An Autonomic Software Defined Network (SDN) Architecture With Performance Improvement Considering
محورهای موضوعی : Network Management
Alireza shirmarz
1
(University of Aletaha)
ali ghaffari
2
(University of Tabris, Islamic Azad )
کلید واژه: SDN , Performance , Autonomic System , Resource Management , QoS,
چکیده مقاله :
SDN makes the network programmable, agile, and flexible with data and control traffic separating. This architecture consists of three layers which are application, control and data. The aim of our research is concentrated on the control layer to improve the performance of the network in an autonomic manner. In the first step, we have categorized the performance improvement researches based on network performance improvement solutions proposed in the recent papers. This performance improvement solution clustering is one of our contributions to our paper. The significant contribution in this paper is a novel autonomic SDN-based architecture to ameliorate the performance metrics including blocking probability (BP), delay, jitter, packet loss rate (PLR), and path utilization. Our SDN-based autonomic system consists of three layers (data, autonomic control, and Route learning) to separate the traffics based on deep neural networks (DNN) and to route the flows with the greedy algorithm. The autonomic SDN-based architecture which has proposed in this paper makes better network performance metrics dynamically. Our proposed autonomic architecture will be developed in the POX controller which has developed by python. Mininet is used for simulation and the results are compared with the commonly used SDN named pure SDN in this article. The simulation results show that our structure works better in a full-mesh topology and improves the performance metrics simultaneously. The average performance is improved by about %2.5 in comparison with pure SDN architecture based on the Area Under Curve (AUC) of network performance.
SDN makes the network programmable, agile, and flexible with data and control traffic separating. This architecture consists of three layers which are application, control and data. The aim of our research is concentrated on the control layer to improve the performance of the network in an autonomic manner. In the first step, we have categorized the performance improvement researches based on network performance improvement solutions proposed in the recent papers. This performance improvement solution clustering is one of our contributions to our paper. The significant contribution in this paper is a novel autonomic SDN-based architecture to ameliorate the performance metrics including blocking probability (BP), delay, jitter, packet loss rate (PLR), and path utilization. Our SDN-based autonomic system consists of three layers (data, autonomic control, and Route learning) to separate the traffics based on deep neural networks (DNN) and to route the flows with the greedy algorithm. The autonomic SDN-based architecture which has proposed in this paper makes better network performance metrics dynamically. Our proposed autonomic architecture will be developed in the POX controller which has developed by python. Mininet is used for simulation and the results are compared with the commonly used SDN named pure SDN in this article. The simulation results show that our structure works better in a full-mesh topology and improves the performance metrics simultaneously. The average performance is improved by about %2.5 in comparison with pure SDN architecture based on the Area Under Curve (AUC) of network performance.
[1] J. Pan, S. Paul, and R. Jain, “A survey of the research on future internet architectures,” Commun. Mag. IEEE, vol. 49, no. 7, pp. 26–36, 2011.
[2] H. Qi and K. Li, Software Defined Networking Applications in Distributed Datacenters. Dalian, China: Engineering, SpringerBriefs in Electrical and Computer, 2016.
[3] N. Feamster, J. Rexford, and E. Zegura, “The Road to SDN: An Intellectual History of Programmable Networks,” ACM Sigcomm Comput. Commun., vol. 44, no. 2, pp. 87–98, 2014.
[4] R. Masoudi and A. Ghaffari, “Software defined networks: A survey,” J. Netw. Comput. Appl., vol. 67, no. May, pp. 1–25, 2016.
[5] A. K. Singh, “A survey and classification of controller placement problem in SDN,” Int. J. Netw. Manag., no. December 2017.
[6] S. Bera, S. Misra, and A. V. Vasilakos, “Software-Defined Networking for Internet of Things: A Survey,” IEEE Internet Things J., vol. 4662, pp. 1–1, 2017.
[7] Y. E. Oktian, S. Lee, H. Lee, and J. Lam, “Distributed SDN Controller System: A Survey on Design Choice,” Comput. Networks, vol. 121, pp. 100–111, 2017.
[8] A. Shirmarz and A. Ghaffari, “Performance issues and solutions in SDN-based data center: a survey,” J. Supercomput., 2020.
[9] A. T. Oliveira, B. Jos, C. A. Martins, M. F. Moreno, and A. B. Vieira, “SDN-Based Architecture for Providing QoS to High-Performance Distributed Applications,” in IEEE Symposium on Computers and Communications (ISCC), 2018.
[10] O. Aldhaibani, F. Bouhafs, M. Makay, and A. Raschellà, “An SDN-based Architecture for Smart Handover to Improve QoE in IEEE 802. 11 WLANs,” in 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2014, pp. 287–292.
[11] I. Engineering, “Enhancing the performance of future wireless networks with software-defined networking ∗,” Front. Inf. Technol. Electron. Eng., vol. 17, no. 7, pp. 606–619, 2016.
[12] T. Shozi, P. Mudali, and O. Matthew, “An SDN Solution for Performance Improvement in Dedicated Wide- Area Networks,” in 2019 Conference on Information Communications Technology and Society (ICTAS), pp. 1–6.
[13] J. W. Kleinrouweler, S. Cabrero, and P. Cesar, “Delivering Stable High-Quality Video : An SDN Architecture with DASH Assisting Network Elements Categories and Subject Descriptors,” in Proceedings of the 7th International Conference on Multimedia Systems, 2016.
[14] W. Wendong, Q. I. Qinglei, G. Xiangyang, H. U. Yannan, and Q. U. E. Xirong, “Autonomic QoS Management Mechanism in Software Defined Network,” China Commun., no. July, pp. 13–23, 2014.
[15] G. Poulios, K. Tsagkaris, P. Demestichas, A. Tall, Z. Altman, and C. Destré, “Autonomics and SDN for self-organizing networks,” 11th Int. Symp. Wirel. Commun. Syst. ISWCS 2014 - Proc., pp. 830–835, 2014.
[16] P. Neves et al., “The SELFNET Approach for Autonomic Management in an NFV/SDN Networking Paradigm,” Int. J. Distrib. Sens. Networks, vol. 2016, no. 4, 2016.
[17] IBM, “Autonomic Computing White Paper: An Architectural Blueprint for Autonomic Computing,” IBM White Pap., no. June, p. 34, 2005.
[18] V. W. Protocol, “OpenFlow Switch Specification,” 2012.
[19] C. Zhang and X. Wang, “Deep learning-based network application classification for SDN,” Trans. Emerg. Telecommun. Technol. Wiley Online Libr. J., no. February 2018.
[20] A. Shirmarz and A. Ghaffari, “An adaptive greedy flow routing algorithm for performance improvement in a software-defined network,” Int. Numer. Model. Electron. networks, Devices, Fields-Wiley online Libr., no. March, pp. 1–21, 2019.
[21] T. Auld, A. W. Moore, and S. F. Gull, “Bayesian Neural Networks for Internet Traffic Classification,” IEEE Trans. Neural Networks, vol. 18, no. 1, pp. 223–239, 2007.
