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

• Autorzy: Szostak Daniel , Walkowiak Krzysztof

Identyfikatory

DOI

10.2478/fcds-2020-0012

• Języki publikacji: EN

Abstrakty

EN

Knowledge about future optical network traffic can be beneficial for network operators in terms of decreasing an operational cost due to efficient resource management. Machine Learning (ML) algorithms can be employed for forecasting traffic with high accuracy. In this paper we describe a methodology for predicting traffic in a dynamic optical network with service function chains (SFC). We assume that SFC is based on the Network Function Virtualization (NFV) paradigm. Moreover, other type of traffic, i.e. regular traffic, can also occur in the network. As a proof of effectiveness of our methodology we present and discuss numerical results of experiments run on three benchmark networks. We examine six ML classifiers. Our research shows that it is possible to predict a future traffic in an optical network, where SFC can be distinguished. However, there is no one universal classifier that can be used for each network. Choice of an ML algorithm should be done based on a network traffic characteristics analysis.

Słowa kluczowe

EN

Dynamic Optical Networks; Traffic Prediction; Service Function Chaining; machine learning

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2020
• Tom: Vol. 45, No. 3
• Strony: 217--232
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Szostak Daniel

• Wrocław University of Science and Technology, Department of Systems and Computer Networks, Poland

autor

Walkowiak Krzysztof

• Wrocław University of Science and Technology, Department of Systems and Computer Networks, Poland

Bibliografia

• [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.
• [2] Bifet A., Morales G., et al., Big Data Stream Learning with SAMOA, IEEE, Shenzhen, 2014.
• [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.
• [4] Chan V.W.S., Jang E., Cognitive all-optical fiber network architecture, 19th International Conference on Transparent Optical Networks, Girona, 2017.
• [5] CISCO: Cisco Annual Internet Report (2018-2023), CISCO, 2020.
• [6] European Telecommunications Standards Institute (ETSI): Network Function Virtualization, https://www.etsi.org/technologies/nfv, 2013.
• [7] Ghaznavi M., Shahriar N., Ahmed R., Boutaba R., Service function chaining simplified, CoRR, vol. abs/1601.00751, 2016.
• [8] Halpern J., Pignataro C., Service Function Chaining (SFC) Architecture, Internet Engineering Task Force (IETF), 2015.
• [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] 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.
• [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.
• [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.
• [13] Medhat A., et al., Service function chaining in next generation networks: State of the art and research challenges, IEEE CommunicationsMagazine, vol 55.2, pp. 216-223, 2016.
• [14] Mouftah H.T., Ho P., Optical Networks Architecture and Survivability, Springer Science & Business Media, 2003.,
• [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.
• [16] Nikam V., Gross J., Rostami A., VNF Service Chaining in Optical Data Center Networks, 2017 IEEE Conference on Network Function Virtualization and Software DefinedNetworks, Berlin, 2017.
• [17] Orlowski S., Wessaly R., Pióro M., Tomszewski A., SNDlib 1.0 - Survivable Network Design Library, Networks, vol. 55, pp. 276-286, 2010.
• [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.
• [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.
• [20] Rafique D., Velasco L., Machine Learning for Network Automation: Overview, Architecture, and Applications [Invited Tutorial], Journal of Optical Communications andNetworking. 10, 2018.
• [21] Simmons J., Optical Network Design and Planning, 2nd edition, Springer, 2014.
• [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.
• [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.
• [24] Wolfgang J., et al.: Research directions in network service chaining., 2013 IEEE SDN for future networks and services, IEEE, Trento, 2013.
• [25] Wolpert D.H., The Supervised learning no-free-lunch theorems, 6th Online World Conference on Soft Computing in Industrial Applications, 2001.
• [26] Xie Y., Liu Z., Wang S., Wang Y., Service Function Chaining Resource Allocation: A Survey, https://arxiv.org/abs/1608.00095, 2016.
• [27] Zervas G.S., Simeonidou D., Cognitive optical networks: Need, requirements and architecture, 12th International Conference on Transparent Optical Networks, Munich, 2010.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).