Abstract
The adoption of Software Define Networking (SDN), Network Function Virtualization (NFV) and Machine Learning (ML) will play a key role in the control and management of 5G network slices to fulfill the specific requirements of application/services and the new requirements of fifth generation (5G) networks. In this research, we propose a distributed architecture to perform network analytics applying ML techniques in the context of network operation and control of 5G networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
3GPP: Study of enablers for network automation for 5G. Technical report (TR), 3rd Generation Partnership Project (3GPP), December 2018
3GPP: System architecture for the 5G system. Technical Specification (TS), 3rd Generation Partnership Project (3GPP), December 2018
Bosneag, A., Wang, M.X.: Intelligent network management mechanisms as a step towards 5G. In: 2017 8th International Conference on the Network of the Future (NOF), pp. 52–57, November 2017
Boutaba, R., et al.: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J. Internet Serv. Appl. 9(1), 16 (2018)
Buda, T.S., et al.: Can machine learning aid in delivering new use cases and scenarios in 5G?. In: 2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016, pp. 1279–1284, April 2016
Condoluci, M., Mahmoodi, T.: Softwarization and virtualization in 5G mobile networks: benefits, trends and challenges. Comput. Netw. 146, 65–84 (2018)
ETSI: Experiential networked intelligence (ENI); ENI use cases. Group report, Experiential Networked Intelligence ETSI ISG, April 2018
Ordonez-Lucena, J., et al.: Network slicing for 5G with SDN/NFV: concepts, architectures, and challenges. IEEE Commun. Mag. 55(5), 80–87 (2017)
Xie, J., et al.: A survey of machine learning techniques applied to software defined networking (SDN): research issues and challenges. IEEE Commun. Surv. Tutor. 21(1), 393–430 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hernández-Chulde, C., Cervelló-Pastor, C. (2019). Intelligent Optimization and Machine Learning for 5G Network Control and Management. In: De La Prieta, F., et al. Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection. PAAMS 2019. Communications in Computer and Information Science, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-24299-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-24299-2_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24298-5
Online ISBN: 978-3-030-24299-2
eBook Packages: Computer ScienceComputer Science (R0)