saucis Sakarya University Journal of Computer and Information Sciences 2636-8129 Sakarya University 10.35377/saucis...1599100 Software Engineering (Other) Yazılım Mühendisliği (Diğer) Load Balancing and Quality of Experience in Software-Defined Networks Load Balancing and Quality of Experience in Software-Defined Networks https://orcid.org/0000-0003-2884-2982 Çoban Gürhan TC MİLLİ EĞİTİM BAKANLIĞI BURSA https://orcid.org/0000-0002-5657-7402 Cicioğlu Murtaza BURSA ULUDAĞ ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ https://orcid.org/0000-0002-5798-3103 Çalhan Ali DÜZCE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ 09 30 2025 8 3 382 391 12 10 2024 08 13 2025 Copyright © 2018, Sakarya University Journal of Computer and Information Sciences 2018 Sakarya University Journal of Computer and Information Sciences

Software-defined networks (SDN) aim to eliminate the disadvantages of traditional network environments by separating the control plane and the data plane. As a result, the controllability of the network is increased. This study proposes to examine the relationship between load balancing and Quality of Experience (QoE) in SDNs in terms of Structural Similarity Index Method (SSIM), Round Trip Time (RTT), delay and hop count measurements. In addition, the data loss parameter is also examined. A comparison is made between clients and servers in a video streaming test. Five scenarios, namely no-load balancing, Round Robin, average RTT, delay-based and the proposed load balancing method, are tested. The average SSIM value of 0.9678 is obtained with the proposed load balancing method. When the data loss is examined, the average data loss value is 0.25 Mbytes higher than the Round Robin method, but better results are obtained than the other scenarios. High SSIM value and reduction in data loss indicate a better QoE. It is observed that load balancing operations performed by considering the load status of the servers and the statistical data of the network give better results.

Software-defined networks (SDN) aim to eliminate the disadvantages of traditional network environments by separating the control plane and the data plane. As a result, the controllability of the network is increased. This study proposes to examine the relationship between load balancing and Quality of Experience (QoE) in SDNs in terms of Structural Similarity Index Method (SSIM), Round Trip Time (RTT), delay and hop count measurements. In addition, the data loss parameter is also examined. A comparison is made between clients and servers in a video streaming test. Five scenarios, namely no-load balancing, Round Robin, average RTT, delay-based and the proposed load balancing method, are tested. The average SSIM value of 0.9678 is obtained with the proposed load balancing method. When the data loss is examined, the average data loss value is 0.25 Mbytes higher than the Round Robin method, but better results are obtained than the other scenarios. High SSIM value and reduction in data loss indicate a better QoE. It is observed that load balancing operations performed by considering the load status of the servers and the statistical data of the network give better results.

 Load Balancing Quality of Experience SDN Load Balancing Quality of Experience SDN Bursa Uludağ University Scientific Research Projects Unit FGA 2023–1557 X. Huang, M. Zeng, and K. Xie, “Intelligent traffic control for QoS optimization in hybrid SDNs,” Computer Networks, vol. 189, Apr. 2021, doi: 10.1016/j.comnet.2021.107877. M. Cicioğlu and A. Çalhan, “Yazılım Tanımlı Ağlar – YTA,” Karaelmas Fen ve Mühendislik Dergisi, vol. 7, no. 2, pp. 684–695, Jun. 2017. T. Abar, A. Ben Letaifa, and S. El Asmi, “Machine Learning Based QoE Prediction in SDN Networks,” in 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, Jun. 2017, pp. 1395–1400. L. Cantor, “A word from Sandvine CEO,” 2023. Accessed: Jan. 29, 2024. [Online]. Available: https://www.sandvine.com/global-internet-phenomena-report-2023 Q. Wang, H. N. Dai, H. Wang, and D. Wu, “Data-driven QoE analysis on video streaming in mobile networks,” in Proceedings - 15th IEEE International Symposium on Parallel and Distributed Processing with Applications and 16th IEEE International Conference on Ubiquitous Computing and Communications, ISPA/IUCC 2017, Institute of Electrical and Electronics Engineers Inc., May 2018, pp. 1115–1121. doi: 10.1109/ISPA/IUCC.2017.00168. Fortinet, “What is Quality of Service QoS in Network.” Accessed: Apr. 21, 2023. [Online]. Available: https://www.fortinet.com/resources/cyberglossary/qos-quality-of-service A. Raake et al., “Qualinet White Paper on Definitions of Quality of Experience,” Novi Sad, Mar. 2013. International Telecommunication Union, “T-REC-P.800-199608.” Accessed: Apr. 21, 2023. [Online]. Available: https://www.itu.int/rec/T-REC-P.800-199608-I Huawei mLab and iLab, “Technical White Paper on Mobile Bearer Network Requirements for Mobile Video Services Joint Release by Huawei mLab and iLab.” A. Ben Letaifa, G. Maher, and S. Mouna, “ML based QoE enhancement in SDN context: Video streaming case,” in 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, Jun. 2017, pp. 103–108. doi: 10.1109/IWCMC.2017.7986270. F. Artuğer and F. Özkaynak, “Görüntü Sıkıştırma Algoritmalarının Performans Analizi İçin Değerlendirme Rehberi,” International Journal of Pure and Applied Sciences, vol. 8, no. 1, pp. 102–110, Jun. 2022, doi: 10.29132/ijpas.1012013. A. Kumar and D. Anand, “Load balancing for Software Defined Network using Machine learning,” Turkish Journal of Computer and Mathematics Education, vol. 12, no. 2, pp. 527–535, Apr. 2021. B. Babayigit and B. Ulu, “Deep learning for load balancing of SDN-based data center networks,” International Journal of Communication Systems, vol. 34, no. 7, May 2021, doi: 10.1002/dac.4760. G. S. Begam, M. Sangeetha, and N. R. Shanker, “Load Balancing in DCN Servers through SDN Machine Learning Algorithm,” Arab J Sci Eng, vol. 47, no. 2, pp. 1423–1434, Feb. 2022, doi: 10.1007/s13369-021-05911-1. S. WilsonPrakash and P. Deepalakshmi, “Artificial Neural Network Based Load Balancing On Software Defined Networking,” in 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Tamilnadu, India: IEEE, Apr. 2019, pp. 1–4. doi: 10.1109/INCOS45849.2019.8951365. A. Filali, Z. Mlika, S. Cherkaoui, and A. Kobbane, “Preemptive SDN Load Balancing with Machine Learning for Delay Sensitive Applications,” IEEE Trans Veh Technol, vol. 69, no. 12, pp. 15947–15963, Dec. 2020, doi: 10.1109/TVT.2020.3038918. S. Liang, W. Jiang, F. Zhao, and F. Zhao, “Load Balancing Algorithm of Controller Based on SDN Architecture Under Machine Learning,” Journal of Systems Science and Information, vol. 8, no. 6, pp. 578–588, Dec. 2020, doi: 10.21078/JSSI-2020-578-11. Mininet.org, “Mininet.” Accessed: Dec. 25, 2023. [Online]. Available: mininet.org Floodlight, “Floodlight Controller.” Accessed: Dec. 25, 2023. [Online]. Available: https://floodlight.atlassian.net/wiki/spaces/floodlightcontroller/overview Tracy L. LaQuey, “NSFNET,” in The User’s Directory of Computer Networks, Elsevier, 1990, pp. 247–250. doi: 10.1016/B978-1-55558-047-6.50032-1.