A Prediction-Based Load Distribution Approach for Software-Defined Networks
Subject Areas : electrical and computer engineeringHossein Mohammadi 1 , سیداکبر مصطفوی 2
1 - Yazd University
2 - Yazd University
Keywords: Software-defined networking, load balancing, prediction algorithm, extreme learning machine (ELM),
Abstract :
Software-defined networking is a new network architecture which separates the control layer from the data layer. In this approach, the responsibility of the control layer is delegated to the controller software to dynamically determine the behavior of the entire network. It results in a flexible network with centralized management in which network parameters can be well controlled. Due to the increasing number of users, the emergence of new technologies, the explosive growth of network traffic, meeting the requirements of quality of service and preventing underload or overload of resources, load balancing in software-based networks is of substantial importance. Load imbalance increases costs, reduces scalability, flexibility, efficiency, and delay in network service. So far, a number of solutions have been proposed to improve the performance and load balancing in the network, which take into account different criteria such as power consumption and server response time, but most of them do not prevent the system from entering the load imbalance mode and the risks of load imbalance. In this paper, a predictive load balancing method is proposed to prevent the system from entering the load imbalance mode using the Extreme Learning Machine (ELM) algorithm. The evaluation results of the proposed method show that in terms of controller processing delay, load balance and response time, it performs better than CDAA and PSOAP methods.
[1] G. Li, X. Wang, and Z. Zhang, "SDN-based load balancing scheme for multi-controller deployment," IEEE Access, vol. 7, pp. 39612-39622, Mar. 2019.
[2] Y. Wu, G. Min, and L. T. Yang, "Performance analysis of hybrid wireless networks under bursty and correlated traffic," IEEE Trans. Veh. Technol., vol. 62, no. 1, pp. 449-454, Jan. 2013.
[3] D. Zhang, H. Huang, J. Zhou, F. Xia, and Z. Chen, "Detecting hot road mobility of vehicular Ad Hoc networks," Mob. Networks Appl., vol. 18, no. 6, pp. 803-813, Dec. 2013.
[4] M. Dong, T. Kimata, K. Sugiura, and K. Zettsu, "Quality-of-Experience (QoE) in emerging mobile social networks," IEICE Trans. Inf. Syst., vol. E97D, no. 10, pp. 2606-2612, Oct. 2014.
[5] H. Kim and N. Feamster, "Improving network management with software defined networking," IEEE Commun. Mag., vol. 51, no. 2, pp. 114-119, Feb. 2013.
[6] S. Sezer, S. Scott-Hayward, P. Kaur, B. Fraser, D. Lake, J. Finnegan, and N. Viljoen, "Are we ready for SDN? implementation challenges for software-defined networks," IEEE Commun. Mag., vol. 51, no. 7, pp. 36-43, Jul. 2013.
[7] H. Zhong, Q. Lin, J. Cui, R. Shi, and L. Liu, "An efficient SDN load balancing scheme based on variance analysis for massive mobile users," Mob. Inf. Syst., vol. 2015, Article ID: 241732, 2015.
[8] D. Wetherall and D. Tennenhouse, "Retrospective on 'towards an active network architecture'," Comput. Commun. Rev., vol. 49, no. 5, pp. 86-89, Nov. 2019.
[9] H. Xu, H. Huang, S. Chen, G. Zhao, and L. Huang, "Achieving high scalability through hybrid switching in software-defined networking," IEEE/ACM Trans. Netw., vol. 26, no. 1, pp. 618-632, Jan. 2018.
[10] C. Decusatis, M. Haley, T. Bundy, and R. Cunnistra, "Dynamic, software-defined service provider network infrastructure and cloud drivers for SDN adoption," in Proc. IEEE Int. Conf. Commun. Work, pp. 235-239, Budapest, Hungary, 9-13 Jun. 2013.
[11] Sandhya, Y. Sinha, and K. Haribabu, "A survey: hybrid SDN," J. Netw. Comput. Appl., vol. 100, pp. 35-55, 15 Dec. 2017.
[12] D. Namiot and M. Sneps-sneppe, Metadata in SDN API, arXive, abs/1503.06630, 2015.
[13] A. Shalimov, D. Zimarina, and V. Pashkov, "Advanced study of SDN/OpenFlow controllers," in Proc. of the 9th Central & Eastern European Software Engineering Conf. in Russia, 6 pp., Moscow, Russia, 24-25 Oct. 2013.
[14] G. Bin Huang, Q. Y. Zhu, and C. K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1-3, pp. 489-501, May 2006.
[15] G. Huang, G. Bin Huang, S. Song, and K. You, "Trends in extreme learning machines: a review," Neural Networks, vol. 61, , pp. 32-48, Oct. 2015.
[16] F. K. Inaba, E. O. Teatini Salles, S. Perron, and G. Caporossi, "DGR-ELM-distributed generalized regularized ELM for classification," Neurocomputing, vol. 275, pp. 1522-1530, Dec. 2018.
[17] S. Liao and C. Feng, "Meta-ELM: ELM with ELM hidden nodes," Neurocomputing, vol. 128, pp. 81-87, Oct. 2014.
[18] H. Zhong, Y. Fang, and J. Cui, "LBBSRT: an efficient SDN load balancing scheme based on server response time," Futur. Gener. Comput. Syst., vol. 68, pp. 183-190, Oct. 2017.
[19] W. C. Chien, C. F. Lai, H. H. Cho, and H. C. Chao, "A SDN-SFC-based service-oriented load balancing for the IoT applications," J. Netw. Comput. Appl., vol. 114, pp. 88-97, Jul. 2018.
[20] L. Boero, M. Cello, C. Garibotto, M. Marchese, and M. Mongelli, "BeaQoS: load balancing and deadline management of queues in an OpenFlow SDN switch," Comput. Networks, vol. 106, pp. 161-170, Sept. 2016.
[21] S. Shahryari, S. A. Hosseini-Seno, and F. Tashtarian, "An SDN based framework for maximizing throughput and balanced load distribution in a cloudlet network," Futur. Gener. Comput. Syst., vol. 110, pp. 18-32, Sept. 2020.
[22] A. A. Ateya, A. Muthanna, A. Vybornova, and A. D. Algarni, "Chaotic salp swarm algorithm for SDN multi-controller networks," Eng. Sci. Technol. an Int. J., vol. 22, no. 4, pp. 1001-1012, Aug. 2019.
[23] M. Chen, K. Ding, J. Hao, C. Hu, G. Xie, C. Xing, and B. Chen, "LCMSC: a lightweight collaborative mechanism for SDN controllers," Comput. Networks, vol. 121, pp. 65-75, Jul. 2017.
[24] T. Zhang, P. Giaccone, A. Bianco, and S. De Domenico, "The role of the inter-controller consensus in the placement of distributed SDN controllers," Comput. Commun., vol. 113, no. C, pp. 1-13, Nov. 2017.
[25] P. Song, Y. Liu, C. Liu, and D. Qian, "ParaFlow: fine-grained parallel SDN controller for large-scale networks," J. Netw. Comput. Appl., vol. 87, pp. 46-59, Jun. 2017.
[26] J. Cui, Q. Lu, H. Zhong, M. Tian, and L. Liu, "A load-balancing mechanism for distributed SDN control plane using response time," IEEE Trans. Netw. Serv. Manag., vol. 15, no. 4, pp. 1197-1206, Oct. 2018.
[27] V. R. Dasari and T. S. Humble, "OpenFlow arbitrated programmable network channels for managing quantum metadata," J. Def. Model. Simul., vol. 16, no. 1, pp. 67-77, Jan. 2019.
[28] Y. Wu, S. Zhou, Y. Wei, and S. Leng, "Deep reinforcement learning for controller placement in software defined network," in Proc. IEEE Conf. on Computer Communications Workshops pp. 1254-1259, Toronto, Canada, 6-9 Jul. 2020.
[29] X. Tian, H. Shen, X. Chen, and C. Li, "Analysis of SDN multi-controller placement," in Proc. Int. Conf. on Data Processing Techniques and Applications for Cyber-Physical Systems, Advances in Intelligent Systems and Computing, vol. 1379, pp. 677-683, Jun. 2021.
[30] S. Yeo, Y. Naing, T. Kim, and S. Oh, "Achieving balanced load distribution with reinforcement learning-based switch migration in distributed SDN controllers," Electronics, vol. 10, no. 2, pp. 1-16, Jan. 2021.
[31] H. Mostafaei, M. Menth, and M. S. Obaidat, "A learning automaton-based controller placement algorithm for software-defined networks," in Proc. IEEE Global Communications Conf., 6 pp., Abu Dhabi, United Arab Emirates, 9-13 Dec. 2018.
