•  
  •  
 

Keywords

Quality of Experience, quality of service, Artificial neural, Packet loss reduction, Delay and jitter optimization

Document Type

Research Paper

Abstract

In modern networking, ensuring both Quality of Experience (QoE) and Quality of Service (QoS) is paramount due to the increasing demand for efficient, reliable, and high-performance networks. With the surge in data traffic, traditional network management techniques often struggle to maintain optimal service levels, particularly in addressing challenges such as packet loss, excessive delay, and jitter. This paper presents an approach that integrates an Artificial Neural Network (ANN) into a multi-level queuing system, combined with Weighted Round Robin (WRR) scheduling, to address these persistent issues effectively. The key innovation lies in the dynamic and intelligent adaptation of network resource allocation based on real-time traffic conditions, which significantly outperforms static management methods. The proposed solution optimizes performance across various types of network traffic, enabling efficient handling of voice, video, and other data packets. This enhancement results in measurable improvements in both QoE and QoS, establishing the approach as an invaluable tool for network operators and service providers striving to meet the growing demands of today’s dynamic network environments. The system offers an adaptable and forward-looking solution for next-generation networks. Its effectiveness is demonstrated through a substantial reduction in average delay to 10.842 ms, jitter to 0.031 ms, and packet loss to 0–1%.

References

A. S. Ajrash, R. F. Ghani, and L. Al-Jobouri, Quality of experience (QoE) measurement algorithm for transmitted video, 2018 10th Electron. Comput. Sci. Eng., (CEEC), 2018. https://doi.org/10.1109/CEEC.2018.8674213 R. E. Alhassany and R. F. Ghani, Audio-visual quality of experience prediction based on ELM model, Int. J. Intell. Eng. Syst., 15 (2022) 296–307. https://doi.org/10.22266/ijies2022.1031.27 H. Oleiwi, N. Saeed, H. Al-Taie, and D. Mhawi, An Enhanced interface selectivity technique to improve the QoS for the multi-homed node, Eng. Technol. J., vol 40 (2022) 101–109. https://doi.org/10.30684/etj.2022.133066.1165 M. A. Mohammed, M. AbdulMajid, B. A. Mustafa, and R. F. Ghani, Queueing theory study of round robin versus priority dynamic quantum time round robin scheduling algorithms, 2015 4th Int. Conf. Software Engineering and Computer Systems, 2015. https://doi.org/10.1109/ICSECS.2015.7333108 R. F. Ghani and A. S. Ajrash, Quality of experience metric of streaming video: A survey, Irq. J. Sci., 59 (2018) 1531–1537. https://ijs.uobaghdad.edu.iq/index.php/eijs/article/view/447 P. Uthansakul, P. Anchuen, M. Uthansakul, and A. A. Khan, Estimating and synthesizing QoE based on QoS measurement for improving multimedia services on cellular networks using ANN method, IEEE Trans. Netw. Serv. Manag., 17 (2020) 389–402. https://doi.org/10.1109/TNSM.2019.2946091 A. B. Khudhair and R. F. Ghani, IoT based smart video surveillance system usingconvolutional neural network, 2020 6th Int. Eng. Conf. Sustainable Technology and Development (IEC), 2020. https://doi.org/10.1109/IEC49899.2020.9122901 N. Moussa, A. E. El Alaoui, and C. Chaudet, A novel approach of WSN routing protocols comparison for forest fire detection, Wirel. Networks, 26 (2020) 1857–1867. https://doi.org/10.1007/s11276-018-1872-3 A. M. R. Abdali and R. F. Ghani, Robust real-time fire detector using CNN And LSTM, 2019 IEEE Student Conf. on Research and Development (SCOReD), 2019. https://doi.org/10.1109/SCORED.2019.8896246 S. A. Alghamdi, Cellular v2x with d2d communications for emergency message dissemination and qos assured routing in 5g environment, IEEE Access, 9 (2021) 56049–56065. https://doi.org/10.1109/ACCESS.2021.3071349 Z. A. Abood, H. B. Taher, and R. F. Ghani, Detection of road traffic congestion using V2V communication based on iot, Iraqi J. Sci., 62 (2021) 335–345. https://doi.org/10.24996/ijs.2021.62.1.32 Al. M. R. Abdali and R. F. Ghani, Robust character recognition for optical and natural images using deep learning, IEEE Student Conf. Research and Development (SCOReD), 2019. https://doi.org/10.1109/SCORED.2019.8896354 D. Aureli, A. Cianfrani, M. Listanti, M. Polverini, and S. Secci, Augmenting DiffServ operations with dynamically learned classes of services, Comput. Networks, 202 (2022) 108624. https://doi.org/10.1016/J.COMNET.2021.108624 A. Mousa and M. Abdullah, A Survey on load balancing, routing, and congestion in SDN, Eng. Technol. J., 40 (2022) 1–11. https://doi.org/10.30684/etj.2022.132886.1150 M. Sari, I. Sembiring, and H. D. Purnomo, Analysis of frontier’s internet network quality, J. Bumigora Inf. Technol., 4 (2022) 205–216. https://doi.org/10.30812/bite.v4i2.2184 Z. Yu, C. Hu, J. Wu, X. Sun, V. Braverman et al, Programmable packet scheduling with a single queue, in SIGCOMM 2021 – Proc. of the ACM SIGCOMM 2021 Conf., Association for Computing Machinery, 2021, 179–193. https://doi.org/10.1145/3452296.3472887 Y. Su, P. Jiang, H. Chen, and X. Deng, A QoS-guaranteed and congestion-controlled SDN routing strategy for smart grid, Appl. Sci., 12 (2022) 7629. https://doi.org/10.3390/app12157629 M. O. Elbasheer, A. Aldegheishem, N. Alrajeh, and J. Lloret, video streaming adaptive QoS routing with resource reservation (VQoSRR) model for SDN networks, Electronics, 11 (2022) 1252. https://doi.org/10.3390/ELECTRONICS11081252 M. Pundir and J. K. Sandhu, A systematic review of quality of service in wireless sensor networks using machine learning: recent trend and future vision, J. Network Comput. Appl., 188 (2021) 03084. https://doi.org/10.1016/j.jnca.2021.103084 Z. G. Hu, H. R. Yan, T. Yan, H. J. Geng, and G. Q. Liu, Evaluating QoE in VoIP networks with QoS mapping and machine learning algorithms, Neurocomputing, 386 (2020) 63–83. https://doi.org/10.1016/j.neucom.2019.12.072 V. Surya, N. Reddy, and J. Mungara, Artificial intelligence machinelearning in healthcare systemfor improving quality of service, Cardiometry, (2022) 1161–1167. https://doi.org/10.18137/cardiometry.2022.25.11611167 T. A. Nguyen, H. B. Ly, H. V. T. Mai, and V. Q. Tran, Using ANN to estimate the critical buckling load of y shaped cross-section steel columns, Sci. Program., 2021. https://doi.org/10.1155/2021/5530702 T. O. Hodson, Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not, Geosci. Model Dev., 15 (2022) 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022 S. Louvros, M. Paraskevas, and T. Chrysikos, QoS-Aware resource management in 5G and 6G cloud-based architectures with priorities, Information, 14 (2023) 175. https://doi.org/10.3390/info14030175 S. Ahmed, M. Ali, A. Baz, H. Alhakami, B. Akbar, I.A. Khan, A. Ahmed, M. Junaid, A Design of packet scheduling algorithm to enhance QoS in high-speed downlink packet access (HSDPA) core network, Int. J. Adv. Comput. Sci. Appl., 11 (2020) 596- 602. https://doi.org/10.14569/IJACSA.2020.0110478 M. Beshley, N. Kryvinska, H. Beshley, O. Yaremko, and J. Pyrih, Virtual router design and modeling for future networks with qos guarantees, Electronics, 10 (2021) 1139. https://doi.org/10.3390/electronics10101139 S. Q. A. Shah, F. Z. Khan, A. Baig, and M. M. Iqbal, A QoS model for real-time application in wireless network using software defined network, Wirel. Pers. Commun., 112 (2020) 1025–1044. https://doi.org/10.1007/s11277-020-07089-5 U. Tomer and P. Gandhi, An enhanced software framework for improving QoS in IoT, 12 (2022) 9172–9177. https://doi.org/10.48084/etasr.5095 R. F. Ghani and L. Al-Jobouri, Packet loss optimization in router forwarding tasks based on the particle swarm algorithm, Electronics, 12 (2023) 462. https://doi.org/10.3390/ELECTRONICS12020462 S. Peñaherrera, C. Baena, S. Fortes, S. Member, and R. Barco, ML-powered KQI estimation for XR services. A case study on 360-video, Techrxiv, 2024. https://doi.org/10.36227/techrxiv.171707289.94776977/v1 L. Cristobo, E. Ibarrola, I. Casado-O’Mara, and L. Zabala, Global quality of service (QoX) management for wireless networks, Electronics, 13 (2024) 3113. https://doi.org/10.3390/electronics13163113 C. Callegari, S. Giordano, and M. Pagano, On the proper choice of datasets and traffic features for realtime anomaly detection, J. Phys.: Conf. Ser., 2091 (2021) 012001. https://doi.org/10.1088/1742-6596/2091/1/012001 D. Aureli, A. Cianfrani, A. Diamanti, J. M. Sanchez Vilchez, and S. Secci, Going beyond diffserv in IP traffic classification, Proc. IEEE/IFIP Netw. Oper. Manag. Symp. 2020 Manag. Age Softwarization Artif. Intell. NOMS 2020, Apr. 2020. https://doi.org/10.1109/NOMS47738.2020.9110430 I. Saidu, N. A. Shinkafi, A. Roko, and A. U. Moyi, A Prioritized load aware weighted round robin (PLAWRR) algorithm in broadband wireless networks, Eur. J. Electr. Eng. Comput. Sci., 3 (2019)1–4. https://doi.org/10.24018/ejece.2019.3.4.112

Highlights

Enhancing both Quality of Experience (QoE) and Quality of Service (QoS) is a key focus of the study The approach integrates Artificial Neural Networks with multi-level queuing and Weighted Round Robin scheduling The method achieves a reduction in average delay to 10.842 ms, jitter to 0.031 ms, and packet loss to 0–1% The solution offers operators and providers better methods for managing diverse traffic types

DOI

10.30684/etj.2024.154491.1835

First Page

234

Last Page

243

Download

Share

COinS