LSTM-BASED SERVER AND ROUTE SELECTION IN DISTRIBUTED AND HETEROGENEOUS SDN NETWORK
Author affiliations
DOI:
https://doi.org/10.15625/1813-9663/17591Keywords:
SDN, Inter-SDN domain, LSTM, Network state prediction, QoS, server and route selection algorithmAbstract
Today, the Software-defined Network, with its advantages such as greater reliability via automation, more efficient network management, cost-savings, and faster scalability, is increasingly being deployed in many network systems and network operators. The most common deployment architecture is a distributed system with the existence of many independent domains, each controlled by an SDN controller. One of the well-known applications in SDN is server selection and routing. However, deploying server and route selection in distributed and heterogeneous SDN networks faces two issues. First, the lack of global views of the whole system is because the inter-communication between SDN domains has not been standardized for the distributed and heterogeneous SDN network. To solve this issue, we use our previous work, an open East-West interface called SINA, to adaptively guarantee the network state consistency of the distributed SDN network with multiple domains. Secondly, selecting the path for packet transmission based only on the current network states of a local SDN domain is ineffective as it can bring over-utilization to several links and under-utilization to others. Predicting the link cost of the whole path from the source to the destination is necessary. Therefore, this paper proposes an LSTM-based link cost prediction for the server and route selection mechanism in a distributed and heterogeneous SDN network. The experimental results show that our proposal improves up to 15% of link utilization, reduces 10% of packet loss, and obtains the lowest servers’response time compared to benchmarks
Metrics
References
H. A. Akyıldız, I. Hokelek, M. Ileri, E. Saygun, and H. A. Cirpan, “Joint server and route selection in sdn networks,” in 2017 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), 2017, pp. 1–5. DOI: https://doi.org/10.1109/BlackSeaCom.2017.8277663
S. Asadollahi, B. Goswami, and M. Sameer, “Ryu controller’s scalability experiment on software defined networks,” in 2018 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), 2018, pp. 1–5. DOI: https://doi.org/10.1109/ICCTAC.2018.8370397
A. Azzouni, R. Boutaba, and G. Pujolle, “Neuroute: Predictive dynamic routing for softwaredefined networks,” in 2017 13th International Conference on Network and Service Management (CNSM), 2017, pp. 1–6. DOI: https://doi.org/10.23919/CNSM.2017.8256059
A. Azzouni and G. Pujolle, “Neutm: A neural network-based framework for traffic matrix prediction in sdn,” in NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium, 2018, pp. 1–5. DOI: https://doi.org/10.1109/NOMS.2018.8406199
P. Berde, M. Gerola, J. Hart, Y. Higuchi, M. Kobayashi, T. Koide, B. Lantz, B. O’Connor, P. Radoslavov, W. Snow et al., “Onos: towards an open, distributed sdn os,” in Proceedings of the third workshop on Hot topics in software defined networking, 2014, pp. 1–6.
S. Bhanja and A. Das, “Impact of data normalization on deep neural network for time series forecasting,” 2018. [Online]. Available: https://arxiv.org/abs/1812.05519
J. Bhatia, R. Dave, H. Bhayani, S. Tanwar, and A. Nayyar, “Sdn-based real-time urban traffic analysis in vanet environment,” Computer Communications, vol. 149, pp. 162–175, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0140366419308916 DOI: https://doi.org/10.1016/j.comcom.2019.10.011
D. D. Clark, C. Partridge, J. C. Ramming, and J. T. Wroclawski, “A knowledge plane for the internet,” in Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications, 2003, pp. 3–10. DOI: https://doi.org/10.1145/863955.863957
N.-T. Hoang, H.-N. Nguyen, H.-A. Tran, and S. Souihi, “A novel adaptive east-west interface for a heterogeneous and distributed sdn network,” Electronics, vol. 11, no. 7, 2022. [Online]. Available: https://www.mdpi.com/2079-9292/11/7/975 DOI: https://doi.org/10.3390/electronics11070975
R. Jawaharan, P. M. Mohan, T. Das, and M. Gurusamy, “Empirical evaluation of sdn controllers using mininet/wireshark and comparison with cbench,” in 2018 27th International Conference on Computer Communication and Networks (ICCCN), 2018, pp. 1–2. DOI: https://doi.org/10.1109/ICCCN.2018.8487382
L. Jiang, W. Xia, F. Yan, L. Shen, Y. Zhang, and Y. Gao, “Qos-aware routing optimization algorithm using differential search in sdn-based manets,” in 2021 IEEE Global Communications Conference (GLOBECOM), 2021, pp. 1–6. DOI: https://doi.org/10.1109/GLOBECOM46510.2021.9685327
L. Jiang, W. Xia, Y. Zheng, F. Yan, L. Shen, Y. Zhang, and X. Yang, “Qos sensitive routing algorithm with link quality prediction in sdn-based ad hoc networks,” in 2020 International Conference on Wireless Communications and Signal Processing (WCSP), 2020, pp. 1188–1193. DOI: https://doi.org/10.1109/WCSP49889.2020.9299692
S. Kaur, K. Kumar, J. Singh, and N. S. Ghumman, “Round-robin based load balancing in software defined networking,” in 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015, pp. 2136–2139.
S. Knight, H. X. Nguyen, N. Falkner, R. Bowden, and M. Roughan, “The internet topology zoo,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 9, pp. 1765–1775, 2011. DOI: https://doi.org/10.1109/JSAC.2011.111002
ONOS. (2021) Portstatistics api. [Online]. Available: https://api.onosproject.org/1.12.0/org/ onosproject/net/device/PortStatistics.html DOI: https://doi.org/10.32699/device.v12i2.2861
S. Patil, “Load balancing approach for finding best path in sdn,” in 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), 2018, pp. 612–616. DOI: https://doi.org/10.1109/ICIRCA.2018.8597425
S. Sanagavarapu and S. Sridhar, “Sdpredictnet-a topology based sdn neural routing framework with traffic prediction analysis,” in 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 2021, pp. 0264–0272. DOI: https://doi.org/10.1109/CCWC51732.2021.9376123
——, “Sdpredictnet-a topology based sdn neural routing framework with traffic prediction analysis,” in 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 2021, pp. 0264–0272.
J. Verbraeken, M. Wolting, J. Katzy, J. Kloppenburg, T. Verbelen, and J. S. Rellermeyer, “A survey on distributed machine learning,” Acm computing surveys (csur), vol. 53, no. 2, pp. 1–33, 2020. DOI: https://doi.org/10.1145/3377454
Y.-J. Wu, P.-C. Hwang, W.-S. Hwang, and M.-H. Cheng, “Artificial intelligence enabled routing in software defined networking,” Applied Sciences, vol. 10, no. 18, 2020. [Online]. Available: https://www.mdpi.com/2076-3417/10/18/6564 DOI: https://doi.org/10.3390/app10186564
Y. Xie, “Servr: A simple http server to serve static files or dynamic documents,” R package version 0.4, vol. 1, 2016.
Downloads
Published
How to Cite
Issue
Section
License
1. We hereby assign copyright of our article (the Work) in all forms of media, whether now known or hereafter developed, to the Journal of Computer Science and Cybernetics. We understand that the Journal of Computer Science and Cybernetics will act on my/our behalf to publish, reproduce, distribute and transmit the Work.2. This assignment of copyright to the Journal of Computer Science and Cybernetics is done so on the understanding that permission from the Journal of Computer Science and Cybernetics is not required for me/us to reproduce, republish or distribute copies of the Work in whole or in part. We will ensure that all such copies carry a notice of copyright ownership and reference to the original journal publication.
3. We warrant that the Work is our results and has not been published before in its current or a substantially similar form and is not under consideration for another publication, does not contain any unlawful statements and does not infringe any existing copyright.
4. We also warrant that We have obtained the necessary permission from the copyright holder/s to reproduce in the article any materials including tables, diagrams or photographs not owned by me/us.
