An intelligent routing approach for multimedia traffic transmission over SDN

Nowadays, multimedia applications such as video streaming services have become significantly popular, especially with the rapid growth of users, various devices, and the increased availability and diversity of these services over the internet. In this case, service providers and network administrators have difficulties ensuring end-user satisfaction because the traffic generated by such services is more exposed to multiple network quality of service impairments, including bandwidth, delay, jitter, and loss ratio. This paper proposes an intelligent-based multimedia traffic routing framework that exploits the integration of a reinforcement learning technique with software-defined networking to explore, learn and find potential routes for video streaming traffic. Simulation results through a realistic network and under various traffic loads demonstrate the proposed scheme's effectiveness in providing a better end-user viewing quality, higher throughput and lower video quality switches when compared to the existing techniques.

[1] U. Cisco, "Cisco annual internet report (2018-2023) white paper," Cisco:San Jose, CA, USA, 2020.

[2] A. Doumanoglou, N. Zioulis, D. Griffin, J. Serrano, T. K. Phan,D. Jimenez, D. Zarpalas, F. Alvarez, M. Rio, and P. Daras, "A system 'architecture for live immersive 3d-media transcoding over 5g networks," in 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, 2018, pp. 11-15.

[3] N. Jawad, M. Salih, K. Ali, B. Meunier, Y. Zhang, X. Zhang, R. Zetik, C. Zarakovitis, H. Koumaras, M.-A. Kourtis et al., "Smart television services using nfv/sdn network management," IEEE Transactions on Broadcasting, vol. 65, no. 2, pp. 404-413, 2019.
https://doi.org/10.1109/TBC.2019.2898159

[4] A. A. Barakabitze, L. Sun, I.-H. Mkwawa, and E. Ifeachor, "A novel qoe-centric sdn-based multipath routing approach for multimedia services over 5g networks," in 2018 IEEE International Conference on Communications (ICC), 2018, pp. 1-7.

[5] C. Liang, Y. He, F. R. Yu, and N. Zhao, "Enhancing video rate adaptation with mobile edge computing and caching in software-defined mobile networks," IEEE Transactions on Wireless Communications, vol. 17, no. 10, pp. 7013-7026, 2018.

[6] N. Anerousis, P. Chemouil, A. A. Lazar, N. Mihai, and S. B. Weinstein, "The origin and evolution of open programmable networks and sdn," IEEE Communications Surveys & Tutorials, 2021.

[7] T. Uzakgider, C. Cetinkaya, and M. Sayit, "Learning-based approach for layered adaptive video streaming over sdn," Computer Networks, vol. 92, pp. 357-368, 2015.
https://doi.org/10.1016/j.comnet.2015.09.027

[8] S. Sendra, A. Rego, J. Lloret, J. M. Jimenez, and O. Romero, "Including artificial intelligence in a routing protocol using software defined networks," in 2017 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2017, pp. 670-674.

[9] A. Al-Jawad, P. Shah, O. Gemikonakli, and R. Trestian, "Learnqos: A learning approach for optimizing qos over multimedia-based sdns," in 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, 2018, pp. 1-6.

[10] X. Huang, T. Yuan, G. Qiao, and Y. Ren, "Deep reinforcement learning for multimedia traffic control in software defined networking," IEEE Network, vol. 32, no. 6, pp. 35-41, 2018.
https://doi.org/10.1109/MNET.2018.1800097

[11] A. Al-Jawad, I.-S. Coms¸a, P. Shah, O. Gemikonakli, and R. Trestian, "An innovative reinforcement learning-based framework for quality of service provisioning over multimedia-based sdn environments," IEEE Transactions on Broadcasting, vol. 67, no. 4, pp. 851-867, 2021.
https://doi.org/10.1109/TBC.2021.3099728

[12] A. Al-Jawad, I.-S. Coms¸a, P. Shah, O. Gemikonakli, and R. Trestian, "Redo: a reinforcement learning-based dynamic routing algorithm selection method for sdn," in 2021 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2021, pp. 54-59.

[13] M. Al Jameel, T. Kanakis, S. Turner, A. Al-Sherbaz, and W. S. Bhaya, "A reinforcement learning-based routing for real-time multimedia traffic transmission over software-defined networking," Electronics, vol. 11, no. 15, p. 2441, 2022.

[14] O. Oginni, P. Bull, and Y. Wang, "Constraint-aware software-defined network for routing real-time multimedia," ACM SIGBED Review, vol. 15, no. 3, pp. 37-42, 2018.
https://doi.org/10.1145/3267419.3267425

[15] Z. Mammeri, "Reinforcement learning based routing in networks: Review and classification of approaches," Ieee Access, vol. 7, pp. 55 916-
https://doi.org/10.1109/ACCESS.2019.2913776 55 950, 2019.

[16] P. Juluri, V. Tamarapalli, and D. Medhi, "Measurement of quality of experience of video-on-demand services: A survey," IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 401-418, 2015.
https://doi.org/10.1109/COMST.2015.2401424

[17] L. Al Shalabi and Z. Shaaban, "Normalization as a preprocessing engine for data mining and the approach of preference matrix," in 2006 International conference on dependability of computer systems. IEEE, 2006, pp. 207-214.

[18] H. J. Kim, D. G. Yun, H.-S. Kim, K. S. Cho, and S. G. Choi, "Qoe assessment model for video streaming service using qos parameters in wired-wireless network," in 2012 14th International Conference on Advanced Communication Technology (ICACT). IEEE, 2012, pp. 459-464.

[19] R. L. S. de Oliveira, C. M. Schweitzer, A. A. Shinoda, and L. R. Prete, "Using mininet for emulation and prototyping software-defined networks," in 2014 IEEE Colombian Conference on Communications and Computing (COLCOM), 2014, pp. 1-6.

[20] 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). IEEE, 2018, pp. 1-5.

[21] Z. Li, C. Bampis, J. Novak, A. Aaron, K. Swanson, A. Moorthy, and J. Cock, "Vmaf: The journey continues," Netflix Technology Blog, vol. 25, 2018.
https://doi.org/10.22233/20412495.040118.25

[22] U. Sara, M. Akter, and M. S. Uddin, "Image quality assessment through fsim, ssim, mse and psnr-a comparative study," Journal of Computer and Communications, vol. 7, no. 3, pp. 8-18, 2019.

[23] M. O. Elbasheer, A. Aldegheishem, J. Lloret, and N. Alrajeh, "A qosbased routing algorithm over software defined networks," Journal of Network and Computer Applications, vol. 194, p. 103215, 2021.
https://doi.org/10.1016/j.jnca.2021.103215

[24] Big buck bunny. [Online]. Available: https://peach.blender.org/