International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 16, No. 6, 8 Dec. 2024
Cover page and Table of Contents: PDF (size: 888KB)
Deep Learning Based Traffic Management in Knowledge Defined Network
PDF (888KB), PP.73-83
Views: 0 Downloads: 0
Author(s)
Index Terms
Knowledge Defined Network (KDN), Data Center Network (DCN), Convolutional Layer, Recurrent Neural Network (RNN), LSTM, BiLSTM
Abstract
In recent Artificial Intelligence developments, large datasets as knowledge are a prime requirement for analysis and prediction. To manage the knowledge of the network, the Data Center Network (DCN) has been considered a global data storage facility on edge servers and cloud servers. In recent research trends, knowledge-defined networking (KDN) architecture is considered, where the management plane works as the knowledge plane. The major network management task in the DCN is to control traffic congestion. To improve network management, i.e., optimized resource management, enhanced Quality of Service (QoS), we propose a path prediction technique by combining the convolution layer with the RNN deep learning model, i.e., Convolution-Long short-term memory network as Convolution-LSTM and the bi-directional long short-term memory (BiLSTM) network as Convolution-BiLSTM. The experimental results demonstrate that, in terms of many metrics, i.e., network latency, packet loss ratio, network throughput, and overhead, our proposed methodologies perform better than the existing works, i.e., OSPF, FlowDCN, modified discrete PSO, ANN, CNN, and LSTM-based routing approaches. The proposed approach improves the network throughput by approximately 30% and 12% as compared to existing CNN and LSTM-based routing approaches, respectively.
Cite This Paper
Tejas M. Modi, Kuna Venkateswararao, Pravati Swain, "Deep Learning Based Traffic Management in Knowledge Defined Network", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.6, pp.73-83, 2024. DOI:10.5815/ijisa.2024.06.04
Reference
[1]Albert Mestres, Alberto Rodriguez-Natal, Josep Carner, Pere Barlet-Ros, Eduard Alarco´n, Marc Sole´, Victor Munte´s-Mulero, David Meyer, Sharon Barkai, Mike J Hibbett, et al. Knowledge-defined networking. ACM SIG- COMM Computer Communication Review, 47(3):2–10, 2017.
[2]Nick McKeown, Tom Anderson, Hari Balakrishnan, Guru Parulkar, Larry Peterson, Jennifer Rexford, Scott Shenker, and Jonathan Turner. Openflow: enabling innovation in campus networks. ACM SIGCOMM Computer Communi- cation Review, 38(2):69–74, 2008.
[3]Michael Jarschel, Thomas Zinner, Tobias Hoßfeld, Phuoc Tran-Gia, and Wolfgang Kellerer. Interfaces, attributes, and use cases: A compass for sdn. IEEE Communications Magazine, 52(6):210–217, 2014.
[4]Wei Lu, Lipei Liang, Bingxin Kong, Baojia Li, and Zuqing Zhu. Ai-assisted knowledge-defined network orchestra- tion for energy-efficient data center networks. IEEE Communications Magazine, 58(1):86–92, 2020.
[5]Saptarshi Gosh, Brahim El Boudani, Tasos Dagiuklas, and Muddesar Iqbal. So-kdn: A self-organised knowledge defined networks architecture for reliable routing. In Proceedings of the 4th International Conference on Information Science and Systems, pages 160–166, 2021.
[6]Weiye Zheng, Jizhong Zhu, and Qingju Luo. Distributed dispatch of integrated electricity-heat systems with variable mass flow. IEEE Transactions on Smart Grid, 14(3):1907–1919, 2022.
[7]Bomin Mao, Fengxiao Tang, Zubair Md Fadlullah, Nei Kato, Osamu Akashi, Takeru Inoue, and Kimihiro Mizutani. A novel non-supervised deep-learning-based network traffic control method for software defined wireless networks. IEEE Wireless Communications, 25(4):74–81, 2018.
[8]Bomin Mao, Zubair Md Fadlullah, Fengxiao Tang, Nei Kato, Osamu Akashi, Takeru Inoue, and Kimihiro Mizutani. Routing or computing? the paradigm shift towards intelligent computer network packet transmission based on deep learning. IEEE Transactions on Computers, 66(11):1946–1960, 2017.
[9]Tejas Modi and Pravati Swain. Flowdcn: Flow scheduling in software defined data center networks. In proceedings of the 3rd IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pages 1–5, 2019.
[10]Tug˘rul C¸ avdar and S¸ eyma Aymaz. New approach to dynamic load balancing in software-defined network-based data centers. ETRI Journal, 45(3):433–447, 2023.
[11]Albert Mestres, Eduard Alarco´n, Yusheng Ji, and Albert Cabellos-Aparicio. Understanding the modeling of com- puter network delays using neural networks. In proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, pages 46–52, 2018.
[12]Bomin Mao, Fengxiao Tang, Zubair Md Fadlullah, and Nei Kato. An intelligent route computation approach based on real-time deep learning strategy for software defined communication systems. IEEE Transactions on Emerging Topics in Computing, 9(3):1554–1565, 2019.
[13]Abdelhadi Azzouni and Guy Pujolle. Neutm: A neural network-based framework for traffic matrix prediction in sdn. In proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), pages 1–5, 2018.
[14]Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7):1527–1554, 2006.
[15]Junfeng Xie, F Richard Yu, Tao Huang, Renchao Xie, Jiang Liu, Chenmeng Wang, and Yunjie Liu. A survey of machine learning techniques applied to software defined networking (sdn): Research issues and challenges. IEEE Communications Surveys & Tutorials, 21(1):393–430, 2018.
[16]Priyanka Lokhande and Bhavana S Tiple. A step towards advanced machine learning approach: Deep learning. In proceeding of the IEEE International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pages 1112–1116, 2017.
[17]Alex MR Ruelas and Christian Esteve Rothenberg. A load balancing method based on artificial neural networks for knowledge-defined data center networking. In proceedings of the 10th Latin America Networking Conference, pages 106–109, 2018.
[18]Jonghwan Hyun, Nguyen Van Tu, and James Won-Ki Hong. Towards knowledge-defined networking using in-band network telemetry. In proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), pages 1–7, 2018.
[19]Daniela Maria Casas Velasco, Nelson Luis Saldanha da Fonseca, and Oscar Maur´ıcio Caicedo Rendo´n. Routing based on reinforcement learning for software-defined networking. In Extended Annals of the XXXIX Brazilian Symposium on Computer Networks and Distributed Systems, pages 185–192, 2021.
[20]Yuxiang Hu, Ziyong Li, Julong Lan, Jiangxing Wu, and Lan Yao. Ears: Intelligence-driven experiential network architecture for automatic routing in software-defined networking. China Communications, 17(2):149–162, 2020.
[21]Yu Li, Hu Wang, and Juanjuan Liu. Can cnn construct highly accurate models efficiently for high-dimensional problems in complex product designs? arXiv preprint arXiv:1712.01639, 2017.
[22]Wolfgang Braun and Michael Menth. Software-defined networking using openflow: Protocols, applications and architectural design choices. Future Internet, 6(2):302–336, 2014.