Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
DE
EN
Books
Journals
Events
Topic Page
Marketing
Log in
Learn more about individual access
Request company access
12th International Engine Congress 2025 | 25. + 26. February 2025

Meeting Place for the Powertrain and Sustainable Fuels Community

The congress will examine the future role of the internal combustion engine and its non-fossil fuels from a global perspective with leading experts. Be part of it! More information and booking options are available on our website.
EXTENDED SEARCH
Log in
Top
Published in:
Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks

Authors : Saloni Rai, Amit Kumar Garg

Published in: Proceedings of Third International Conference on Computing and Communication Networks

Publisher: Springer Nature Singapore

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The tremendous development of network traffic causes the need to develop new network applications. Machine learning provides a pertinent platform to enhance currently used network optimization methods. The information about future traffic volumes is vital for the network operators. This paper presents an issue in traffic prediction and pattern extraction in high-speed optical networks and fills the literature gap by using a multiprocessor module from Python to enhance the efficiency of the work. An ML approach based on regression is designed. In the investigation, a dataset has been simulated and generated using the SNDLIB library and Python module GNPy estimation tool, which provides dynamic traffic matrices stated for various real network topologies and mimics real-world data. The performance of the proposed system is evaluated using different evaluation indexes like Mean Square Error (MSE), Mean Absolute Error (MAE), Max error (ME), and processing time based on the tuning of hyper-parameters. Outcomes of findings indicate better results compared to the existing technique. The findings confirm that proposed approach’s efficiency is better and seems to be a promising solution to the current network problem.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Review of Various Approaches for Authorship Identification in Digital Forensics
next chapter Improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption
Literature
1.
Zhong, Z., Hua, N., Tornatore, M., Li, J., Li, Y., Zheng, X., Mukherjee, B.: Provisioning short-term traffic fluctuations in elastic optical networks. IEEE/ACM Trans. Netw. 27(4), 1460–1473 (2019)CrossRef
2.
Dias, M.P.I., Karunaratne, B.S., Wong, E.: Bayesian estimation and prediction-based dynamic bandwidth allocation algorithm for sleep/doze-mode passive optical networks. J. Lightwave Technol. 32(14), 2560–2568 (2014)CrossRef
3.
Chitra, K., Senkumar, M. R.: Hidden Markov model based lightpath establishment technique for improving QoS in optical WDM networks. In: Second International Conference on Current Trends In Engineering and Technology-ICCTET 2014, pp. 53–62. IEEE (2014)
4.
de Araújo, D.R., Bastos-Filho, C.J., Martins-Filho, J.F.: Methodology to obtain a fast and accurate estimator for blocking probability of optical networks. J. Opt. Commun. Netw. 7(5), 380–391 (2015)CrossRef
5.
Filho, R.H., Maia, J.E.B.: Network traffic prediction using PCA and K-means. In: Proceedings of the 2010 IEEE/IFIP Network Operations and Management Symposium, NOMS 2010, Osaka, Japan, 19–23 April 2010, pp. 938–941
6.
Rai, S., Garg, A.K.: Analysis of RWA in WDM optical networks using machine learning for traffic prediction and pattern extraction. J. Opt., 1–8 (2021)
7.
Mata, J., de Miguel, I., Durán, R.J., Merayo, N., Singh, S.K., Jukan, A., Chamania, M.: Artificial intelligence (AI) methods in optical networks: a comprehensive survey. Opt. Switch. Netw. 28, 43–57 (2018)CrossRef
8.
Lechowicz, P.: Regression-based fragmentation metric and fragmentation-aware algorithm in spectrally-spatially flexible optical networks. Comput. Commun. 175, 156–176 (2021)CrossRef
9.
Mata, J., De Miguel, I., Durán, R.J., Aguado, J.C., Merayo, N., Ruiz, L., Fernández, P., Lorenzo, R.M., Abril, E.J.: A SVM approach for lightpath QoT estimation in optical transport networks. In: Proceedings of the 2017 IEEE International Conference on Big Data, Big Data 2017, Boston, MA, USA, 11–14 December 2017; pp. 4795–4797 (2017)
10.
Vinchoff, C., Chung, N., Gordon, T., Lyford, L., Aibin, M.: Traffic prediction in optical networks using graph convolutional generative adversarial networks. In: Proceedings of the International Conference on Transparent Optical Networks, Bari, Italy, 19–23 July, pp. 3–6 (2020)
11.
Musumeci, F., Rottondi, C., Nag, A., Macaluso, I., Zibar, D., Ruffini, M., Tornatore, M.: An overview on application of machine learning techniques in optical networks. IEEE Commun. Surv. Tutor. 21, 1383–1408 (2019)CrossRef
12.
Cenedese, A., Tramarin, F., Vitturi, S.: An energy efficient Ethernet strategy based on traffic prediction and shaping. IEEE Trans. Commun. 65, 270–282 (2017)
13.
Rzym, G., Boryło, P., Chołda, P.: A time-efficient shrinkage algorithm for the Fourier-based prediction enabling proactive optimisation in software-defined networks. Int. J. Commun. Syst. 33, e4448 (2020)CrossRef
14.
Zhong, Z., Hua, N., Yuan, Z., Li, Y., Zheng, X.: Routing without routing algorithms: An AI-based routing paradigm for multi-domain optical networks. In: 2019 Optical Fiber Communications Conference and Exhibition (OFC).Optical Society of America: Washington, DC, USA (2019)
15.
Guan, L., Zhang, M., Wang, D.: Demonstration of AI-assisted energy-efficient traffic aggregation in 5G optical access network. In: 2020 Optical Fiber Communications Conference and Exhibition (OFC) , pp. 1–3. Optical Society of America, Washington, DC, USA (2020)
16.
D’Amico, A., Straullu, S., Borraccini, G., London, E., Bottacchi, S., Piciaccia, S., Curri, V.: Enhancing lightpath QoT computation with machine learning in partially disaggregated optical networks. IEEE Open J. Commun. Soc. 2, 564–574 (2021)CrossRef
17.
Andreoletti, D., Rottondi, C., Bianco, A., Giordano, S.: A machine learning framework for scalable routing and wavelength assignment in large optical networks. In: Optical Fiber Communication Conference, pp. F2G-3. Optica Publishing Group (2021)
18.
Aibin, M.: Traffic prediction based on machine learning for elastic optical networks. Opt. Switch. Netw. 30, 33–39 (2018)CrossRef
19.
Cheng, S., Xiao, D., Huang, A., Aibin, M.: Machine learning for regenerator placement based on the features of the optical network. In: 2019 21st International Conference on Transparent Optical Networks (ICTON), pp. 1–3. IEEE (2019)
20.
Locatelli, F., Christodoulopoulos, K., Fàbrega, J. M., Moreolo, M.S., Nadal, L., Spadaro, S.: Experimental demonstration of a machine learning-based in-band OSNR estimator from optical spectra. In: 2020 International Conference on Optical Network Design and Modeling (ONDM), pp. 1–4. IEEE (2020)
21.
Song, C., Zhang, M., Huang, X., Zhan, Y., Wang, D., Liu, M., Rong, Y.: Machine learning enabling traffic-aware dynamic slicing for 5G optical transport networks. In: CLEO: Science and Innovations, pp. JTu2A-44. Optica Publishing Group (2018)
22.
Samadi, P., Amar, D., Lepers, C., Lourdiane, M., Bergman, K.: Quality of transmission prediction with machine learning for dynamic operation of optical WDM networks. In: 2017 European Conference on Optical Communication (ECOC), pp. 1–3. IEEE (2017)
23.
Aibin, M., Chung, N., Gordon, T., Lyford, L., Vinchoff, C.: On short-and long-term traffic prediction in optical networks using machine learning. In: 2021 International Conference on Optical Network Design and Modeling (ONDM), pp. 1–6. IEEE (2021)
24.
Szostak, D., Włodarczyk, A., Walkowiak, K.: Machine learning classification and regression approaches for optical network traffic prediction. Electronics 10(13), 1578 (2021)CrossRef
25.
Szostak, D., Walkowiak, K.: Application of machine learning algorithms for traffic forecasting in dynamic optical networks with service function chains. Found. Comput. Decis. Sci. 45(3), 217–232 (2020)CrossRef
26.
Alarcon-Aquino, V., Barria, J.A.: Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction. IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev.) 36(2), 208–220 (2006)
27.
Krishnamurthy, B., Sen, S., Zhang, Y., Chen, Y.: Sketch-based change detection: methods, evaluation, and applications. In: Proceedings of the 3rd ACM SIGCOMM Conference on Internet Measurement, pp. 234–247 (2003)
28.
Knapińska, A., Lechowicz, P., Walkowiak, K.: Machine-learning based prediction of multiple types of network traffic. In: International Conference on Computational Science, pp. 122–136. Springer International Publishing, Cham (2021)
29.
Szostak, D., Walkowiak, K., Włodarczyk, A.: Short-term traffic forecasting in optical network using linear discriminant analysis machine learning classifier. In: 2020 22nd International Conference on Transparent Optical Networks (ICTON), pp. 1–4. IEEE (2020)
30.
Orlowski, S., Pióro, M., Tomaszewski, A., Wessäly, R.: SNDlib 1.0—Survivable network design library. In: Proceedings of the 3rd International Network Optimization Conference (INOC 2007), Spa, Belgium, 12–14 June 2007
31.
Jurkiewicz, P., Rzym, G., Borylo, P.: Flow length and size distributions in campus Internet traffic. Comput. Commun. 167, 15–30 (2021)CrossRef
32.
Ba, S., Chatterjee, B.C., Oki, E.: Defragmentation scheme based on exchanging primary and backup paths in 1+1 path protected elastic optical networks. IEEE/ACM Trans. Netw. 25, 1717–1731 (2017)CrossRef
33.
Goścień, R.: On the efficient dynamic routing in spectrally-spatially flexible optical networks. In Proceedings of the Resilient Networks Design and Modeling (RNDM), Nicosia, Cyprus, 14–16, (2019).
34.
Walkowiak, K., Klinkowski, M., Lechowicz, P.: Dynamic routing in spectrally spatially flexible optical networks with back-to-back regeneration. IEEE/OSA J. Opt. Commun. Netw 10, 523–534 (2018)CrossRef
35.
Gaizi, K., Abdi, F., Abbou, F.M.: Realistic dynamic traffic generation for WDM Optical Networks. In: Proceedings of the 2016 27th Irish Signals and Systems Conference (ISSC), Londonderry, UK, 21–22, pp. 1–4 (2016)
36.
Gencata, A., Mukherjee, B.: Virtual-topology adaptation for WDM mesh networks under dynamic traffic. IEEE/ACM Trans. Netw. 11, 236–247 (2003)CrossRef
37.
.Troia, S., Cibari, A., Alvizu, R., Maier, G.: Dynamic programming of network slices in software-defined metro-core optical networks. Opt. Switch. Netw, 36, 100551 (2020)
38.
Goścień, R., Knapińska, A., Włodarczyk, A.: Modeling and prediction of daily traffic patterns—wask and six case study. Electronics 10(14), 1637 (2021)CrossRef
Metadata
Title
Machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks
Authors
Saloni Rai
Amit Kumar Garg
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Proceedings of Third International Conference on Computing and Communication Networks

Print ISBN: 978-981-9708-91-8

Electronic ISBN: 978-981-9708-92-5

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-0892-5_20