No Binding Machine Learning Architecture for SDN Controllers - Machine learning scheduling for load balancing

Aug 3, 2026

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Software-defined networking improves network management but still faces inefficient load balancing across distributed controllers. Existing binding architectures often create uneven controller workloads and can degrade performance. Dynamic binding approaches mitigate imbalance yet add significant latency and complexity. This paper proposes SDNCTRL ML, integrating machine learning scheduling into a dedicated scheduling layer to assign flow requests to controllers. Results show SDNCTRL ML outperforms static-binding designs without adding dynamic-binding overhead.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

SDN architecture and APIs

AI and machine learning for SDN load balancing

Learning methods used in SDN

Bulletin of Electrical Engineering and Informatics Vol. 14, No. 3, June 2025, pp. 2413∼2428 ISSN: 2302-9285, DOI: 10 . 11591/eei.v14i3 .8483 ❒ 2413 | No binding machine learning architecture for SDN
controllers
Wael Hosny Fouad Aly, Hassan Kanj, Nour Mostafa, Zakwan Al-Arnaout, Hassan Harb
College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait | | | | --- | --- | --- | | Article Info
Article history:
Received Mar 19, 2024 Revised Nov 4, 2024 Accepted Mar 9, 2025
Keywords:
Artificial intelligence Controller placement Machine learning Neural networks
Software-defined networking | | ABSTRACT
Although software-defined networking (SDN) has improved the network management process, but challenges persist in achieving efficient load balancing among distributed controllers. Present architectures often suffer from uneven load distribution, leading to significant performance deterioration. While dynamic binding mechanisms have been explored to address this issue, these mechanisms are complex and introduce a significant latency. This paper proposes SDNCTRL ML, a novel approach that applies machine learning mechanisms to improve load balancing. SDNCTRL ML introduces a scheduling layer that dynamically assigns flow requests to controllers using machine learning scheduling algorithms. Unlike previous approaches, SDNCTRL ML integrates with the standard SDN switches and adapts to different scheduling algorithms, minimizing disruption and network delays. Experimental results show that SDNCTRL ML has outperformed static-binding controllers models without adding complexities of dynamic-binding systems.
This is an open access article under the CC BY-SA license. | | Corresponding Author: | | | | Wael Hosny Fouad Aly
College of Engineering and Technology, American University of the Middle East Egaila 54200, Kuwait
[Email: wael.aly@aum.edu.kw](Email: wael.aly@aum.edu.kw) | | | 1. INTRODUCTION Scalable network architectures are important in various network applications. They enable the provision of reliable and sufficient services for specific types of traffic [1] . Scalability could be achieved by maintaining an overview of the states and conditions of networks worldwide as observed from a broader perspective by regulating the flow of the network traffic across underlying layers [2] . This has led to significant changes in network design and management [3] . A prominent example of such a deployment is the Ethane project [4], which introduces a network paradigm for SDNs that utilizes a central controller managing policy atthe flow level. According to Almadani et al. [5], the SDN paradigm has the advantage of separating the control plane from the data plane, which leads to a faster and more dynamic approach compared to traditional network architectures [6] . Prabakaran et al. [7] suggest that the control plane could be split into multiple virtual networks implementing different policies. This approach allows the SDN paradigm to address networking issues from different perspectives [8], and to meet the requirements of emerging technologies like IoT and 5G [9] . The adoption of the SDN paradigm depends on its success in providing solutions to problems that could not be addressed through conventional networking protocols and architectures. Large companies such as Microsoft and Google have already implemented the SDN paradigm in their data centers [10]-[13] . The SDN architecture is composed of three main planes: the data, control, and application planes, as depicted in Figure 1 . The data plane is responsible for forwarding packets, the control plane decides how packets should be forwarded, and the application plane hosts network services [14] . The northbound API facilitates communication between the control and application planes, enabling developers to build applications without needing detailed knowledge of the controller operations within the data plane [1

FAQs

What problem does the paper target in SDN controller architectures?

It targets inefficient load balancing among distributed controllers, which can cause uneven load distribution and performance deterioration.

How does SDNCTRL ML improve load balancing?

SDNCTRL ML introduces a scheduling layer that uses machine learning scheduling algorithms to dynamically assign flow requests to controllers.

What is the key difference from prior dynamic-binding approaches?

SDNCTRL ML integrates with standard SDN switches and adapts to different scheduling algorithms, minimizing disruption and network delays.

Aug 3, 2026

Upload by Quinn