Abstract
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.
Keywords
Project Number
FGA 2023–1557
References
- 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.
Abstract
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.
Keywords
Ethical Statement
It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.
Supporting Institution
Bursa Uludağ University Scientific Research Projects Unit
Project Number
FGA 2023–1557
References
- 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.
Details
| Primary Language | English |
|---|---|
| Subjects | Software Engineering (Other) |
| Journal Section | Research Article |
| Authors | |
| Project Number | FGA 2023–1557 |
| Early Pub Date | September 24, 2025 |
| Publication Date | September 30, 2025 |
| Submission Date | December 10, 2024 |
| Acceptance Date | August 13, 2025 |
| Published in Issue | Year 2025 Volume: 8 Issue: 3 |
Cite
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