International Journal of Computer Techniques Volume 12 Issue 3 | AI-Driven Optimization for Enhancing Performance, Efficiency, and Personalization in Content Delivery Networks
AI-Driven Optimization for Enhancing Performance, Efficiency, and Personalization in Content Delivery Networks
International Journal of Computer Techniques – Volume 12 Issue 3, May – June 2025
Abstract
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**.
Keywords
Artificial Intelligence, Machine Learning, Content Delivery Network, Predictive Caching, Adaptive Preloading, Edge Computing, Personalized Content Delivery, AI CDN Optimization.
Conclusion
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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