Citation

Tan, Kai Yuan and Tan, Saw Chin (2025) DRL-Driven Batch-Oriented SDN Migration with Weighted Traffic Matrix Clustering for Dynamic Networks. Lecture Notes in Computer Science, 16203. pp. 197-211. ISSN 0302-9743

Text DRL-Driven Batch-Oriented SDN Migration with Weighted Traffic Matrix Clustering for Dynamic Networks _ Hybrid Artificial Intelligent Systems.pdf - Published Version Restricted to Repository staff only Download (292kB)

Abstract

Current incremental Software-Defined Networking (SDN) migration strategies face two critical limitations: the inability to effectively represent dynamic network conditions and prolonged transition periods due to single-node sequential migration approaches. Existing approaches in dynamic network environments treat all traffic matrices clusters equally, ignoring their varying frequencies of occurrence and the presence of outliers, while single-node sequential migration strategies delay the realization of SDN benefits. This paper investigates the effectiveness of combining weighted traffic matrices with batch-oriented migration in a Deep Reinforcement Learning (DRL)-driven SDN migration framework. We propose a new framework integrating a multi-stage hybrid clustering method for weighted traffic matrices with a DRL model that enables batch migration decisions. Our framework demonstrates superior clustering performance through improved Dunn Index (0.0933 vs 0.0045 for GEANT, 0.1083 vs 0.0815 for Abilene) and Davies-Bouldin Index (1.2295 vs 1.3589 for GEANT, 1.6856 vs 1.7264 for Abilene) compared to standalone K-means, while achieving better weighted MLU performance during crucial early migration phases. Our research contributes to the development of practical SDN migration strategies that effectively represent dynamic network environments while enabling faster and more efficient transition through batch migration.

Item Type:	Article
Uncontrolled Keywords:	Software-Defined Networking
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Computing and Informatics (FCI) Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	10 Feb 2026 02:36
Last Modified:	10 Feb 2026 02:36
URII:	http://shdl.mmu.edu.my/id/eprint/15271

Downloads

Edit (login required)