customization. J. Comput. Sci. Technol. 37, 1492–1508 (2022). https://doi.org/10.1007/s11390-021-0686-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-021-0686-4

Keywords

Search

Navigation