Dynamic Multi-Objective Controller Placement in SD-WAN: A GMM-MARL Hybrid Framework
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| Publicado en: | Network vol. 5, no. 4 (2025), p. 52-83 |
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| Autor principal: | |
| Otros Autores: | , , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 003 | UK-CbPIL | ||
| 022 | |a 2673-8732 | ||
| 024 | 7 | |a 10.3390/network5040052 |2 doi | |
| 035 | |a 3286331729 | ||
| 045 | 2 | |b d20251001 |b d20251231 | |
| 100 | 1 | |a Abdulghani, Abdulrahman M |u Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia | |
| 245 | 1 | |a Dynamic Multi-Objective Controller Placement in SD-WAN: A GMM-MARL Hybrid Framework | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Mathematical programming | ||
| 653 | |a Machine learning | ||
| 653 | |a Software | ||
| 653 | |a Integer programming | ||
| 653 | |a Wide area networks | ||
| 653 | |a Network topologies | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Real time | ||
| 653 | |a Adaptation | ||
| 653 | |a Linear programming | ||
| 653 | |a Algorithms | ||
| 653 | |a Clustering | ||
| 653 | |a Fault tolerance | ||
| 653 | |a Efficiency | ||
| 653 | |a Business metrics | ||
| 700 | 1 | |a Azizol, Abdullah |u Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia | |
| 700 | 1 | |a Rahiman, A R |u Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia | |
| 700 | 1 | |a Abdul Hamid Nor Asilah Wati |u Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia | |
| 700 | 1 | |a Akram Bilal Omar |u Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia | |
| 773 | 0 | |t Network |g vol. 5, no. 4 (2025), p. 52-83 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286331729/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286331729/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286331729/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |