Application of Machine Learning Algorithms for Traffic Forecasting in Dynamic Optical Networks with Service Function Chains

[1] Bhamare D., Jain R., Samaka M., Erbad A., A survey on service function chaining, Journal of Network and Computer Applications, vol. 75, pp. 138-155, November 2016.10.1016/j.jnca.2016.09.001Search in Google Scholar

[2] Bifet A., Morales G., et al., Big Data Stream Learning with SAMOA, IEEE, Shenzhen, 2014.10.1109/ICDMW.2014.24Search in Google Scholar

[3] Boutaba R., Salahuddin M. A., Limam N., et al. A comprehensive survey on machine learning for networking: evolution, applications and research opportunities, Journal of Internet Services and Applications 9, 2018.10.1186/s13174-018-0087-2Search in Google Scholar

[4] Chan V. W. S., Jang E., Cognitive all-optical fiber network architecture, 19th International Conference on Transparent Optical Networks, Girona, 2017.10.1109/ICTON.2017.8025063Search in Google Scholar

[5] CISCO: Cisco Annual Internet Report (2018–2023), CISCO, 2020.Search in Google Scholar

[6] European Telecommunications Standards Institute (ETSI): Network Function Virtualization, https://www.etsi.org/technologies/nfv, 2013.Search in Google Scholar

[7] Ghaznavi M., Shahriar N., Ahmed R., Boutaba R., Service function chaining simplified, CoRR, vol. abs/1601.00751, 2016.Search in Google Scholar

[8] Halpern J., Pignataro C., Service Function Chaining (SFC) Architecture, Internet Engineering Task Force (IETF), 2015.10.17487/RFC7665Search in Google Scholar

[9] Hmaity A., et al., Virtual network function placement for resilient service chain provisioning, 2016 8th International Workshop on Resilient Networks Design and Modeling, IEEE, 2016.10.1109/RNDM.2016.7608294Search in Google Scholar

[10] Huin N., Tomassilli A., Giroire F., Jaumard B., Energy-Efficient Service Function Chain Provisioning, Journal of Optical Communications and Networking, vol.10, pp. 114-124, 2018.10.1364/JOCN.10.000114Search in Google Scholar

[11] Jeong S., et al., Machine Learning based Link State Aware Service Function Chaining, 2019 20th Asia-Pacific Network Operations and Management Symposium, IEEE, 2019.10.23919/APNOMS.2019.8893037Search in Google Scholar

[12] Mata J., Miguealet I., al. Artificial intelligence (AI) methods in optical networks: A comprehensive survey, Optical Switching and Networking, vol 28, pp. 43-57, 2018.10.1016/j.osn.2017.12.006Search in Google Scholar

[13] Medhat A., et al., Service function chaining in next generation networks: State of the art and research challenges, IEEE Communications Magazine, vol 55.2, pp. 216-223, 2016.10.1109/MCOM.2016.1600219RPSearch in Google Scholar

[14] Mouftah H. T., Ho P., Optical Networks Architecture and Survivability, Springer Science & Business Media, 2003.,Search in Google Scholar

[15] Musumeci F., et all, An Overview on Application of Machine Learning Techniques in Optical Networks, IEEE Communications Surveys & Tutorials, vol. 21, pp. 1383 – 1408, 2019.Search in Google Scholar

[16] Nikam V., Gross J., Rostami A., VNF Service Chaining in Optical Data Center Networks, 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks, Berlin, 2017.10.1109/NFV-SDN.2017.8169845Search in Google Scholar

[17] Orlowski S., Wessäly R., Pióro M., Tomszewski A., SNDlib 1.0 - Survivable Network Design Library, Networks, vol. 55, pp. 276-286, 2010.10.1002/net.20371Search in Google Scholar

[18] Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., et al., Scikit-learn: Machine learning in python, Journal of machine learning research, vol 12, no. Oct, pp. 2825-2830, 2011.Search in Google Scholar

[19] Pei J., Peilin H., Defang L.: Virtual network function selection and chaining based on deep learning in SDN and NFV-enabled networks, 2018 IEEE International Conference on Communications Workshops, Kansas City, 2018.10.1109/ICCW.2018.8403657Search in Google Scholar

[20] Rafique D., Velasco L., Machine Learning for Network Automation: Overview, Architecture, and Applications [Invited Tutorial], Journal of Optical Communications and Networking. 10, 2018.10.1364/JOCN.10.00D126Search in Google Scholar

[21] Simmons J., Optical Network Design and Planning, 2nd edition, Springer, 2014.10.1007/978-3-319-05227-4Search in Google Scholar

[22] Szostak D., Walkowiak K., Influence of traffic type on traffic prediction quality in dynamic optical networks with service chains, Polskie Porozumienie na Rzecz Rozwoju Sztucznej Inteligencji, Wrocław, 2019.10.1109/ICTON.2019.8840301Search in Google Scholar

[23] Szostak D., Walkowiak K., Machine Learning Methods for Traffic Prediction in Dynamic Optical Networks with Service Chains, 21th International Conference on Transparent Optical Networks, Angers, 2019.10.1109/ICTON.2019.8840301Search in Google Scholar

[24] Wolfgang J., et al.: Research directions in network service chaining., 2013 IEEE SDN for future networks and services, IEEE, Trento, 2013.Search in Google Scholar

[25] Wolpert D. H., The Supervised learning no-free-lunch theorems, 6^th Online World Conference on Soft Computing in Industrial Applications, 2001.10.1007/978-1-4471-0123-9_3Search in Google Scholar

[26] Xie Y., Liu Z., Wang S., Wang Y., Service Function Chaining Resource Allocation: A Survey, https://arxiv.org/abs/1608.00095, 2016.Search in Google Scholar

[27] Zervas G. S., Simeonidou D., Cognitive optical networks: Need, requirements and architecture, 12th International Conference on Transparent Optical Networks, Munich, 2010.10.1109/ICTON.2010.5549176Search in Google Scholar