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Recent Advances in Software-Defined Networking (SDN)
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Recent Advances in Software-Defined Networking (SDN)

A Special Issue of Network (ISSN 2673-8732).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1412

Editors

Dr. Marek Bolanowski

E-Mail Website
Guest Editor
Department of Complex Systems, Rzeszow University of Technology, Rzeszow, Poland
Interests: distributed systems; self-aware and autonomous systems; anomaly detection; industry 4.0; internet of everything; cybersecurity; complex communication systems; SDN networks; PoC systems
Special Issues, Collections and Topics in MDPI journals
Dr. Paweł Kuraś

E-Mail Website
Guest Editor
Department of Telecommunications, AGH University of Science and Technology, Kraków, Poland
Interests: machine learning; information extraction; data science; text classification; web mining; text analysis; data preparation; game development

Special Issue Information

Dear Colleagues,

Programmable networks are one of the main research areas related to modern communication systems. The mechanisms included in this architecture align with cognitive networks, which automate reconfiguration scenarios triggered in response to changing operating conditions in distributed systems. Such an approach enables rapid responses to failures, the elimination of communication bottlenecks, and the integration of the system’s hardware components with the software layer.

For this reason, SDN-class systems are used not only in traditional core communication networks but also in 5G, 6G, and hybrid, partially virtualized communication systems.

In this Special Issue, particular emphasis will be placed on the applicability of proposed solutions in real communication environments and the assessment of the usefulness of new deployment paradigms for this class of solution. We are particularly interested in practical implementations, case studies, and experimental evaluations that demonstrate the maturity and readiness of SDN technologies for industrial and commercial use. Additionally, we encourage submissions addressing the role of SDN in enabling AI-driven network automation and orchestration. The list of topics includes, but is not limited to, the following:

  • Scalability and performance of SDN infrastructure and controllers;
  • Security and attacks related to the operation of SDN networks;
  • Interoperability and integration of SDN with existing infrastructure;
  • Monitoring, diagnostics, and operational complexity;
  • Complex network architecture;
  • Mathematical and numerical analysis of networks;
  • Modeling of SDN computer networks;
  • Cognitive networks;
  • Dynamics on networks;
  • Complex SDN network management;
  • Performance modeling and analysis in SDN networks;
  • Algorithms for controlling and monitoring SDN computer networks;
  • Evolving networks;
  • Detection of anomalies in the operation of enterprise-class SDN computer network components;
  • Predictive maintenance with the use of SDN networks;
  • Role of SDN in edge and fog computing architectures;
  • Integration of SDN with NFV (network function virtualization);
  • AI and machine learning applications in SDN-based network control;
  • Energy-efficient SDN solutions for green networking;
  • Testbeds, benchmarks, and datasets for SDN research.

Dr. Marek Bolanowski
Dr. Paweł Kuraś
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Network is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • software-defined networking (SDN)
  • SDN controller scalability
  • security
  • network virtualization
  • SDN interoperability
  • SDN performance optimization
  • SDN and NFV integration
  • experimental SDN deployment

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Published Papers (2 papers)

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35 pages, 2657 KB  
Article
Design and Evaluation of PSA-FRR and PSAR-FRR for Fast Reroute in Homogeneous and Hybrid SDN Networks
by Md Imtiaz Ahmed and Yaser Al Mtawa
Network 2026, 6(3), 65; https://doi.org/10.3390/network6030065 - 10 Aug 2026
Viewed by 315
Abstract
Fast Reroute (FRR) after link failures is essential for carrier-grade Software-Defined Networking (SDN), yet hybrid deployments remain dominated by slow legacy routing convergence. This paper presents two port-state-driven FRR mechanisms for homogeneous and hybrid SDN networks. First, Port-State-Aware Fast Reroute (PSA-FRR) uses OpenFlow [...] Read more.
Fast Reroute (FRR) after link failures is essential for carrier-grade Software-Defined Networking (SDN), yet hybrid deployments remain dominated by slow legacy routing convergence. This paper presents two port-state-driven FRR mechanisms for homogeneous and hybrid SDN networks. First, Port-State-Aware Fast Reroute (PSA-FRR) uses OpenFlow port-status events to trigger proactive, rule-based protection in the data plane. Second, Port-State-Aware Neural Fast Reroute (PSAR-FRR) formulates hybrid FRR as a controller-local multi-class classification problem and predicts the backup egress port from a port-centric state representation, enabling microsecond-scale decision latency. We evaluate the methods on the Abilene wide-area network (WAN) topology using Mininet with Open vSwitch (OVS) and a Ryu controller (homogeneous case) and Graphical Network Simulator-3 (GNS3) with Cisco IOS routers (hybrid baseline). In homogeneous SDN emulation, PSA-FRR restores connectivity within 30–100 ms under the evaluated configurations. In the hybrid baseline, conventional routing protocols converge in 13.8–256.1 s (Enhanced Interior Gateway Routing Protocol (EIGRP), Intermediate System to Intermediate System (IS-IS), Open Shortest Path First (OSPF), Border Gateway Protocol (BGP), and Routing Information Protocol (RIP)), confirming that control-plane recovery cannot meet a 50 ms target. Using the collected dataset, PSAR-FRR reduces controller decision time from 6.753 μs (PSA-FRR rule evaluation) to 0.214 μs (deep neural network (DNN) inference), a 31.5× speedup. These results show that port-state awareness combined with learned, controller-local policies can substantially reduce the decision-to-action latency of FRR, providing a practical path toward low-latency failure recovery in SDN migration scenarios. Full article
(This article belongs to the Special Issue Recent Advances in Software-Defined Networking (SDN))
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33 pages, 522 KB  
Article
Embedding-Based K-Means for Multi-Controller Placement in Software-Defined Networks: A Cross-Scale Empirical Study on Internet Topology Zoo
by Aymen Chentouf, Zouhair Chiba and Mounia Miyara
Network 2026, 6(3), 63; https://doi.org/10.3390/network6030063 - 6 Aug 2026
Viewed by 328
Abstract
Multi-controller deployments in Software-Defined Networking require choosing both the number of controllers and their placement on the topology. Clustering-based methods, particularly k-means, are widely used for this problem, but the topology representation that the clustering operates on is rarely chosen explicitly. We treat [...] Read more.
Multi-controller deployments in Software-Defined Networking require choosing both the number of controllers and their placement on the topology. Clustering-based methods, particularly k-means, are widely used for this problem, but the topology representation that the clustering operates on is rarely chosen explicitly. We treat the representation step as a design axis: the clustering algorithm is held fixed at k-means with k-means++ initialisation, and the input is varied across three classical, training-free embeddings of the propagation-delay distance matrix: metric multidimensional scaling (MDS), Isomap, and Laplacian Eigenmaps (Spectral). The evaluation covers thirteen Internet Topology Zoo backbones grouped into three scale tiers under an effective-N definition, with the controller count K varied from 2 to 10, and reports node-to-controller latency (N2C), controller-to-controller latency (C2C), and load imbalance jointly rather than singly, with paired significance tests over ten distinct seeds. The representation choice is consequential: 60% of pairwise embedding comparisons are statistically separated (Holm-corrected Wilcoxon, α=0.05), and the median best-versus-worst gap per configuration is 23–25% on the two latency metrics and 71% on load imbalance. Metric MDS achieves the lowest N2C in most regimes; Spectral achieves the lowest C2C on the medium and large tiers at mid-to-high K; Isomap trades single-metric wins for worst-case robustness and is non-dominated in 79% of the 63 large-tier configurations. Across all 117 configurations, each embedding is empirically non-dominated (within the embeddings compared and on this benchmark) in 67–85% of configurations, and 16% admit a single statistically separated best choice. Two baselines contextualise these results: k-means on the raw latency matrix leads in under 8% of configurations, and a Node2Vec baseline is competitive on C2C and load balance but trails the classical methods on N2C at one to two orders of magnitude higher embedding cost. Best-embedding identities transfer from k-means to a k-medoids variant in 70% of configurations. Capacity-, energy-, and reliability-aware extensions remain open. Full article
(This article belongs to the Special Issue Recent Advances in Software-Defined Networking (SDN))
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