Skip to main content

Advertisement

SpringerLink
Log in

NNIRSS: neural network-based intelligent routing scheme for SDN

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the increasing diversification of network applications, SDN tends to be inefficient to satisfy the diversified application demands. Meanwhile, the continuous update of OpenFlow and flow table expansion causes the efficiency of routing and forwarding ability decreased as well as the storage space of ternary content addressable memory (TCAM) occupied by flow tables increased. In this paper, we present NNIRSS, a novel neural network (NN)-based intelligent routing scheme for SDN, which leverages the centralized controller to achieve transmission patterns of data flow by utilizing NN and replaces flow table with well-trained NN in the form of NN packet. The route of data flow can be predicted based on its application type to meet the quality of service requirements of network applications. Furthermore, we devise a radial basis function neural network-based intelligent routing mechanism. With combining APC-III and K-means algorithm, we propose APC-K-means algorithm to determine radial basis function centers. Finally, the simulation results demonstrate that our proposed NNIRSS is feasible and effective. It can reduce storage space of TCAM and routing time overhead as well as improve routing efficiency.

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
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Kreutz D, Ramos FMV, Esteves Verissimo P et al (2015) Software-defined networking: a comprehensive survey. Proc IEEE 103(1):14–76

    Article  Google Scholar 

  2. Bu C, Wang X, Cheng H, Huang M, Li K, Das SK (2017) Enabling Adaptive Routing Service Customization via the integration of SDN and NFV. J Netw Comput Appl 93:123–136

    Article  Google Scholar 

  3. Yi B, Wang X, Huang M (2017) Design and evaluation of schemes for provisioning service function chain with function scalability. J Netw Comput Appl 93:197–214

    Article  Google Scholar 

  4. Lv J, Wang X, Huang M et al (2017) RISC: ICN routing mechanism incorporating SDN and community division. Comput Netw 123:88–103

    Article  Google Scholar 

  5. Yi B, Wang X, Li K, Das SK, Huang M (2018) A comprehensive survey of network function virtualization. Comput Netw 133:212–262

    Article  Google Scholar 

  6. McKeown N, Anderson T, Balakrishnan H et al (2008) OpenFlow: enabling innovation in campus networks. ACM SIGCOMM Comput Commun Rev 38(2):69–74

    Article  Google Scholar 

  7. Raza MH, Sivakumar SC, Nafarieh A et al (2014) A comparison of software defined network (SDN) implementation strategies. Proc Comput Sci 32:1050–1055

    Article  Google Scholar 

  8. Liu Zhongjin, Li Yong, Li Su et al (2014) TCAM storage efficient OpenFlow multilevel flow table mapping mechanism. J Tsinghua Univ Nat Sci Ed 54(4):437–442

    Google Scholar 

  9. Ge J, Chen Z, Wu Y (2015) H-SOFT: a heuristic storage space optimisation algorithm for flow table of OpenFlow. Concurr Comput Pract Exp 27(13):3497–3509

    Article  Google Scholar 

  10. Banerjee S, Kannan K (2014) Tag-in-tag: efficient flow table management in SDN switches. In: 10th IEEE international conference on network and service management, pp 109–117

  11. Kannan K, Banerjee S (2013) Compact TCAM: flow entry compaction in TCAM for power aware SDN. In: International conference on distributed computing and networking, pp 439–444

  12. Huang JF, Chang GY, Wang CF et al (2016) Heterogeneous flow table distribution in software-defined networks. IEEE Trans Emerg Top Comput 4(2):252–261

    Article  Google Scholar 

  13. Tsai TH, Wang K, Chao TY (2016) Dynamic flow aggregation in SDNs for application-aware routing. In: 10th IEEE international symposium on communication systems, networks and digital signal processing, pp 1–5

  14. Koerner M, Kao O (2012) Multiple service load-balancing with OpenFlow. In: IEEE 13th international conference on high performance switching and routing, pp 210–214

  15. Egilmez HE, Civanlar S (2013) An optimization framework for QoS-enabled adaptive video streaming over OpenFlow networks. IEEE Trans Multimed 15(3):710–715

    Article  Google Scholar 

  16. Tsung-Feng Y, Wang K, Hsu YH (2015) Adaptive routing for video streaming with QoS support over SDN networks .In: International conference on information networking, pp 318–323

  17. Jeong K, Kim J, Kim YT (2015) QoS-aware network operating system for software defined networking with generalized OpenFlows. In: Network operations and management symposium, pp 1167–1174

  18. Tajiki MM, Akbari B, Mokari N (2017) Optimal QoS-aware network reconfiguration in software defined cloud data centers. Comput Netw 120:71–86

    Article  Google Scholar 

  19. Ishimori A, Farias F, CerqueiraE, et al (2013) Control of multiple packet schedulers for improving QoS on OpenFlow/SDN networking. In: Second European workshop on software defined networks, pp 81–86

  20. Cui H, Zhu Y, Yao Y, et al (2014) Design of intelligent capabilities in SDN. In: 4th International conference on wireless communications, vehicular technology, information theory and aerospace & electronic systems, pp 1–5

  21. Tajiki MM, Salsano S, Shojafar M, et al (2017) Joint energy efficient and QoS-aware path allocation and VNF placement for service function chaining (2017). arXiv preprint arXiv:1710.02611

  22. Shojafar M, Cordeschi N, Baccarelli E (2016) Energy-efficient adaptive resource management for real-time vehicular cloud services. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2551747

  23. Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst 16(1):44–55

    Article  Google Scholar 

  24. Er MJ, Wu S, Lu J et al (2002) Face recognition with radial basis function (RBF) neural networks. IEEE Trans Neural Netw 13(3):697–710

    Article  Google Scholar 

  25. Naldi MC, Campello RJGB, Hruschka ER et al (2011) Efficiency issues of evolutionary k-means. Appl Soft Comput 11(2):1938–1952

    Article  Google Scholar 

  26. Omari A, Figueiras-Vidal AR (2013) Feature combiners with gate-generated weights for classification. IEEE Trans Neural Netw Learn Syst 24(1):158–163

    Article  Google Scholar 

  27. Network performance objectives for IP-based services, ITU-T Y.1541 (2011)

  28. Takahashi N, Yamada I, Sayed AH (2010) Diffusion least-mean squares with adaptive combiners: formulation and performance analysis. IEEE Trans Signal Process 58(9):4795–4810

    Article  MathSciNet  Google Scholar 

  29. NS3. https://www.nsnam.org/

  30. Wu J, Wang JH, Yang J (2011) CNGI-CERNET2: an IPv6 deployment in China. ACM SIGCOMM Comput Commun Rev 41(2):48–52

    Article  Google Scholar 

  31. Wong TT (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit 48(9):2839–2846

    Article  Google Scholar 

  32. Open Network Foundation. OpenFlow switch specification version 1.1.0. http://archive.openflow.org/documents/openflowspec-v1.1.0.pdf. Accessed 2 Oct 2016

Download references

Acknowledgements

This work was supported by the Major International (Regional) Joint Research Project of NSFC under Grant No. 71620107003, the National Natural Science Foundation of China under Grant No. 61572123, the National Science Foundation for Distinguished Young Scholars of China under Grant No. 71325002, MoE and ChinaMobile Joint Research Fund under Grant No. MCM20160201, Program for Liaoning Innovative Research Term in University under Grant No. LT2016007, CERNET Innovation Project under Grant No. NGII20160126 and the Fundamental Research Funds for the Central Universities Project under Grant No. N150403007. We would like to thanks all referees for their invaluable insights and suggestions.

Author information

Authors and Affiliations

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

    Chuangchuang Zhang & Fuliang Li

  2. College of Software, Northeastern University, Shenyang, 110169, Liaoning, China

    Xingwei Wang

  3. College of Information Science and Engineering, State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, Liaoning, China

    Min Huang

Authors
  1. Chuangchuang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xingwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fuliang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Min Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chuangchuang Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, C., Wang, X., Li, F. et al. NNIRSS: neural network-based intelligent routing scheme for SDN. Neural Comput & Applic 31, 6189–6205 (2019). https://doi.org/10.1007/s00521-018-3427-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3427-z

Keywords

Search

Navigation