Personalization and Performance Optimization in AI-Augmented Content Delivery Networks

Abstract

The advent of artificial intelligence (AI) has significantly transformed the landscape of Content Delivery Networks (CDNs), enabling more personalized, efficient, and performance-driven content distribution. This paper explores the integration of AI in CDNs to enhance both personalization and performance optimization. We examine the application of machine learning algorithms for real-time content recommendation, traffic prediction, and adaptive caching strategies, which enable CDNs to tailor content to individual user preferences and optimize resource utilization. Additionally, AI-driven analytics allow for proactive anomaly detection and network optimization, reducing latency and ensuring seamless user experiences across diverse devices and geographic locations