Generic Intent-Driven Networking Paradigm with Different Levels of Policy Abstraction
Liu, Xianglin Li, Tong Yang, Chungang Ouyang, Ying Han, Zhu Guizani, Mohsen
Liu, Xianglin
Li, Tong
Yang, Chungang
Ouyang, Ying
Han, Zhu
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Intent-driven networking automatically refines high-level intents into network configuration policies, thereby simplifying network management complexity, for a large-scale network in particular. The intent refinement technique facilitates the full-lifecycle refinement from declarative intents to executable policies, and finally to device-centric configurations. Currently, policies at different abstraction levels during the intent refinement process lack a generic framework. Therefore, in this article, we propose a novel network paradigm to present the hierarchical framework of policies and the full-lifecycle of intent. We survey existing policy languages and classify them based on different abstraction levels. In addition, we distinguish the intent refinement techniques according to template-based, model-based, and search-based design approaches, and then analyze refinement between different levels of policies. Finally, we design an intent refinement system to validate the feasibility and effectiveness of our proposed framework.
Citation
X. Liu, T. Li, C. Yang, Y. Ouyang, Z. Han and M. Guizani, "Generic Intent-Driven Networking Paradigm with Different Levels of Policy Abstraction," in IEEE Communications Magazine, vol. 63, no. 4, pp. 162-168, April 2025, doi: 10.1109/MCOM.001.2400061
Source
IEEE Communications Magazine
Conference
Keywords
Natural languages, Translation, Resource description framework, Knowledge engineering, Bandwidth, Standardization, Semantics, Refining, Protocols, Performance evaluation
Subjects
Source
Publisher
IEEE