Dynamic Multi-Objective Controller Placement in SD-WAN: A GMM-MARL Hybrid Framework

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I whakaputaina i:	Network vol. 5, no. 4 (2025), p. 52-83
Kaituhi matua:	Abdulghani, Abdulrahman M
Ētahi atu kaituhi:	Azizol, Abdullah, Rahiman, A R, Abdul Hamid Nor Asilah Wati, Akram Bilal Omar
I whakaputaina:	MDPI AG
Ngā marau:	Mathematical programming Machine learning Software Integer programming Wide area networks Network topologies Artificial intelligence Optimization techniques Real time Adaptation Linear programming Algorithms Clustering Fault tolerance Efficiency Business metrics
Urunga tuihono:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Whakaahuatanga
Whakarāpopotonga:	Modern Software-Defined Wide Area Networks (SD-WANs) require adaptive controller placement addressing multi-objective optimization where latency minimization, load balancing, and fault tolerance must be simultaneously optimized. Traditional static approaches fail under dynamic network conditions with evolving traffic patterns and topology changes. This paper presents a novel hybrid framework integrating Gaussian Mixture Model (GMM) clustering with Multi-Agent Reinforcement Learning (MARL) for dynamic controller placement. The approach leverages probabilistic clustering for intelligent MARL initialization, reducing exploration requirements. Centralized Training with Decentralized Execution (CTDE) enables distributed optimization through cooperative agents. Experimental evaluation using real-world topologies demonstrates a noticeable reduction in the latency, improvement in network balance, and significant computational efficiency versus existing methods. Dynamic adaptation experiments confirm superior scalability during network changes. The hybrid architecture achieves linear scalability through problem decomposition while maintaining real-time responsiveness, establishing practical viability.
ISSN:	2673-8732
DOI:	10.3390/network5040052
Puna:	Publicly Available Content Database