Loss classification in optical burst switching networks using machine learning techniques: improving the performance of TCP
Abstract
Optical burst switching (OBS) is considered as a contending technology for the core of the Internet in future. However, due to lack of the buffers, losses occur due to contention among simultaneously arriving bursts at the core nodes. Contention losses do not necessarily indicate a situation of congestion in the network. Thus differentiation (classification) of losses is essential in many applications to avoid false identification of congestion. In this paper, we propose a loss classification technique for the OBS networks based on machine learning techniques. We devise a new measure to differentiate between congestion and contention losses, which is derived from the observed losses, called the number of bursts between failures (NBBF). We observe that the NBBF follows a Gaussian distribution with different parameters for contention and congestion losses. This feature is used in differentiation. We use both a supervised learning technique (hidden Markov model (HMM)) and an unsupervised learning technique (expectation maximization (EM) clustering) on the observed losses and classify them into a set of states (clusters) after which an algorithm differentiates between the congestion and contention losses. We also demonstrate the use of loss differentiation in improving the performance of transport control protocol (TCP) over OBS networks. We modify congestion control mechanism of TCP suitably to arrive at two variants of TCP, HMM-TCP and EM-TCP. Their performance is compared with TCP NewReno, TCP SACK, and Burst TCP (X. Yu et al., Mar. 2004). Simulation results demonstrate the effectiveness and accuracy of the loss classification technique in different network scenarios.
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 TCP variant using EM for clustering performs slightly better than EM for HMM, as the former produces states with a higher degree of similarity, while requiring similar training time.
“The TCP variant using EM for clustering perform slightly better than EM for HMM, as the former produce states (clusters) with a higher degree of similarity, while requiring a similar training time.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [214]
Jayaraj et al. tackle the classification of congestion losses and contention losses in OBS networks.
“Jayaraj et al. [214] tackle the classification of congestion losses and contention losses in Optical Burst Switching (OBS) networks.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [214]
Cites
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743 · see citing works
Witten, I. H. (2008) · Cited by 9,115 · see citing works
Introduction to Machine Learning
(2019) · Series in machine perception and artificial intelligence · doi:10.1142/9789811201967_0001 · Cited by 1,638 · 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
Flow 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
Internet traffic classification using bayesian analysis techniques
Moore, A. W., & Zuev, D. (2005) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1071690.1064220 · Cited by 460 · see citing works
End-to-end differentiation of congestion and wireless losses
Cen, S., Cosman, P. C., & Voelker, G. M. (2003) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2003.818187 · Cited by 356 · see citing works
Optical Burst Switched Networks
Jue, J. P., & Vokkarane, V. M. (2005) · Kluwer Academic Publishers eBooks · doi:10.1007/b102280 · Cited by 138 · see citing works
Dynamic congestion-based load balanced routing in optical burst-switched networks
Thodime, G. P. V., Vokkarane, V. M., & Jue, J. P. (2004) · IEEE Global Telecommunications Conference (GLOBECOM) · doi:10.1109/glocom.2003.1258712 · Cited by 111 · see citing works
Hidden Markov modeling for network communication channels
Salamatian, K., & Vaton, S. (2001) · ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS) · doi:10.1145/378420.378439 · Cited by 110 · see citing works
TCP implementations and false time out detection in OBS networks
Yu, X., Qiao, C., & Liu, Y. (2005) · IEEE INFOCOM 2004 · doi:10.1109/infcom.2004.1356966 · Cited by 92 · see citing works
Comparison of contention resolution strategies in OBS network scenarios
Gauger, C. M., Kohn, M. S., & Scharf, J. (2004) · International Conference on Transparent Optical Networks · doi:10.1109/icton.2004.1360237 · Cited by 55 · see citing works
Maximizing throughput for optical burst switching networks
Li, J., Qiao, C., Xu, J., & Xu, D. (2004) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2004.1354595 · Cited by 50 · see citing works
Transport control protocol in optical burst switched networks: issues, solutions, and challenges
Shihada, B., & Ho, P. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2008.4564480 · Cited by 34 · see citing works
1 Introduction to machine learning
Veiga, M. H., & Ged, F. G. (2024) · De Gruyter eBooks · doi:10.1515/9783111288994-001 · Cited by 33 · see citing works
Dynamic load balancing in IP-over-WDM optical burst switching networks
Li, J., Gurusamy, M., & Chua, K. C. (2004) · Computer Networks · doi:10.1016/j.comnet.2004.07.019 · Cited by 30 · see citing works
Congestion Control and Contention Elimination in Optical Burst Switching
Maach, A., Bochman, G. V., & Mouftah, H. T. (2004) · Telecommunication Systems · doi:10.1023/b:tels.0000041005.87009.e5 · Cited by 19 · see citing works
A dynamic load-aware congestion control scheme in optical burst switching networks
Kim, L., Lee, S., & Song, J. (2006) · Photonic Network Communications · doi:10.1007/s11107-006-0046-x · Cited by 5 · 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
An Overview on Application of Machine Learning Techniques in Optical Networks
Musumeci, F., Rottondi, C., Nag, A., Irene Macaluso, B., Zibar, D., Ruffini, M., & Tornatore, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2880039 · Cited by 644 · see citing works
Artificial intelligence (AI) methods in optical networks: A comprehensive survey
Mata, J., de Miguel, I., Barroso, R. J. D., Merayo, N., Singh, S. K., Jukan, A., & Chamania, M. (2018) · Optical Switching and Networking · doi:10.1016/j.osn.2017.12.006 · Cited by 401 · see citing works
Bian, K., & Priyadarshi, R. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10110-w · Cited by 139 · see citing works
When machine learning meets congestion control: A survey and comparison
Jiang, H., Li, Q., Jiang, Y., Shen, G., Sinnott, R., Tian, C., & Xu, M. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108033 · Cited by 107 · see citing works
Improving TCP Congestion Control with Machine Intelligence
Kong, Y., Zang, H., & Ma, X. (2018) · ACM SIGCOMM Workshop on Network Meets AI & ML (NetAI) · doi:10.1145/3229543.3229550 · Cited by 83 · see citing works
Machine Learning for End-to-End Congestion Control
Zhang, T., & Mao, S. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900509 · Cited by 75 · see citing works
Artificial intelligence in computer networks
Jaber, T. A. (2022) · Periodicals of Engineering and Natural Sciences (PEN) · doi:10.21533/pen.v10.i1.539 · Cited by 49 · see citing works
Learning-Based and Data-Driven TCP Design for Memory-Constrained IoT
Li, W., Zhou, F., Meleis, W., & Chowdhury, K. (2016) · 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS) · doi:10.1109/dcoss.2016.8 · Cited by 29 · see citing works
Bridging machine learning and computer network research: a survey
Yang, C. H., Geng, J., Wang, Y., Li, J., Li, D., & Wu, J. (2018) · CCF Transactions on Networking · doi:10.1007/s42045-018-0009-7 · Cited by 23 · see citing works
Rajab, A. D., Huang, C., & Al-Shargabi, M. (2018) · Optical Switching and Networking · doi:10.1016/j.osn.2018.03.001 · Cited by 20 · see citing works
Machine Learning for Broad-Sensed Internet Congestion Control and Avoidance: A Comprehensive Survey
Huang, H., Zhu, X., Bi, J., Cao, W., & Zhang, X. (2021) · IEEE Access · doi:10.1109/access.2021.3060287 · Cited by 20 · see citing works
Dynamic Provisioning of Self-Organized Consumer Grid Services Over Integrated OBS/WSON Networks
Liu, L., Guo, H., Tsuritani, T., Yin, Y., Wu, J., Hong, X., Lin, J., & Suzuki, M. (2011) · Journal of Lightwave Technology · doi:10.1109/jlt.2011.2180508 · Cited by 17 · see citing works
An intelligent three-phase spam filtering method based on decision tree data mining
Sheu, J.-J., Chen, Y.-K., Chu, K.-T., Tang, J.-H., & Yang, W.-P. (2016) · Secur. Commun. Networks · doi:10.1002/sec.1584 · Cited by 15 · see citing works
A cognitive accountability mechanism for penalizing misbehaving ECN‐based TCP stacks
Latré, S., de Meerssche, W. V., Deschrijver, D., Papadimitriou, D. B., Dhaene, T., & Turck, F. D. (2012) · International Journal of Network Management · doi:10.1002/nem.1806 · Cited by 11 · see citing works
Network troubleshooting: Survey, Taxonomy and Challenges
Tong, V., Tran, H. A., Souihi, S., & Mellouk, A. (2018) · International Conference on Smart Communications in Network Technologies (SaCoNeT) · doi:10.1109/saconet.2018.8585610 · Cited by 11 · see citing works
On the QoS Estimation in an OpenFlow Network: The Packet Loss Case
Cello, M., Marchese, M., & Mongelli, M. (2016) · IEEE Communications Letters · doi:10.1109/lcomm.2016.2516537 · Cited by 10 · see citing works
MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks
Galliera, R., Morelli, A., Fronteddu, R., & Suri, N. (2023) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms56928.2023.10154210 · Cited by 11 · see citing works
LSTM for Cloud Data Centers Resource Allocation in Software-Defined Optical Networks
Aibin, M. (2020) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon51285.2020.9298133 · Cited by 9 · see citing works
A PSO-SVM for Burst Header Packet Flooding Attacks Detection in Optical Burst Switching Networks
Liu, S., Liao, X., & Shi, H. (2021) · Photonics · doi:10.3390/photonics8120555 · Cited by 8 · see citing works
Hybrid SDN Deployment Using Machine Learning
Wei, S. H., Tan, S. C., & Lee, C. K. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-33-4069-5_19 · Cited by 5 · see citing works
Predicting the performance of broadband passive optical networks using machine learning
Singh, K., Varma, P. R. K., Singh, R., & Kaur, R. (2023) · Journal of Optical Communications · doi:10.1515/joc-2022-0216 · Cited by 5 · see citing works
Hossain, M. K., Haque, M. M., & Dewan, M. A. A. (2021) · Computers · doi:10.3390/computers10080095 · Cited by 4 · see citing works
Poorzare, R., Poorzare, A., & Abedidarabad, S. (2019) · Review of Computer Engineering Studies · doi:10.18280/rces.050101 · Cited by 4 · see citing works
Optimizing optical networks by using CWN algorithm
Poorzare, R., & Abedidarabad, S. (2017) · Review of Computer Engineering Studies · doi:10.18280/rces.040303 · Cited by 4 · see citing works
Hossain, M. K., & Haque, M. M. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-0146-3_66 · Cited by 3 · see citing works
Artificial Intelligence, Internet of Things, and Communication Networks
Singh, G., & Kaur, G. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818576-6.00003-4 · Cited by 3 · see citing works
Applications of Reinforcement Learning to Routing and Virtualization in Computer Networks
Haeri, S. (2016) · Summit (Simon Fraser University) · Cited by 3 · see citing works
Design and analysis of a modified TCP for Optical Burst Switched networks
Datta, S., Dutta, A., & Choudhury, S. (2014) · doi:10.1109/icbim.2014.6970919 · Cited by 2 · see citing works
Panda, M., Gandhi, N., & Abraham, A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-49339-4_9 · Cited by 2 · see citing works
Identifying Network Congestion on SDN-Based Data Centers with Supervised Classification
de Oliveira, F. D. S., Pillon, M. A., Miers, C. C., & Koslovski, G. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-28451-9_20 · Cited by 2 · see citing works
A Unified View to Machine Learning and Control for Measurement-based Equivalent Bandwidth
Mongelli, M., Marco Muselli, B., & Marchese, M. (2020) · International Conference on the Design of Reliable Communication Networks (DRCN) · doi:10.1109/drcn48652.2020.1570609737 · Cited by 2 · see citing works
Function approximation-based reinforcement learning for large-scale problem domains
Li, W. (2019) · doi:10.17760/d20317961 · Cited by 2 · see citing works
Signal Processing for Optical Communication System Assisted by Computer Vision Techniques
Bo, T., Chan, C. C.-K., & Kim, H. (2018) · IEEE International Conference on Advanced Infocomm Technology · doi:10.1109/icait.2018.8686606 · Cited by 1 · see citing works
Detecting BHP Flood Attacks in OBS Networks: A Machine Learning Prospective
(2019) · International Journal of Science and Applied Information Technology · doi:10.30534/ijsait/2019/26862019 · Cited by 1 · see citing works
A Machine Learning-Based Approach for Improving TCP Congestion Detection Mechanism in IoTs
Arif, M., Qamar, U., & Riaz, A. (2022) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit57066.2022.00035 · Cited by 1 · see citing works
Technologies for Autonomic Dependable Services Platform: Achievements and Future Challenges
Ovaska, E., Dobrica, L., Purhonen, A., & Jaakola, M. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-36177-7_13
Cost-Throughput Analysis of a Modified TCP for Optical Burst Switched Networks
Datta, S., Dutta, A., & Choudhury, S. (2012) · International Journal of Computer Applications · doi:10.5120/9773-4312 · Cited by 4,370 · see citing works
Glisic, S., & Lorenzo, B. (2022) · Wiley eBooks · doi:10.1002/9781119790327.ch7
Venkatesh, T. G., & Murthy, C. S. R. (2009) · Springer eBooks · doi:10.1007/978-1-4419-1510-8_5
Self-citation
- Authors
- Jayaraj, A., Venkatesh, T., & Murthy, C. S. R.
- Affiliations
- Indian Institute of Technology Madras (IN)
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2008-08
- Volume
- 26
- Issue
- 6
- Pages
- 45-54
- ISSN
- 0733-8716 (ISSN-L)
- OpenAlex
- W2137166796
- Cited by
- 40
- References
- 20
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 3 · 2021: 6 · 2020: 5 · 2019: 3 · 2018: 8 · 2017: 1 · 2016: 4 · 2014: 1 · 2013: 1 · 2012: 2
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal on Selected Areas in Communications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- optical burst switching, loss classification, congestion loss, hidden Markov models, expectation-maximization clustering, TCP congestion control, bursty traffic, optical core networks
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
