In the modern digital age, the proliferation of devices, the explosion of data traffic, and the increasing complexity of network environments have driven the demand for more sophisticated and efficient network management solutions. The unrelenting growth of the digital landscape, fueled by cloud computing, the Internet of Things (IoT), and bandwidth-hungry applications, has exposed the limitations of traditional network architectures. These rigid structures struggle to adapt to the ever-fluctuating demands of modern traffic patterns, leading to bottlenecks, inefficiencies, and security vulnerabilities. Network administrators are constantly battling congestion, struggling to maintain optimal performance, and forced to react to security breaches after they occur. Traditional networking approaches, characterized by their static and hardware-centric nature, struggle to meet the dynamic requirements of contemporary networks. This has led to the advent of Software-Defined Networking (SDN), a paradigm shift that decouples the control plane from the data plane, enabling centralized, programmable, and dynamic network control.
SDN emerged as a paradigm shift, offering a programmable approach to network management. By decoupling the data plane (packet forwarding) from the control plane (network policies), SDN introduces a centralized controller that can dynamically configure and optimize network behavior. This newfound programmability offers significant advantages over traditional methods, allowing for automation and easier configuration changes. However, the complexity of modern networks, with their ever-evolving demands and constantly shifting landscapes, necessitates more than just programmable logic. SDN provides a flexible and agile infrastructure, allowing network administrators to manage network resources and policies more efficiently. However, as networks continue to grow in scale and complexity, even SDN-based approaches face challenges related to scalability, adaptability, and real-time decision-making. This is where the integration of AI comes into play. AI, with its capabilities in machine learning, pattern recognition, and predictive analytics, offers promising solutions to enhance the intelligence and automation of SDN networks.
• Analyze the existing literature on AI-powered SDN (also referred to as Intelligent SDN or AI-SDN). • Identify key areas where AI can be integrated with SDN to improve network performance, security, and efficiency. • Evaluate different AI techniques (machine learning, deep learning) suitable for SDN applications. • Design and implement a prototype system demonstrating the integration of AI with a specific SDN function (e.g., traffic optimization, anomaly detection). • Analyze the performance and scalability of the proposed AI-powered SDN solution.