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Asmita Chavan,
Bhakti Gavali,
Aditi Gavali,
Jyoti Ghadage,
Vaibhav Godase,
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- Assistant Professor, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
Abstract
Software-defined networking (SDN) separates control logic from packet-forwarding devices and provides a programmable substrate for traffic engineering. Machine learning (ML), deep learning, and reinforcement learning have consequently been explored for route selection, congestion mitigation, load balancing, and adaptive network control. Yet reported improvements are difficult to compare when studies use different topologies, traffic traces, baselines, simulation settings, and measurement practices. This review manuscript develops a structured framework for examining those issues, with emphasis on generalization, computational overhead, and reproducibility rather than algorithm names alone. It synthesizes foundational SDN and ML-for-networking literature, identifies common design choices in learning-based routing, and proposes a study-extraction matrix and evidence-assessment framework. The manuscript also specifies a reproducible search and screening protocol aligned with PRISMA 2020 reporting guidance. Importantly, the current document is a research-ready review draft: a complete multi-database search, independent screening, and final PRISMA record counts have not been performed in this drafting step. Therefore, it does not claim exhaustive systematic coverage or fabricate included-study totals. Before journal submission, the authors should execute the protocol, update the evidence tables from screened primary studies, and report actual search dates and selection counts. The central argument is that routing performance should be evaluated as a multi-dimensional engineering outcome: network quality of service, transfer to unseen conditions, computational and control-plane cost, and reproducibility must be considered together.
Keywords: Software-Defined Networking; Intelligent Routing; Traffic Engineering; Machine Learning; Reinforcement Learning.
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Journal of Telecommunication, Switching Systems and Networks
| Volume | 13 | |
| 03 | ||
| Received | 29/10/2026 | |
| Accepted | 30/10/2026 | |
| Published | 31/10/2026 | |
| Publication Time | 2 Days |