Optimal Video Caching at The Edge of Network by Using Machine Learning

Abstract Efficiently managing network resources in the dynamic field of video-on-demand (VoD) services is a significant challenge. This requires creative methods to optimize video caching strategies. This study examines the use of machine learning (ML), particularly reinforcement learning (RL), into edge network designs to improve video caching procedures. The decentralized, adaptable, and learning characteristics of machine learning offer a powerful method for effectively managing caching in real-time. This technique leads to reduced latency, optimized bandwidth utilization, and improved cache hit rates. Conventional caching algorithms frequently fail to adjust to the changing needs and popularity of material that is inherent in Video on Demand (VoD) services. Our study presents an innovative reinforcement learning (RL) caching method that is specifically developed to automatically adapt caching choices using real-time data analysis, thus guaranteeing an effective content delivery network. We created and evaluated three separate machine learning models: Convolutional Neural Networks (CNN) for extracting features, Deep Q-Networks (DQN) for generating decisions, and a hybrid model that combines the advantages of both CNN and DQN to achieve improved performance. By conducting thorough simulations and implementing our models in real-world deployment scenarios, we have shown substantial improvements compared to traditional caching methods in terms of scalability, adaptability to evolving user patterns, and overall network efficiency. This innovative method not only simplifies the transfer of content in Video on Demand (VoD) systems, but also establishes a new standard for integrating Artificial Intelligence (AI) technologies in network optimization. It effectively tackles present and future obstacles in the digital streaming industry.