I2BN: Intelligent Intent Based Networks





Intent, I2BN, network and service automation, network intelligence, ZSM


Intent based network management reduces the complexity of network programming from a growing set of deeply technical APIs to context-free high-level objectives that the network should autonomously achieve and keep. The practical implementation of an intent based network requires substantial automation technology embedded in the network. Automation should cover the entire lifecycle of intents, from their ingestion to fulfillment and assurance. This article investigates the feasibility of automatically assembling interworking implementation units into intent specific automation pipelines, where units are reusable self-learning closed loop micro-services with self-declared capabilities. Each closed loop may gain knowledge and respond to dynamically changing network conditions, thereby enabling network autonomy in reaching the declared intent objectives. The human-network intent interface for expressing intents is proposed to be based on the aggregation of the deployed network and service automation capabilities, rather than a formalism decoupled from the actual network implementation. This principle removes the ambiguity and compatibility gap between human intent definition and machine intent fulfillment, while retaining the flexibility and extendibility of the intents offered by any specific system via onboarding additional micro-services with novel capabilities. The concepts discussed by the article fit into the architecture and closed loop work items already defined by ETSI ZSM and provides considerations towards new areas such as intent driven autonomous networks and enablers for automation.


Download data is not yet available.

Author Biography

Péter Szilágyi, Nokia Bell Labs, Budapest, Hungary

Péter Szilágyi received an M.Sc. degree in Software Engineering from the Budapest University of Technology and Economics (BME), Hungary, in 2009. He is a senior research engineer and DMTS at Nokia Bell Labs. His research history includes telecommunications and software, artificial intelligence, data analytics, 5G and verticals, connected vehicles, end-to-end system engineering and rapid prototyping. He has more than 40 patents and 25 publications in book chapters, journals and conferences.


J. C. Strassner, Policy-Based Network Management, Elsevier, 2003.

J. C. Strassner, “Intent-based Policy Management,” in SDNrg, IETF 95, Buenos Aires, 2016.

A. Clemm, L. Ciavaglia, L. Granville and J. Tantsura, Intent-Based Networking – Concepts and Definitions, IETF Network Working Group, 2020.

C. Li, O. Havel, W. Liu, A. Olariu, P. Martinez-Julia, J. Nobre and D. Lopez, “ Intent Classification,” IRTF, 2020.

3GPP TR 28.812, “Telecommunication management; Study on scenarios for Intent driven management services for mobile networks,” 2020.

3GPP TS 28.312, “Management and orchestration; Intent driven management services for mobile networks,” 2020.

ONF TR-523, “Intent NBI – Definition and Principles,” 2016.

R. Addad, D. Dutra, M. Bagaa, T. Taleb, H. Flinck and M. Namane, “Benchmarking the ONOS Intent Interfaces to Ease 5G Service Management,” in IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, 2018.

ETSI ZSM 005 V0.4.1, “Zero-touch network and Service Management (ZSM); Means of Automation,” 2020-03.

ETSI ZSM 002 V1.1.1, “Zero-touch network and Service Management (ZSM); Reference Architecture,” 2019-08.

P. Szilágyi and S. Nováczki, “An Automatic Detection and Diagnosis Framework for Mobile Communication Systems,” IEEE Transactions on Network and Service Management, vol. 9, no. 2, pp. 184–197, 2012.

G. Ciocarlie, U. Lindqvist, K. Nitz, S. Nováczki and H. Sanneck, “DCAD: Dynamic Cell Anomaly Detection for operational cellular networks,” in IEEE Network Operations and Management Symposium (NOMS), Krakow, 2014.

P. Ramachandra, K. Zetterberg, F. Gunnarsson, R. Moosavi, S. B. Redhwan and S. Engström, “Automatic neighbor relations (ANR) in 3GPP NR,” in IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Barcelona, 2018.

ETSI ZSM 009-1 V0.10.4, “Zero-touch network and Service Management (ZSM); Closed Loop Automation – Enablers,” 2020-12.

ETSI ZSM 009-3 V0.1.0, “Zero-touch network and Service Management (ZSM); Closed Loop Automation – Advanced Topics,” 2020-10.

A. Agocs and J. L. Goff, “A web service based on RESTful API and JSON Schema/JSON Meta Schema to construct knowledge graphs,” in International Conference on Computer, Information and Telecommunication Systems (CITS), Colmar, 2018.

Bluetooth SIG, “Bluetooth Core Specification v5.2,” 2019.

3GPP TS 28.552, “Management and orchestration; 5G performance measurements,” 2020.

M. Sun, G. Convertino and M. Detweiler, “Designing a Unified Cloud Log Analytics Platform,” in International Conference on Collaboration Technologies and Systems (CTS), Orlando, 2016.

3GPP TR 23.700-91, “Study on enablers for network automation for the 5G System (5GS); Phase 2,” 2020.

ITU-T, “FG ML5G – Unified architecture for machine learning in 5G and future networks,” 2019.

H. Liu, Z. Zhou, F. Shang, X. Qi, Y. Liu and L. Jiao, Boosting Gradient for White-Box Adversarial Attacks, arXiv, 2020.

C. Xu, D. Li and M. Yang, Improve Adversarial Robustness via Weight Penalization on Classification Layer, arXiv, 2020.

Y. Shi, Y. E. Sagduyu, T. Erpek and M. C. Gursoy, How to Attack and Defend 5G Radio Access Network Slicing with Reinforcement Learning, arXiv, 2021.






Special Issue on Zero-touch Network and Service Automation (ZSM)