A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs
Abstract
In this paper, a QoS-aware traffic classification framework for software defined networks is proposed. Instead of identifying specific applications in most of the previous work of traffic classification, our approach classifies the network traffic into different classes according to the QoS requirements, which provide the crucial information to enable the fine-grained and QoS-aware traffic engineering. The proposed framework is fully located in the network controller so that the real-time, adaptive, and accurate traffic classification can be realized by exploiting the superior computation capacity, the global visibility, andthe inherent programmability of the network controller. More specifically, the proposed framework jointly exploits deep packet inspection (DPI) and semi-supervised machine learning so that accurate traffic classification can be realized, while requiring minimal communications between the network controller and the SDN switches. Based on the real Internet data set, the simulation results show the proposed classification framework can provide good performance in terms of classification accuracy and communication costs
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The accuracy of classifying long-lived flows to QoS classes exceeds 90% in Wang et al.'s evaluation.
“In the evaluation, the accuracy of classifying long-lived flows to QoS classes exceed 90%.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [462]
Wang et al. [462] propose a framework to classify network traffic to QoS classes rather than applications.
“In contrast, Wang et al. [462] propose a framework to classify network traffic to QoS classes rather than applications.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [462]
The Laplacian-SVM classifier uses flow features from the first twenty packets to classify flows into QoS classes.
“The Laplacian-SVM classifier employed uses flow features from the first twenty packets to classify flows into QoS classes.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [462]
Cites
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934 · see citing works
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Williams, C. K. I. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s269 · Cited by 4,390 · see citing works
(2006) · Cited by 3,624 · see citing works
Pattern recognition and machine learning
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,782 · see citing works
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Atiya, A. F. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.848998 · Cited by 2,248 · see citing works
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
BelkinMikhail, NiyogiPartha, & SindhwaniVikas (2006) · Journal of Machine Learning Research · doi:10.5555/1248547.1248632 · Cited by 2,129 · see citing works
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. V. (2005) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/1064212.1064220 · Cited by 1,216 · see citing works
Pattern recognition and machine learning
(1993) · Choice Reviews Online · doi:10.5860/choice.30-3308 · Cited by 1,046 · see citing works
Toward the Accurate Identification of Network Applications
Moore, A. W., & Papagiannaki, K. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31966-5_4 · Cited by 801 · see citing works
Traffic classification using clustering algorithms
Erman, J., Arlitt, M. F., & Mahanti, A. (2006) · doi:10.1145/1162678.1162679 · Cited by 712 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Semi-Supervised Graph-Based Hyperspectral Image Classification
Camps‐Valls, G., Bandos, T. V., & Zhou, D. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2007.895416 · Cited by 622 · see citing works
A roadmap for traffic engineering in SDN-OpenFlow networks
Akyildiz, I. F., Lee, A., Wang, P., Luo, M., & Chou, W. (2014) · Computer Networks · doi:10.1016/j.comnet.2014.06.002 · Cited by 536 · see citing works
Self-citationFlow Clustering Using Machine Learning Techniques
McGregor, A., Hall, M., Lorier, P., & Brunskill, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24668-8_21 · Cited by 521 · see citing works
Roughan, M., Sen, S., Spatscheck, O., & Duffield, N. (2004) · Proceedings of the 4th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1028788.1028805 · Cited by 494 · see citing works
Laplacian Support Vector Machines Trained in the PrimalPreprint
Melacci, S., & Belkin, M. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0909.5422 · Cited by 315 · see citing works
NetFPGA--An Open Platform for Gigabit-Rate Network Switching and Routing
Lockwood, J. W., McKeown, N., Watson, G., Gibb, G., Hartke, P., Naous, J., Raghuraman, R., & Luo, J. (2007) · IEEE International Conference on Microelectronic Systems Education (MSE) · doi:10.1109/mse.2007.69 · Cited by 315 · see citing works
Offline/realtime traffic classification using semi-supervised learning
Erman, J., Mahanti, A., Arlitt, M., Cohen, I., & Williamson, C. (2007) · Performance Evaluation · doi:10.1016/j.peva.2007.06.014 · Cited by 287 · see citing works
Laplacian Support Vector Machines Trained in the Primal
MelacciStefano, & BelkinMikhail (2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2021038 · Cited by 217 · see citing works
Thu, T. N. T., & Armitage, G. J. (2006) · Conference on Local Computer Networks · doi:10.1109/lcn.2006.322122 · Cited by 123 · see citing works
(1998) · The MIT Press eBooks · doi:10.7551/mitpress/3348.003.0005 · Cited by 114 · see citing works
Jointly optimized QoS-aware virtualization and routing in software defined networks
Lin, S., Wang, P., & Luo, M. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.08.003 · Cited by 29 · see citing works
Self-citationRethinking Flow Classification in SDN
Farhadi, H., & Nakao, A. (2014) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2014.24 · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810 · see citing works
Xie, J., Yu, F. R., Huang, T., Xie, R., Liu, J., Wang, C., & Liu, Y. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2866942 · Cited by 700 · see citing works
Akyildiz, I. F., Lin, S.-C., & Wang, P. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.10.013 · Cited by 118 · see citing works
Self-citation
- Authors
- Wang, P., Lin, S.-C., & Luo, M.
- Affiliations
- Wichita State University (US)
- Georgia Institute of Technology (US)
- Huawei Technologies (China) (CN)
- Venue
- 2016 IEEE International Conference on Services Computing (SCC)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 760-765
- OpenAlex
- W2511505324
- Cited by
- 155
- References
- 23
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 15 · 2023: 14 · 2022: 21 · 2021: 20 · 2020: 30 · 2019: 20 · 2018: 13 · 2017: 7 · 2015: 2
- FWCI
- 11.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Services Computing (publishedVersion)
- Holmes Museum Of Anthropology (Wichita State University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- network traffic classification, software-defined networking, quality of service, semi-supervised learning, deep packet inspection, network controller, traffic engineering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
