Enabling Scalability and Flexibility into Network Routing Protocol using Behavior Tree
Huang, Jiaorui Yang, Chungang Feng, Tao Dong, Lujia Anpalagan, Alagan Ni, Qiang Guizani, Mohsen
Huang, Jiaorui
Yang, Chungang
Feng, Tao
Dong, Lujia
Anpalagan, Alagan
Ni, Qiang
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Current network routing protocol design is faced with novel challenges due to evolving network scale, various network service demands, and dynamic network states. However, the conventional finite state machine models lack both scalability and flexibility for the description of network routing protocol states. In this article, we enable scalability and flexibility into network routing protocol by exploring and exploiting behavior trees, where behavior trees can reformulate the network routing protocol by characterizing state transformation as action nodes. We first present a generic routing protocol architecture with a comparative analysis of the behavior tree, finite state machine, etc. Then, we propose an implementable functional scheme, which provides a foundation for extending the functionality and enabling flexible configurations towards the network routing protocol. Finally, we design two use cases to verify that behavior trees can effectively replace finite state machines and the excellent scalability of behavior trees in terms of routing protocols. © 2025 IEEE.
Citation
J. Huang et al., "Enabling Scalability and Flexibility into Network Routing Protocol using Behavior Tree," in IEEE Network, doi: 10.1109/MNET.2025.3577627
Source
IEEE Network
Conference
Keywords
Behavior Tree, Control Plane Decoupling, Network Routing Protocol
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Source
Publisher
IEEE