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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Harb, Hassan
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Computer Science & IT Electrical & Electronics Engineering Engineering

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No binding machine learning architecture for SDN controllers Hosny Fouad Aly, Wael; Kanj, Hassan; Mostafa, Nour; Al-Arnaout, Zakwan; Harb, Hassan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8483

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 SDNCTRLML , a novel approach that applies machine learning mechanisms to improve load balancing. SDNCTRLML introduces a scheduling layer that dynamically assigns flow requests to controllers using machine learning scheduling algorithms. Unlike previous approaches, SDNCTRLML integrates with the standard SDN switches and adapts to different scheduling algorithms, minimizing disruption and network delays. Experimental results show that SDNCTRLML has outperformed static-binding controllers models without adding complexities of dynamic-binding systems.
AI-powered hub optimization: a reinforcement learning and graph-based approach to scalable blockchain networks Danach, Kassem; Rkein, Hassan; Ramadan, Alaaeddine; Harb, Hassan; Hamdar, Bassam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp536-546

Abstract

Blockchain networks face persistent scalability challenges, including network congestion, high latency, and transaction costs. To address these limitations, this study proposes an AI-driven hub location optimization framework that integrates reinforcement learning (RL), mixed integer linear programming (MILP), and graph neural networks (GNNs). The RL-based hub selection dynamically identifies optimal supernode placement, while MILP ensures cost-efficient transaction routing, and GNNs predict flow patterns for proactive congestion management. Experimental results on Ethereum and Bitcoin datasets demonstrate significant improvements, including a 58.6% reduction in transaction latency, 28.9% gas fee savings, and 41.5% congestion reduction. Beyond performance gains, statistical tests confirm the significance of these improvements, and ablation studies highlight the complementary role of each component.
Co-Authors Al-Arnaout, Zakwan Danach, Kassem Hamdar, Bassam Hosny Fouad Aly, Wael Kanj, Hassan Mostafa, Nour Ramadan, Alaaeddine Rkein, Hassan