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A Machine Learning Approach to Implementation of Link Aggregation Control Protocol over Software Defined Networking

by Nazrul Islam, Md. Fazla Rabbi, S.M. Shamim, Md. Saikat Islam Khan, Mohammad Abu Yousuf
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 35
Year of Publication: 2021
Authors: Nazrul Islam, Md. Fazla Rabbi, S.M. Shamim, Md. Saikat Islam Khan, Mohammad Abu Yousuf
10.5120/ijca2021921739

Nazrul Islam, Md. Fazla Rabbi, S.M. Shamim, Md. Saikat Islam Khan, Mohammad Abu Yousuf . A Machine Learning Approach to Implementation of Link Aggregation Control Protocol over Software Defined Networking. International Journal of Computer Applications. 183, 35 ( Nov 2021), 38-46. DOI=10.5120/ijca2021921739

@article{ 10.5120/ijca2021921739,
author = { Nazrul Islam, Md. Fazla Rabbi, S.M. Shamim, Md. Saikat Islam Khan, Mohammad Abu Yousuf },
title = { A Machine Learning Approach to Implementation of Link Aggregation Control Protocol over Software Defined Networking },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2021 },
volume = { 183 },
number = { 35 },
month = { Nov },
year = { 2021 },
issn = { 0975-8887 },
pages = { 38-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number35/32159-2021921739/ },
doi = { 10.5120/ijca2021921739 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:20:00.357948+05:30
%A Nazrul Islam
%A Md. Fazla Rabbi
%A S.M. Shamim
%A Md. Saikat Islam Khan
%A Mohammad Abu Yousuf
%T A Machine Learning Approach to Implementation of Link Aggregation Control Protocol over Software Defined Networking
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 35
%P 38-46
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Software Defined Networking (SDN) is a complete and directly programmable network model which splits the control plane to the network data plane. Link Aggregation (LAG) is the grouping of multiple links into a single aggregated logical link with a higher bandwidth of aggregated data. This research sets out the implementation of the Link Aggregation Control Protocol (LACP) on SDN using Mininet Emulator. OpenvSwitch (OVS) acts as a transfer function, while RYU acts as an OpenFlow controller. Mininet Emulator, which is installed on Ubuntu Virtual Machine (VM) for LACP implementation in SDN. The study indicates that the speed of data communication has improved using LACP. This work also addressed that LACP provides inherent automatic redundancy that dynamically redirected to flow across the remaining links while one of the multiple links used in the aggregated groups fail or disabled. Additionally, Machine Learning (ML) approaches are also used to predict bandwidth based on statistical analysis of the data set. The Internet Service Provider (ISP) can gain more advantages to forecast bandwidth and serve customers.

References
  1. Z. Latif, K. Sharif, F. Li, M. M. Karim, S. Biswas, and Y.Wang, “A comprehensive survey of interface protocols for Software defined networks,” J. Netw. Comput.Appl., vol.156, no. July 2019, p. 102563, 2020, doi: 10.1016/j.jnca.2020.102563.
  2. Islam, N., Rahman, M.H., Nasir, M.K., "A Comprehensive Analysis of QoS in Wired and Wireless SDN Based on Mobile IP", International Journal of Computer Network and Information Security (IJCNIS), Vol.13,No.5, pp.18-28, 2021. DOI: 10.5815/ijcnis.2021.05.02
  3. Y. Zhao, Y. Li, X. Zhang, G. Geng, W. Zhang, and Y. Sun, “A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning,” IEEE Access, vol. 7, pp. 95397–95417, 2019, doi: 10.1109/ACCESS.2019.2928564.
  4. Rahman M.H., Islam N., Swapna A.I., Habib M.A. (2020) Analysis of Software Defined Wireless Network with IP Mobility in Multiple Controllers Domain. In:Cyber Security and Computer Science. ICONCS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering,vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-52856-0_42
  5. L. Nkenyereye, L. Nkenyereye, S. M. Riazul Islam, Y. H. Choi, M. Bilal, and J. W. Jang, “Software-defined network-based vehicular networks: A position paper on their modeling and implementation” arXiv, pp. 1–14, 2019.
  6. G. E. Vaillant, “Defense Mechanisms” Encycl. Hum. Behav. Second Ed., vol. 52, no. 2, pp. 659–666, 2012, doi: 10.1016/B978-0-12-375000-6.00124-5.
  7. D. Dobrev and D. Avresky, “Comparison of SDN Controllers for Constructing Security Functions” 2019 IEEE 18th Int. Symp. Netw. Comput. Appl. NCA 2019, pp. 1–5, 2019, doi: 10.1109/NCA.2019.8935053.
  8. “Network world, gartner: 10 critical it trends for the next five years” [Online Available] http://www.networkworld.com/news/2012/102212gartner-trends-263594.html. Accessed: 2 July, 2021
  9. M.t. review, 10 emerging technologies: Tr10: software- defined networking.” [Online Available]http://www2.technologyreview.com/article/412194/tr10software-defined-networkingAccessed: 20 September, 2021
  10. “Enterprise networking, IDC: SDN a 2 billion market by 2016” [Online Available] http://www.enterprisenetworkingplanet.com/datacenter/-idcsdn-a-2-billion-market-by-2016.html. Accessed: 21 September, 2021
  11. “Inside SDN architecture.” [Online Available] https://www.sdxcentral.com/resources-/sdn/inside-sdn-architecture/. Accessed: 17 July, 2021
  12. “SDN networking: Sdx, sdn nfv apis and sdks” [Online Available]https://www.sdxcentr-al.com/comprehensive- list-of-sdn-apis/. Accessed: 20 July, 2021
  13. “What are sdn northbound api is?” [Online Available] https://www.sdxcentral.com-/resources/sdn/north-bound- interfaces-api/. Accessed: 23 July, 2021
  14. “What are sdn southbound api is?” [Online Available] https://www.sdxcentral.com/- resources/sdn/southboundinterface-api/. Accessed: 27July, 2021
  15. N. Kumari, P. M H, S. Kumar Gangarapu, and K. Subramaniam, “Deep Recurrent Neural Network for Bandwidth Prediction in Software Defined Data Center Networks” Proc. CONECCT 2020 - 6th IEEE Int. Conf. Electron. Comput. Commun. Technol., 2020, doi: 10.1109/CONECCT50063.2020.9198338.
  16. Islam, N., Shamim, S. M., Rabbi, M. F., Khan, M. S. I., andYousuf, M. A. (2021). Building Machine Learning Based Firewall on Spanning Tree Protocol over Software Defined Networking.In Proceedings of International Conference on Trends in Computational and Cognitive Engineering (pp. 557-568).Springer, Singapore.
  17. L. Seidlitz and C. Perner, “Fault tolerance in SDN,” no. April, pp. 3–7, 2020, doi: 10.2313/NET-2020-04-1.
  18. H. Kim, M. Schlansker, J. Renato Santos, J. Tourrilhes, Y. Turner and N. Feamster. CORONET: Fault tolerance for Software Defined Networks. 2012 20th IEEE International Conference on Network Protocols (ICNP), doi: 10.1109/ICNP.2012.6459938
  19. H. Imaizumi, T. Nagata, G. Kunito, K. Yamazaki, and H. Morikawa, “Power saving mechanism based on simple moving average for 802.3ad link aggregation,” 2009 IEEE Globecom Work. Gc Work., 2009, doi: 10.1109/GLOCOMW.2009.5360735.
  20. Sen, S., Gupta, K. D., and Ahsan, M. M. “Leveraging machine learning approach to setup software-defined network (SDN) controller rules during DDoS attack”. In Proceedings of International Joint Conference on Computational Intelligence (pp. 49-60). Springer, Singapore.
  21. Z. Zhang, L. Ma, K. Poularakis, K. K. Leung, J. Tucker, and A. Swami, “MACS: Deep reinforcement learning based SDN controller synchronization policy design,” Proc. - Int. Conf. Netw. Protoc. ICNP, vol. 2019-Octob, no. i, pp. 1–11, 2019, doi: 10.1109/ICNP.2019.8888034.
  22. T. Y. Mu, A. Al-Fuqaha, K. Shuaib, F. M. Sallabi, and J. Qadir, “SDN flow entry management using reinforcement learning,” ACM Trans. Auton. Adapt. Syst., vol. 13, no. 2, 2018, doi: 10.1145/3281032.
  23. Irawati, L. D., Hariyani, Y. S., & Hadiyoso, S. Link Aggregation Control Protocol on Software Defined Network. International Journal of Electrical and Computer Engineering (IJECE), 7(5), 2706, 2017. https://doi.org/10.11591/ijece.v7i5.pp2706-2712
  24. Link aggregation according to IEEE standard 802.3ad https://www.juniper.net/documentation/en_US/junose 15.1/top ics/task/configuration/802.3ad-link- aggregation-configuring.html, Accessed: 28 July,2021
  25. W. Zhijun, X. Qing, W. Jingjie, Y. Meng, and L. Liang, “Low-Rate DDoS Attack Detection Based on Factorization Machine in Software Defined Network,” IEEE Access, vol. 8, pp. 17404–17418, 2020, doi: 10.1109/ACCESS.2020.2967478.
  26. D. S. L. Wei, K. Xue, R. Bruschi, and S. Schmid, “Guest Editorial Leveraging Machine Learning in SDN/NFV- Based Networks,” IEEE J. Sel. Areas Commun., vol. 38, no. 2, pp. 245–247, 2020, doi: 10.1109/JSAC.2019.2959197.
  27. M. M. Raikar, S. M. Meena, M. M. Mulla, N. S. Shetti, and M. Karanandi, “Data Traffic Classification in Software Defined Networks (SDN) using supervised- learning,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 2750–2759, 2020, doi: 10.1016/j.procs.2020.04.299.
  28. Amma, N. G. B., Selvakumar, S., & Velusamy, R. L. (2020). A Statistical Approach for Detection of Denial of Service Attacks in Computer Networks. IEEE Transactions on Network and Service Management, 4537(c), 1–12. https://doi.org/10.1109/TNSM.2020.3022799
  29. Kar, P., Banerjee, S., Mondal, K. C., & Chattopadhyay, S. (n.d.). for Hierarchical Filtration of Anomalies. Springer, Singapore. https://doi.org/10.1007/978-981-13-1742
  30. Elhag, S., Fernández, A., Alshomrani, S., and Herrera, F. (2019). Evolutionary fuzzy systems: A case study of Intrusion detection systems. In Studies in Computational Intelligence (Vol. 779). Springer International Publishing.https://doi.org/10.1007/978-3-319-91341-4_9
  31. N. Provos et al., “A virtual honeypot framework” in USENIX Security Symposium, vol. 173, 2004, pp. 1–14.
  32. “osrg/ryu, github.” [Online Available] https://github.com/osrg/ryu. Accessed: 22 July, 2021
  33. Islam, M.T., Islam, N. andRefat, M.A. Node to Node Performance Evaluation through RYU SDN Controller. Wireless PersCommun 112, 555–570 (2020). https://doi.org/10.1007/s11277-020-07060-4
  34. J. Xiao, S. Chen, and M. Sui, “The strategy of path determination and traffic scheduling in private campus networks based on SDN,” Peer-to-Peer Netw. Appl., vol. 12, no. 2, pp. 430–439, 2019, doi: 10.1007/s12083-017- 0623-z.
  35. “NOXRepo.” [Online Available]http://noxrepo.org/wp/ Accessed: 12 June, 2021
  36. “Floodlight OpenFlow Controller-Floodlight Project.” [Online Available] http://www.projectfloodlight.org/floodlight/. Accessed: 15 June, 2021
  37. “Home-Beacon-Confluence” [Online Available] https://openflow.stanford.edu/display/Beacon/Home/. Accessed: 17 June, 2021
  38. F. Keti and S. Askar, “Emulation of Software Defined Networks Using Mininet in Different Simulation Environments,”Proc. - Int. Conf. Intell. Syst. Model. Simulation, ISMS, vol. 2015-Octob, pp. 205–210, 2015, doi: 10.1109/ISMS.2015.46.
  39. Bonding [Online Available] http://www.linuxfoundation.org/collaborate/ workgroups/-networking/bonding Accessed: 20 June, 2021
  40. Data set [Online Available] https://drive.google.com/drive/folders/1abvPEs6Lbu UXxQAnLy_5H9yz2nSwRUkQ?usp=sharing, Accessed: 20 June, 2021
Index Terms

Computer Science
Information Sciences

Keywords

Software Defined Networking (SDN) Link Aggregation Control Protocol (LACP) Open Flow Mininet Emulator RYU Controller

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