AI -Driven Scalability and Resource Management in Cloud - Based Multimedia Content Delivery Networks

Abstract

The rapid growth of multimedia content and user demand has led to the need for highly efficient and scalable delivery systems. This paper explores AI-driven scalability and resource management in cloud-based Multimedia Content Delivery Networks (CDNs). Traditional CDNs often face challenges in handling dynamic workloads, bandwidth constraints, and latency-sensitive content distribution. By integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms, cloud-based CDNs can dynamically allocate computational and storage resources, predict user demand, and optimize data routing for better Quality of Service (QoS). The study emphasizes the role of AI techniques such as reinforcement learning, predictive analytics, and neural network-based optimization in automating resource provisioning and reducing operational costs. Furthermore, AI-driven approaches enhance adaptability to fluctuating network conditions, ensuring real-time decision-making and intelligent caching strategies. The integration of AI with edge computing and 5G networks also provides a new dimension to low-latency content delivery. Overall, the paper highlights how AI-enabled scalability and intelligent resource management significantly improve performance, energy efficiency, and user experience in cloud-based multimedia systems.