An AI-Driven Framework for Dynamic Resource Allocation in Software-Defined Networking to Optimize Cloud Infrastructure Performance and Scalability

Abstract

The rise of cloud computing and the proliferation of highly dynamic workloads present significant challenges for efficient resource management in cloud infrastructures. Existing resource allocation methods are ill-equipped to handle the real-time demands of modern cloud services, especially in sce- narios that require low latency, high throughput, and scalability. Software- Defined Networking (SDN) is a promising approach to address these chal- lenges by decoupling the control plane from the data plane, enabling more flexible and programmable network management. However, the static na- ture of conventional SDN architectures limits their capacity to react dy- namically to fluctuating cloud workloads. This paper proposes a novel framework integrating Artificial Intelligence (AI) with SDN for dynamic resource allocation in cloud environments. The study focuses on the devel- opment of AI-driven resource allocation algorithms, using machine learning (ML) techniques such as reinforcement learning (RL), deep learning (DL), and predictive analytics to optimize network performance, reduce latency, and improve overall service quality. The proposed AI-SDN architecture dy- namically adjusts network resources based on real-time data, network state predictions, and workload analysis, enabling more agile, responsive, and ef- ficient cloud infrastructure. This paper further explores the architectural considerations, algorithmic design, and performance metrics of AI-driven SDN systems, demonstrating the porspective of AI to transform SDN into a fully autonomous network management paradigm capable of meeting the demands of modern cloud.