International Journal of Computer Techniques Volume 12 Issue 3 | AI-Driven Optimization for Enhancing Performance, Efficiency, and Personalization in Content Delivery Networks
International Journal of Computer Techniques Volume 12 Issue 3 | AI-Driven Optimization for Enhancing Performance, Efficiency, and Personalization in Content Delivery Networks
Content Delivery Networks (CDNs) have evolved into **dynamic, AI-powered architectures** that enhance **performance, efficiency, and personalized content delivery**. This paper presents **machine learning-driven techniques** for **predictive caching, adaptive preloading, and real-time optimizations**, achieving **up to 40% improved cache hit rates and reduced latency**. By implementing **AI-enhanced edge computing**, the study outlines **strategies for scalable and resilient CDN deployment**.
The **AI-driven CDN framework** significantly enhances **content distribution by integrating predictive analytics, adaptive content control, and federated learning**. Future research should prioritize **privacy-preserving AI models, fairness-aware optimization, and security-enhanced CDN infrastructures** for scalable AI adoption in content networks.
References
F. Wang et al. (2020). “Intelligent Video Caching at Network Edge: A Multi-Agent Deep Reinforcement Learning Approach.” IEEE INFOCOM.
H. Mao et al. (2017). “Neural Adaptive Video Streaming with Pensieve.” ACM SIGCOMM.
J. Zhou et al. (2022). “Data Caching Optimization with Fairness in Mobile Edge Computing.” IEEE Transactions on Services Computing.
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