A Deep Learning Based System for Traffic Engineering in Software Defined Networks

Keywords: Deep Learning, Quality of Service, Software Defined Networks, Traffic Classification, Traffic Engineering, Traffic Shaping

Abstract

Traffic engineering is essential for network management, particularly in today's large networks carrying massive amounts of data. Traffic engineering aims to increase the network's efficiency and reliability through intelligent allocation of resources. In this paper, we propose a deep learning-based traffic engineering system in software-defined networks (SDN) to improve bandwidth allocation among various applications. The proposed system conducts traffic classification based on deep neural network and 1D – convolution neural network models. It aims to improve the Quality of Service (QoS) by identifying flows from various applications and distributing the identified flow to multiple queues where each queue has a different priority. Then, it applies traffic shaping in order to manage network bandwidth and the volume of incoming traffic. To increase the network's performance and avoid traffic congestion, we implement a technique that considers the port capacity to accomplish general load balancing.  We have evaluated and compared the performance of deep learning and machine learning models, and tried to solve an imbalanced dataset by implementing the SMOTE technique. The experimental results show that deep models can identify traffic flows with higher accuracy than machine learning models, and applying traffic shaping to the identified flow increases the network's performance and bandwidth availability.

Downloads

Download data is not yet available.

References

Mendiola, A., Astorga, J., Jacob, E., & Higuero, M. (2016). A survey on the contributions of software-defined Networking to traffic engineering. IEEE Communications Surveys & Tutorials, 19(2), 918-953.

Robertazzi T.G., Shi L. (2020). Machine Learning in Networking. In: Networking and Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-36704-6_7.

Yan, J., & Yuan, J. (2018, August). A survey of traffic classification in software defined networks. In 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN) (pp. 200-206). IEEE.

Rezaei, S., & Liu, X. (2019). Deep learning for encrypted traffic classification: An overview. IEEE communications magazine, 57(5), 76-81.

Karakus, M., & Durresi, A. (2017). Quality of service (QoS) in software defined Networking (SDN): A survey. Journal of Network and Computer Applications, 80, 200-218.

Jeong, S., Lee, D., Hyun, J., Li, J., & Hong, J. W. K. (2017, September). Application-aware traffic engineering in software-defined network. In 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS) (pp. 315-318). IEEE.

Yan, J., & Yuan, J. (2018, August). A survey of traffic classification in software defined networks. In 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN) (pp. 200-206). IEEE.

Amaral, P., Dinis, J., Pinto, P., Bernardo, L., Tavares, J., & Mamede, H. S. (2016, November). Machine learning in software defined networks: Data collection and traffic classification. In 2016 IEEE 24th International Conference on Network Protocols (ICNP) (pp. 1-5). IEEE.

Lashkari, A. H., Draper-Gil, G., Mamun, M. S. I., & Ghorbani, A. A. (2017, February). Characterization of Tor Traffic using Time based Features. In ICISSP (pp. 253-262).

Rezaei, S., & Liu, X. (2019). Deep learning for encrypted traffic classification: An overview. IEEE communications magazine, 57(5), 76-81.

Choubey, R. N., Amar, L., Khare, S., & Venkanna, U. (2019, December). Internet Traffic Classifier Using Artificial Neural Network and 1D-CNN. In 2019 International Conference on Information Technology (ICIT) (pp. 291-296). IEEE.

Xu, J., Wang, J., Qi, Q., Sun, H., & He, B. (2018, September). Deep neural networks for application awareness in SDN-based network. In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE.

The Linux Foundation. (n.d.) Open vSwitch . Retrieved June 19, 2020, from http://openvswitch.org/

Thazin, N., Nwe, K. M., & Ishibashi, Y. (2019, February). Resource Allocation Scheme for SDN-Based Cloud Data Center Network. Seventeenth International Conference on Computer Applications (ICCA 2019).

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.

Kaur, S., Singh, J., & Ghumman, N. S. (2014, August). Network programmability using POX controller. In ICCCS International Conference on Communication, Computing & Systems, IEEE (Vol. 138, p. 70).

Mininet. Retrieved May 15, 2020 from http://www.mininet.org.

Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press.

Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2018). A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges. IEEE Communications Surveys & Tutorials, 21(1), 393-430.

Dargan, S., Kumar, M., Ayyagari, M. R., & Kumar, G. (2019). A survey of deep learning and its applications: A new paradigm to machine learning. Archives of Computational Methods in Engineering, 1-22.

Published
2020-12-30
How to Cite
[1]
S. Abdulazzaq and M. Demirci, “A Deep Learning Based System for Traffic Engineering in Software Defined Networks”, IJISAE, vol. 8, no. 4, pp. 206-213, Dec. 2020.
Section
Research Article