Quality of Service Management Solution Becomes a Software-Defined Network Challenge
Article Sidebar
-
Control Separation, Data Plane, SDN
Abstract
The use of Software-Defined Networking (SDN) has created a huge leap in the management of computer networks. SDN offers flexibility and the ability to dynamically manage networks, but along with its advantages, it also brings significant challenges in managing quality of service (QoS). QoS is critical to maintaining performance and user experience in increasingly complex and distributed network environments. The research method uses Solution Development to address a quality of service (QoS) management challenge and solution in Software Defined Networking with the approach of designing and developing practical solutions to address QoS management issues and challenges in SDN environments. Control Separation and Data Plane are centralized network control at the SDN controller, while the data plane reside in the networks hardware, both capable of maintaining QoS and a challenges in SDN with a computer network approach that separates the control layer from the data layer, enabling more flexible and centralized network management. Research conclusions related to SDN Software Defined Networking quality of service management problems and solutions, focusing on the problem of separating management and data levels. The separations of control and data plane concept in SDN has great potential to improve flexibility, scalability and more efficient network management.
Full text article
References
Abbasi, S., Keshavarzi, B., Moore, F., Turner, A., Kelly, F. J., Dominguez, A. O., & Jaafarzadeh, N. (2019). Distribution and potential health impacts of microplastics and microrubbers in air and street dusts from Asaluyeh County, Iran. Environmental Pollution, 244, 153–164. https://doi.org/10.1016/j.envpol.2018.10.039
Agus Triansyah, F., Hejin, W., & Stefania, S. (2023). Factors Affecting Employee Performance: A Systematic Review. Journal Markcount Finance, 1(2), 118–127. https://doi.org/10.55849/jmf.v1i2.102
Albrecht, E., & Chin, K. J. (2020). Advances in regional anaesthesia and acute pain management: A narrative review. Anaesthesia, 75(S1). https://doi.org/10.1111/anae.14868
Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004
Archibald, M. M., Ambagtsheer, R. C., Casey, M. G., & Lawless, M. (2019). Using Zoom Videoconferencing for Qualitative Data Collection: Perceptions and Experiences of Researchers and Participants. International Journal of Qualitative Methods, 18, 160940691987459. https://doi.org/10.1177/1609406919874596
Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020). Service robot implementation: A theoretical framework and research agenda. The Service Industries Journal, 40(3–4), 203–225. https://doi.org/10.1080/02642069.2019.1672666
Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742. https://doi.org/10.1080/00207543.2017.1402140
Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2019). Network Intrusion Detection for IoT Security Based on Learning Techniques. IEEE Communications Surveys & Tutorials, 21(3), 2671–2701. https://doi.org/10.1109/COMST.2019.2896380
Coman, C., ?îru, L. G., Mese?an-Schmitz, L., Stanciu, C., & Bularca, M. C. (2020). Online Teaching and Learning in Higher Education during the Coronavirus Pandemic: Students’ Perspective. Sustainability, 12(24), 10367. https://doi.org/10.3390/su122410367
Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14(2), 241–258. https://doi.org/10.1007/s11704-019-8208-z
Giordani, M., Polese, M., Roy, A., Castor, D., & Zorzi, M. (2019). A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies. IEEE Communications Surveys & Tutorials, 21(1), 173–196. https://doi.org/10.1109/COMST.2018.2869411
Hüllermeier, E., & Waegeman, W. (2021). Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning, 110(3), 457–506. https://doi.org/10.1007/s10994-021-05946-3
Jain, N., Brock, J. L., Malik, A. T., Phillips, F. M., & Khan, S. N. (2019). Prediction of Complications, Readmission, and Revision Surgery Based on Duration of Preoperative Opioid Use: Analysis of Major Joint Replacement and Lumbar Fusion. Journal of Bone and Joint Surgery, 101(5), 384–391. https://doi.org/10.2106/JBJS.18.00502
Kang, Z., Wen, L., Chen, W., & Xu, Z. (2019). Low-rank kernel learning for graph-based clustering. Knowledge-Based Systems, 163, 510–517. https://doi.org/10.1016/j.knosys.2018.09.009
Modi, V., & Dunbrack, R. L. (2019). Defining a new nomenclature for the structures of active and inactive kinases. Proceedings of the National Academy of Sciences, 116(14), 6818–6827. https://doi.org/10.1073/pnas.1814279116
Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127–182. https://doi.org/10.1007/s10845-018-1433-8
Peng, X., & Liu, L. (2019). Information measures for q ?rung orthopair fuzzy sets. International Journal of Intelligent Systems, 34(8), 1795–1834. https://doi.org/10.1002/int.22115
Reed, G. M., First, M. B., Kogan, C. S., Hyman, S. E., Gureje, O., Gaebel, W., Maj, M., Stein, D. J., Maercker, A., Tyrer, P., Claudino, A., Garralda, E., Salvador?Carulla, L., Ray, R., Saunders, J. B., Dua, T., Poznyak, V., Medina?Mora, M. E., Pike, K. M., … Saxena, S. (2019). Innovations and changes in the ICD?11 classification of mental, behavioural and neurodevelopmental disorders. World Psychiatry, 18(1), 3–19. https://doi.org/10.1002/wps.20611
Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. D. F., & Rodrigues, F. A. (2019). Clustering algorithms: A comparative approach. PLOS ONE, 14(1), e0210236. https://doi.org/10.1371/journal.pone.0210236
Safiri, S., Kolahi, A.-A., Smith, E., Hill, C., Bettampadi, D., Mansournia, M. A., Hoy, D., Ashrafi-Asgarabad, A., Sepidarkish, M., Almasi-Hashiani, A., Collins, G., Kaufman, J., Qorbani, M., Moradi-Lakeh, M., Woolf, A. D., Guillemin, F., March, L., & Cross, M. (2020). Global, regional and national burden of osteoarthritis 1990-2017: A systematic analysis of the Global Burden of Disease Study 2017. Annals of the Rheumatic Diseases, 79(6), 819–828. https://doi.org/10.1136/annrheumdis-2019-216515
Shahapure, K. R., & Nicholas, C. (2020). Cluster Quality Analysis Using Silhouette Score. 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 747–748. https://doi.org/10.1109/DSAA49011.2020.00096
Tang, C., Zhu, X., Liu, X., Li, M., Wang, P., Zhang, C., & Wang, L. (2019). Learning a Joint Affinity Graph for Multiview Subspace Clustering. IEEE Transactions on Multimedia, 21(7), 1724–1736. https://doi.org/10.1109/TMM.2018.2889560
Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211–223. https://doi.org/10.1016/j.comnet.2019.01.024
Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020). Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. International Journal of Environmental Research and Public Health, 17(5), 1729. https://doi.org/10.3390/ijerph17051729
Wang, Y., Chen, Q., Hong, T., & Kang, C. (2019). Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges. IEEE Transactions on Smart Grid, 10(3), 3125–3148. https://doi.org/10.1109/TSG.2018.2818167
Wortham, J. M., Lee, J. T., Althomsons, S., Latash, J., Davidson, A., Guerra, K., Murray, K., McGibbon, E., Pichardo, C., Toro, B., Li, L., Paladini, M., Eddy, M. L., Reilly, K. H., McHugh, L., Thomas, D., Tsai, S., Ojo, M., Rolland, S., … Reagan-Steiner, S. (2020). Characteristics of Persons Who Died with COVID-19—United States, February 12–May 18, 2020. MMWR. Morbidity and Mortality Weekly Report, 69(28), 923–929. https://doi.org/10.15585/mmwr.mm6928e1
Zhan, K., Nie, F., Wang, J., & Yang, Y. (2019). Multiview Consensus Graph Clustering. IEEE Transactions on Image Processing, 28(3), 1261–1270. https://doi.org/10.1109/TIP.2018.2877335
Zhang, R., Chen, Z., Chen, S., Zheng, J., Büyüköztürk, O., & Sun, H. (2019). Deep long short-term memory networks for nonlinear structural seismic response prediction. Computers & Structures, 220, 55–68. https://doi.org/10.1016/j.compstruc.2019.05.006
Authors
Copyright (c) 2023 Muhajir Syamsu, Cai Jixiong, Lie Jie
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.