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Advances in Computer Networks and Software-Defined Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 595

Special Issue Editors


E-Mail Website
Guest Editor
Institute of Telecommunications, AGH University of Krakow, Krakow, Poland
Interests: computer networks; computer networking; telecommunications engineering; information theory; QoS; routing

E-Mail Website
Guest Editor
Institute of Telecommunications, AGH University of Krakow, Krakow, Poland
Interests: computer networks; computer networking; telecommunications engineering; information theory; optical networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue.

The concept of Software-Defined Networks (SDNs) is widely known. It involves separating control panels from data to enable efficient transmission. Recent years have seen significant developments in internet technologies. Most of these are related to machine-learning algorithms, which can significantly improve the operation of computer networks. Machine-learning solutions are being implemented alongside the concept of SDNs.

This Special Issue invites submissions of articles covering all aspects of SDNs, including new controllers, security, traffic engineering based on machine-learning algorithms and more. Both theoretical and experimental papers, as well as comprehensive review and overview articles, are welcome.

Dr. Jerzy Domzal
Dr. Edyta Biernacka
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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 networks
  • machine-learning
  • traffic engineering
  • cross-layer networks
  • SDN controllers

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Published Papers (1 paper)

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Research

23 pages, 1737 KB  
Article
Log-Driven Proximal Policy Optimization for Adaptive Traffic Control in Software-Defined Networks
by Abzal E. Kyzyrkanov, Yedil S. Nurakhov, Zhenis Otarbay and Danil V. Lebedev
Appl. Sci. 2026, 16(9), 4424; https://doi.org/10.3390/app16094424 - 1 May 2026
Viewed by 263
Abstract
Software-Defined Networking (SDN) enables centralised and programmable traffic control, but adaptive optimization in operational networks remains challenging when safe online exploration is limited and only historical controller traces are available. This study proposes a log-driven Proximal Policy Optimisation (PPO) framework for adaptive SDN [...] Read more.
Software-Defined Networking (SDN) enables centralised and programmable traffic control, but adaptive optimization in operational networks remains challenging when safe online exploration is limited and only historical controller traces are available. This study proposes a log-driven Proximal Policy Optimisation (PPO) framework for adaptive SDN traffic control that learns directly from recorded state–action–reward transitions. The method uses a replay-based pseudo-environment constructed from controller logs. It combines clipped PPO updates with action-consistency regularisation and running state normalisation to improve stability under logged-data constraints. The empirical evaluation shows that the learned model reconstructs the dominant response pattern observed in the traces, preserves a positive relationship between the principal control-related predictor and the response, and reveals a non-uniform interaction structure across telemetry features. The framework also differentiates systematically across operating conditions and experimental groups, with category means ranging from 0.78 to 1.24 and group medians ranging from 0.12 to 1.12, while receiver operating characteristic analysis yields an area under the curve of 0.714. The practical network evaluation further shows that the PPO-controlled setting improves overall throughput, packet loss, jitter, and flow-completion success relative to the baseline controller. These results indicate that log-driven, stability-constrained PPO can provide a stable and informative basis for adaptive SDN traffic control when policy learning must rely on historical controller data rather than unrestricted live-network experimentation. Full article
(This article belongs to the Special Issue Advances in Computer Networks and Software-Defined Networks)
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