Emerging Trends and Challenges of Software-Defined Networking (SDN) Technologies—2nd Edition

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Cloud Continuum and Enabled Applications".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 528

Special Issue Editor


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Guest Editor
School of Liberal Arts, Indiana University Indianapolis, Indianapolis, IN, USA
Interests: cloud computing; software-defined networking; serverless computing; high performance computing; big data; learning analytics
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Special Issue Information

Dear Colleagues,

We are excited to announce a Special Issue of Computers on the topic of "Emerging Trends and Challenges of Software-Defined Networking (SDN) Technologies—2nd Edition". Software-defined networks (SDNs) have been an emerging trend in the world of computer networking for some time. In simple terms, ‘SDN’ describes an approach to networking where the control plane is separated from the data plane. This separation allows for greater flexibility and scalability, as well as improved automation and programmability. Using SDN, network administrators have the ability to manage network traffic and reconfigure network devices dynamically. The recent trends in SDN are network function virtualization (NFV) and software-defined wide-area network (SDN-WAN). The network function virtualization transforms the complex network functions embedded in the hardware into software instances running in the virtual infrastructure. SDN-WAN expands centralized control across the wide-area network (WAN) to cloud providers. The proper allocation of those network functions (NFs) and intelligent routing in SDN-WAN efficiently improves quality of service (QoS).

This Special Issue aims to bring together the latest research on recent developments in SDN technologies. We welcome novel research articles, comprehensive reviews, and survey articles. Extended conference papers are also welcome, but they should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.

Topics of interest include, but are not limited to, the following:

  • SDN–IoT;
  • Security in SDNs;
  • Internet of Things (IoT) cloud platforms based on SDNs;
  • SDN-NFV;
  • SDN-WAN.

Dr. Kannan Govindarajan
Guest Editor

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Keywords

  • software-defined networks
  • mobile networks
  • 5G
  • 6G
  • big data
  • mobile edge computing
  • energy efficiency
  • security and privacy
  • blockchain
  • network resources allocation
  • Internet of Things
  • cloud computing
  • network function virtualization
  • software-defined wide-area network

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Published Papers (1 paper)

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28 pages, 1509 KiB  
Article
Adaptive Congestion Detection and Traffic Control in Software-Defined Networks via Data-Driven Multi-Agent Reinforcement Learning
by Kaoutar Boussaoud, Abdeslam En-Nouaary and Meryeme Ayache
Computers 2025, 14(6), 236; https://doi.org/10.3390/computers14060236 - 16 Jun 2025
Viewed by 302
Abstract
Efficient congestion management in Software-Defined Networks (SDNs) remains a significant challenge due to dynamic traffic patterns and complex topologies. Conventional congestion control techniques based on static or heuristic rules often fail to adapt effectively to real-time network variations. This paper proposes a data-driven [...] Read more.
Efficient congestion management in Software-Defined Networks (SDNs) remains a significant challenge due to dynamic traffic patterns and complex topologies. Conventional congestion control techniques based on static or heuristic rules often fail to adapt effectively to real-time network variations. This paper proposes a data-driven framework based on Multi-Agent Reinforcement Learning (MARL) to enable intelligent, adaptive congestion control in SDNs. The framework integrates two collaborative agents: a Congestion Classification Agent that identifies congestion levels using metrics such as delay and packet loss, and a Decision-Making Agent based on Deep Q-Learning (DQN or its variants), which selects the optimal actions for routing and bandwidth management. The agents are trained offline using both synthetic and real network traces (e.g., the MAWI dataset), and deployed in a simulated SDN testbed using Mininet and the Ryu controller. Extensive experiments demonstrate the superiority of the proposed system across key performance metrics. Compared to baseline controllers, including standalone DQN and static heuristics, the MARL system achieves up to 3.0% higher throughput, maintains end-to-end delay below 10 ms, and reduces packet loss by over 10% in real traffic scenarios. Furthermore, the architecture exhibits stable cumulative reward progression and balanced action selection, reflecting effective learning and policy convergence. These results validate the benefit of agent specialization and modular learning in scalable and intelligent SDN traffic engineering. Full article
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