DOI : https://doi.org/10.18517/ijaseit.13.6.19409

Dynamic QoS: Automatically Modifying QoS Queue's Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement

Muhammad Fendi Osman (1), Mohd Rizal Mohd Isa (2), Mohammad Adib Khairuddin (3), Mohd Afizi Mohd Shukran (4), Noor Afiza Mat Razali (5), Nur Diyana Kamarudin (6), Amin Suharjono (7)
(1) Computer Science Department, National Defense University of Malaysia, Malaysia
(2) Computer Science Department, National Defense University of Malaysia, Malaysia
(3) Computer Science Department, National Defense University of Malaysia, Malaysia
(4) Computer Science Department, National Defense University of Malaysia, Malaysia
(5) Computer Science Department, National Defense University of Malaysia, Malaysia
(6) Computer Science Department, National Defense University of Malaysia, Malaysia
(7) Department of Electrical Engineering, Politeknik Negeri Semarang, Prof. Sudarto Street, Semarang, 50275, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
[1]
M. F. Osman, “Dynamic QoS: Automatically Modifying QoS Queue’s Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 6, pp. 2112–2119, Dec. 2023.
Citation Format :
The heterogeneous data traffic of today's network is a huge challenge to existing best-effort network technology, particularly in the context of large Ethernet, which handles hundreds to thousands of users. The existing conventional best-effort network technology is no longer efficient to handle the diversity of traffic types in the network and requires network management equipment such as Quality of Service (QOS). Usually, QOS is implemented on the gateway router. However, for better network performance and management, to guarantee high priority for sensitive traffic like video conferencing, Voice over Internet Protocol (VoIP), and streaming media within an internal network, it is nice to have QoS implemented on each router in the LAN network, starting from the access router to the gateway router. This paper is to demonstrate the effectiveness of the proposed dynamic QoS that has been developed and deployed in the LAN, purposely to provide adequate bandwidth for sensitive traffic when the network utilization is high and congested, by automatically modifying the QoS Queue's Maximum Bandwidth Rate-Limit of the best-effort traffic queue of the related router. The performance of the proposed developed dynamic QoS was evaluated via a comparison study before and after the dynamic QoS was presented in the network simulation environment that was built using Mininet. Results from the testing show that the developed dynamic QoS can improve the network's performance by automatically giving the appropriate bandwidth for sensitive traffic on the fly while needed/on demand.

N. Z. M. Safar, N. Abdullah, H. Kamaludin, S. Abd Ishak, and M. R. M. Isa, "Characterising and detection of botnet in P2P network for UDP protocol," Indonesian Journal of Electrical Engineering and Computer Science, vol. 18, no. 3, pp. 1584-1595, 2020.

M. F. Mustafa et al., "Student Perception Study on Smart Campus: A Case Study on Higher Education Institution," Malaysian Journal of Computer Science, pp. 1-20, 2021.

M. R. M. Isa, M. A. Khairuddin, M. A. B. M. Sulaiman, M. N. Ismail, M. A. M. Shukran, and A. A. B. Sajak, "SIEM Network Behaviour Monitoring Framework using Deep Learning Approach for Campus Network Infrastructure," International Journal of Electrical and Computer Engineering Systems, pp. 9-21, 2021.

S. Peros, H. Janjua, S. Akkermans, W. Joosen, and D. Hughes, "Dynamic QoS support for IoT backhaul networks through SDN," in 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), 2018, pp. 187-192.

Fortinet, "What is Quality of Service (QoS) in Networking?" 2022.

R. Gandotra and L. Perigo, "SDVoIP—A software-defined VoIP framework for SIP and dynamic QoS," Comput J, vol. 64, no. 2, pp. 254-263, 2021.

A. N. Abosaif and H. S. Hamza, "Quality of service-aware service selection algorithms for the internet of things environment: A review paper," Array, vol. 8, p. 100041, 2020.

S. Messaoudi, A. Ksentini, and C. Bonnet, "SDN Framework for QoS provisioning and latency guarantee in 5G and beyond," in 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), 2023, pp. 587-592.

D. Sarma and H. Kumar, "A Survey on Machine Learning and Deep Learning based Quality of Service aware Protocols for Software Defined Networks," 2021.

O. N. Foundation, "Software-defined networking: the new norm for networks," ONF White Paper, vol. 2, pp. 2-6, 2012.

J. C. C. Chica, J. C. Imbachi, and J. F. B. Vega, "Security in SDN: A comprehensive survey," Journal of Network and Computer Applications, vol. 159, p. 102595, 2020.

S. S. A. Gilani, A. Qayyum, R. N. Bin Rais, and M. Bano, "SDNMesh: An SDN based routing architecture for wireless mesh networks," IEEE Access, vol. 8, pp. 136769-136781, 2020.

N. T. Hoang, H.-N. Nguyen, H.-A. Tran, and S. Souihi, "A novel adaptive east-West Interface for a heterogeneous and distributed SDN network," Electronics (Basel), vol. 11, no. 7, p. 975, 2022.

M. W. Nadeem, H. G. Goh, V. Ponnusamy, and Y. Aun, "DDoS Detection in SDN using Machine Learning Techniques.," Computers, Materials & Continua, vol. 71, no. 1, 2022.

N. Ahmed et al., "Network threat detection using machine/deep learning in sdn-based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction," Sensors, vol. 22, no. 20, p. 7896, 2022.

S. Ahmad and A. H. Mir, "Scalability, consistency, reliability and security in SDN controllers: a survey of diverse SDN controllers," Journal of Network and Systems Management, vol. 29, pp. 1-59, 2021.

Z. Fan, J. Yao, X. Yang, Z. Wang, and X. Wan, "A multi-controller placement strategy based on delay and reliability optimization in SDN," in 2019 28th wireless and optical communications conference (WOCC), 2019, pp. 1-5.

L. Peterson, C. Cascone, and B. Davie, Software-defined networks: a systems approach. Systems Approach, LLC, 2021.

B. P. R. Killi and S. V. Rao, "Controller placement in software defined networks: A comprehensive survey," Computer Networks, vol. 163, p. 106883, 2019.

R. Wazirali, R. Ahmad, and S. Alhiyari, "SDN-openflow topology discovery: An overview of performance issues," Applied Sciences, vol. 11, no. 15, p. 6999, 2021.

A. Khater and M. R. Hashemi, "Dynamic Flow Management Based on DiffServ in SDN Networks," in Electrical Engineering (ICEE), Iranian Conference on, 2018, pp. 1505-1510.

M. Rodriguez, R. F. Moyano, N. Pí©rez, D. Riofrio, and D. Benitez, “Path Planning Optimization in SDN Using Machine Learning Techniques,” in 2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM), 2021, pp. 1-6.

N. N. Josbert, H. N. Joyce, J. Wang, and M. J. Bosco, "End-to-end QoS Routing Scheme in Industrial Internet of Things Managed by Software-Defined Networking Platform," in 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), 2021, pp. 542-549.

A. I. Owusu and A. Nayak, "An intelligent traffic classification in sdn-iot: A machine learning approach," in 2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), 2020, pp. 1-6.

V. Deart, V. Mankov, and I. Krasnova, "Development of a Feature Matrix for Classifying Network Traffic in SDN in Real-Time Based on Machine Learning Algorithms," in 2020 International Scientific and Technical Conference Modern Computer Network Technologies (MoNeTeC), 2020, pp. 1-9.

N. P. K. Goud, G. S. C. Reddy, and A. Maryposonia, "Traffic Classification of SDN Network using Machine Learning Algorithms," in 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), 2022, pp. 1181-1185.

M. Shafiq, X. Yu, A. A. Laghari, L. Yao, N. K. Karn, and F. Abdessamia, "Network traffic classification techniques and comparative analysis using machine learning algorithms," in 2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016, pp. 2451-2455.

M. Nsaif, G. Koví¡sznai, M. Abboosh, A. Malik, and R. de Frí©in, "ML-Based Online Traffic Classification for SDNs," in 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS), 2022, pp. 217-222.

A. Malik, R. de Frí©in, M. Al-Zeyadi, and J. Andreu-Perez, "Intelligent SDN traffic classification using deep learning: Deep-SDN," in 2020 2nd International Conference on Computer Communication and the Internet (ICCCI), 2020, pp. 184-189.

Z. Long and W. Jinsong, "Network traffic classification based on a deep learning approach using netflow data," Comput J, p. bxac049, 2022.

Z. Wu, Y. Dong, X. Qiu, and J. Jin, "Online multimedia traffic classification from the QoS perspective using deep learning," Computer Networks, vol. 204, p. 108716, 2022.

W. Wei, H. Gu, W. Deng, Z. Xiao, and X. Ren, "ABL-TC: A lightweight design for network traffic classification empowered by deep learning," Neurocomputing, vol. 489, pp. 333-344, 2022.

A. I. Owusu and A. Nayak, "A framework for QoS-based routing in SDNs using deep learning," in 2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020, pp. 1-6.

Z. Wu, Y. Dong, X. Qiu, and J. Jin, "Online multimedia traffic classification from the QoS perspective using deep learning," Computer Networks, vol. 204, p. 108716, 2022.

A. Wani and R. Khaliq, "SDN-based intrusion detection system for IoT using deep learning classifier (IDSIoT-SDL)," CAAI Trans Intell Technol, vol. 6, no. 3, pp. 281-290, 2021.

R. Batra, V. K. Shrivastava, and A. K. Goel, "Anomaly Detection over SDN Using Machine Learning and Deep Learning for Securing Smart City," in Green Internet of Things for Smart Cities, CRC Press, 2021, pp. 191-204.

M. R. Hadi and A. S. Mohammed, "A novel approach to network intrusion detection system using deep learning for Sdn: Futuristic approach," arXiv preprint arXiv:2208.02094, 2022.

B. BaÄŸirí¶z, M. Gí¼zel, U. YavanoÄŸlu, and S. í–zdemir, “QoS Prediction Methods in IoT A Survey,” in 2019 IEEE International Conference on Big Data (Big Data), 2019, pp. 2128-2133.

CiscoPress, "Network Fundamentals: Introduction to Network Performance Measurement." 2022.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).