Traffic Engineering with Specified Quality of Service Parameters in Software-defined Networks

Traffic Engineering with Specified Quality of Service Parameters in Software-defined Networks

Автор: Artem Volokyta, Alla Kogan, Oleksii Cherevatenko, Dmytro Korenko, Dmytro Oboznyi, Yurii Kulakov

Журнал: International Journal of Computer Network and Information Security @ijcnis

Статья в выпуске: 5 vol.16, 2024 года.

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A method of traffic engineering (TE) based on the method of multi-path routing is proposed in the study. Today, one of the main challenges in networking is to organize an efficient TE system that will provide such parameters of quality of service (QoS) as the allowable value of packet loss and time for traffic re-routing. Traditional one-way routing facilities do not provide the required quality of service (QoS) parameters for TE. Modern computer networks use static and dynamic routing algorithms, which are characterized by big time complexity and a large amount of service information. This negatively affects the overall state of the network, namely: leads to network congestion, device failure, loss of information during routing and increases the time for traffic re-routing. Research has shown that the most promising way to solve the TE problem in computer networks is a comprehensive approach, which consists of multi-path routing, SDN technology and monitoring of the overall situation of the network. This paper proposes a method of traffic engineering in a software-defined network with specified quality of service parameters, which has reduced the time of traffic re-routing and the percentage of packet loss due to the combination of the centralized TE method and multi-path routing. From a practical point of view, the obtained method, will improve the quality of service in computer networks in comparison with the known method of traffic construction.

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Traffic Engineering, SDN, Quality of Service, Bandwidth, Channel Congestion, Traffic Reconfiguration

Короткий адрес: https://sciup.org/15019469

IDR: 15019469   |   DOI: 10.5815/ijcnis.2024.05.01

Список литературы Traffic Engineering with Specified Quality of Service Parameters in Software-defined Networks

  • M. A. Al-Shareeda, A. A. Alsadhan, H. H. Qasim, and S. Manickam, “Software defined networking for internet of things: review, techniques, challenges, and future directions,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 1, pp. 638–647, Feb. 2024, doi: https://doi.org/10.11591/eei.v13i1.6386.
  • B. Goswami, M. Kulkarni, and J. Paulose, “A Survey on P4 Challenges in Software Defined Networks: P4 Programming,” IEEE Access, vol. 11, pp. 54373–54387, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3275756.
  • B. Babayigit, B. Ulu, and M. Abubaker, “Survey Studies of Software-Defined Networking: A Systematic Review and Meta-analysis,” Engineering Journal, vol. 27, no. 10, pp. 33–66, Oct. 2023, doi: https://doi.org/10.4186/ej.2023.27.10.33.
  • M. S. Farooq, S. Riaz, and A. Alvi, “Security and Privacy Issues in Software-Defined Networking (SDN): A Systematic Literature Review,” Electronics, vol. 12, no. 14, p. 3077, Jan. 2023, doi: https://doi.org/10.3390/electronics12143077.
  • N. S. Shaji and R. Muthalagu, “Survey on security aspects of distributed softwaredefined networking controllers in an enterprise SDWLAN,” Digital Communications and Networks, 2023, doi: https://doi.org/10.1016/j.dcan.2023.09.004.
  • P. A. D. S. N. Wijesekara and S. Gunawardena, “A Comprehensive Survey on Knowledge-Defined Networking,” Telecom, vol. 4, no. 3, pp. 477–596, Sep. 2023, doi: https://doi.org/10.3390/telecom4030025.
  • S. H. Hasan, “Optimizing Software-Defined Networks with Fuzzy Logic-Based Enhancement of Openflow Protocol,” BIO Web of Conferences, vol. 97, pp. 00105–00105, Jan. 2024, doi: https://doi.org/10.1051/bioconf/20249700105.
  • M. C. Saxena, M. Sabharwal, and P. Bajaj, “Review of SDN-based load-balancing methods, issues, challenges, and roadmap,” International journal of electrical and computer engineering systems, vol. 14, no. 9, pp. 1031–1049, Nov. 2023, doi: https://doi.org/10.32985/ijeces.14.9.8.
  • V. Shchur and A. Ali AlZubi, “OPTIMIZED ADAPTIVE LOAD BALANCING METHOD IN SDN NETWORKS USING THE ADAPTIVE ANT COLONY APPROACH,” Measuring Equipment and Metrology, vol. 84, no. 4, pp. 62–66, Jan. 2023, doi: https://doi.org/10.23939/istcmtm2023.04.062.
  • M. Kulkarni, B. Goswami, and J. Paulose, “HULA: Dynamic and Scalable Load Balancing Mechanism for Data Plane of SDN,” in 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–9. doi: https://doi.org/10.1109/ICECCT56650.2023.10179849.
  • A. Khalid, R. G. Anand, and A. Malviya, “A Development of Dynamic EndToEnd Slicing Network Along with the Offline SLAMethod Integrated with Deep Learning for 5G/6G Networks,” in 2024 1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), pp. 1–5. doi: https://doi.org/10.1109/ISTEMS60181.2024.10560180.
  • B. Roy and D. Tiwari, “StarShip: Mitigating I/O bottlenecks in serverless computing for scientific workflows,” Proc. ACM Meas. Anal. Comput. Syst., vol. 8, Art. no. 1, 2024, doi: https://doi.org/10.1145/3639028.
  • E. Kim, K. Lee, and C. Yoo, “DepCon: Achieving Network SLO for High Performance Clouds,” in EuroPar 2021: Parallel Processing Workshops, R. Chaves, B. Heras, A. Ilic, D. Unat, R. M. Badia, A. Bracciali, P. Diehl, A. Dubey, O. Sangyoon, L. Scott, and L. Ricci, Eds., Cham: Springer International Publishing, 2022, pp. 339–351.
  • B. V. Natesha, N. Kumar Sharma, S. Domanal, and M. Reddy, “GWOTS: Grey Wolf Optimization Based Task Scheduling at the Green Cloud Data Center,” in 2018 14th International Conference on Semantics, Knowledge and Grids (SKG), pp. 181–187. doi: https://doi.org/10.1109/SKG.2018.00034.
  • P. Kang, P. Lama, and S. U. Khan, “SLOaware Virtual Rebalancing for Edge Stream Processing,” in 2021 IEEE International Conference on Cloud Engineering (IC2E), pp. 126–135. doi: https://doi.org/10.1109/IC2E52221.2021.00027.
  • K. Santos, S. Nejati, M. Sabetzadeh, and E. Y. Nakagawa, “Self-adaptive, Requirements-driven Autoscaling of Microservices,” arXiv (Cornell University), Feb. 2024, doi: https://doi.org/10.48550/arxiv.2403.08798.
  • S. Wei, J. Zhou, and S. Chen, “DelayAware Multipath Parallel SFC Orchestration,” IEEE Access, vol. 10, pp. 120035–120055, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3221744.
  • R. Barbosa and M. Araujo, “AIdriven Humancentric Control Interfaces for Industry 4.0 with Rolebased Access,” in 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1–6. doi: https://doi.org/10.1109/INISTA55318.2022.9894247.
  • N. Walubita, B. B. Dash, R. N. Satapathy, A. Tripathy, S. S. Patra, and U. Chandra de, “EnergyEfficiency in SoftwareDefined Networking: Rule Placement Approach,” in 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), pp. 1222–1228. doi: https://doi.org/10.1109/ICSSAS57918.2023.10331777.
  • Y. Kulakov, A. Kohan, and S. Kopychko, “Traffic Orchestration in Data Center Network Based on SoftwareDefined Networking Technology,” in Advances in Computer Science for Engineering and Education II, Z. Hu, S. Petoukhov, I. Dychka, and M. He, Eds., Cham: Springer International Publishing, 2020, pp. 228–237.
  • R. P. Dhanya and V. S. Anitha, “Implementation and Performance Evaluation of Load Balanced Routing in SDN based Fat Tree Data Center,” in 2023 6th International Conference on Information Systems and Computer Networks (ISCON), pp. 1–6. doi: https://doi.org/10.1109/ISCON57294.2023.10112200.
  • W. M. AlShammari and M. J. F. Alenazi, “Performance Analysis of a Graph-Theoretic Load Balancing Method for Data Centers,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 8, 2020, doi: https://doi.org/10.14569/ijacsa.2020.0110881.
  • N. S. Radam, S. Al-Janabi, and K. S. Jasim, “Using metaheuristics to improve the placement of multi-controllers in software-defined networking enabled clouds,” Periodicals of Engineering and Natural Sciences (PEN), vol. 10, no. 4, p. 79, Jul. 2022, doi: https://doi.org/10.21533/pen.v10i4.3114.
  • M. M. Tajiki et al., “CECT: computationally efficient congestion avoidance and traffic engineering in software defined cloud data centers,” Cluster Computing, vol. 21, no. 4, pp. 1881–1897, 2018, doi: https://doi.org/10.1007/s1058601828156.
  • S. Tomovic and I. Radusinovic, “Traffic engineering approach to virtuallink provisioning in softwaredefined ISP networks,” in 2017 25th Telecommunication Forum (TELFOR), pp. 1–4. doi: https://doi.org/10.1109/TELFOR.2017.8249296.
  • D. Sun, K. Zhao, Y. Fang, and J. Cui, “Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN,” Future Internet, vol. 10, no. 7, p. 64, Jul. 2018, doi: https://doi.org/10.3390/fi10070064.
  • S. Nayak and B. Bhattacharyya, “Software Defined Networkingbased Multiuser System Models,” in 2023 International Conference on Next Generation Electronics (NEleX), pp. 1–6. doi: https://doi.org/10.1109/NEleX59773.2023.10420937.
  • Oleg Barabash, Yuri Kravchenko, Vadym Mukhin, Yaroslav Kornaga, Olga Leshchenko, "Optimization of Parameters at SDN Technologie Networks", International Journal of Intelligent Systems and Applications, Vol.9, No.9, pp.1-9, 2017.
  • Y. Podzirey and A. Kohan, “Method of forming multipath disjoint channels in a program-configured network,” Bulletin of NTUU “KPI”. Informatics, management and computer engineering, vol. 66, pp. 137–141, 2017.
  • Abdolhossein Fathi, Keihaneh Kia,"A Centralized Controller as an Approach in Designing NoC", International Journal of Modern Education and Computer Science, Vol.9, No.1, pp.60-67, 2017.
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