Performance Optimization in Global Content Delivery Networks using Intelligent Caching and Routing Algorithms
DOI:
https://doi.org/10.15662/IJRAI.2021.0402003Keywords:
Network Optimization, Edge Analytics, Software-Defined Networking (SDN), Intelligent Caching, Machine Learning, Content Delivery Networks (CDNs), Latency ReductionAbstract
To mitigate end-user latency in global CDN deployments this paper proposes an intelligent caching and routing optimization architecture. Due to the general deployment model of traditional CDNs, based on heuristics-oriented routing algorithms and static placement caching, such systems cannot be flexible enough to adapt quickly to users demand changes and network conditions. We present a system that utilizes real-time traffic telemetry information with the support of predictive analytics and adaptive caching placement algorithms to address these limitations. For enhancing content serving rate and overall network capacity, we build a system that dynamically updates routing paths and cache placements by estimating congestion on the path to destination and popularity of contents. Our experimental results show substantial performance gains when performed on geographically distributed testbeds. This results in performance gains of more than 25% and content retrieval latency reductions up to 30% as compared to (pure) CDN caching strategies. This study demonstrates that AI prediction and adaptive network control are appropriate partners. This architecture not only outperforms existing solutions, it can also scale and adapt for use in high-performance providers that serve many diverse and active user bases. It has a modular design so that its use can be phased in, reducing the requirement for downtime and permitting future development. By enabling a better user experience, reducing the operational costs and evolutionary path to a green and datacentric content delivery system, we contribute to design more cost-effective smart solutions for next generation CDN.
References
Nygren, E., Sitaraman, R. K., & Sun, J. (2010). The Akamai Network: A Platform for High-Performance Internet Applications. ACM Digital Library. — Overview of Akamai’s distributed CDN architecture and performance optimization strategies.
2. On the Throughput Capacity of Information-Centric Networks. ResearchGate. — Foundational analysis of throughput and latency behavior in ICNs with in-network cachin over various topologies.
3. Azimdoost, B., Asghari, H., Gündüz, D., & Gesbert, D. (2016). Fundamental Limits on Throughput Capacity in Information-Centric Networks. arXiv. — Analytical study on capacity bounds for cache-enabled ICNs.
4. Zhang, L., Li, X., Lin, P., Wang, Y., & Shi, Y. (2015). Caching in Information-Centric Networking: A Survey. Semantic Scholar. — Comprehensive survey on caching mechanisms, replacement strategies, and performance issues in ICNs.
5. Golrezaei, N., Shanmugam, K., Dimakis, A. G., Molisch, A. F., & Caire, G. (2011–2012). FemtoCaching: Wireless Video Content Delivery through Distributed Caching Helpers. INFOCOM / arXiv. — Foundational framework introducing helper nodes for distributed caching in wireless networks.
6. Golrezaei, N., Molisch, A. F., Dimakis, A. G., & Caire, G. (2012). Wireless Video Content Delivery through Coded Distributed Caching. IEEE ICC / MIT Sloan Repository. — Extension of FemtoCaching using coded caching to improve wireless video delivery performance.
7. Poularakis, K., Iosifidis, G., & Tassiulas, L. (2013–2014). Approximation Algorithms for Mobile Data Caching in Small Cell Networks. IEEE Transactions on Communications / Globecom. — Joint caching and routing algorithms for massive mobile data delivery with approximation guarantees.
8. Ioannidis, S., Chaintoutis, C., & Tassiulas, L. (2017). Distributed, Adaptive Algorithms for Joint Routing and Caching with Provable Guarantees. arXiv. — Introduces distributed 1−1/e-approximation algorithms for optimal cache routing.
9. Baştuğ, E., Bennis, M., & Debbah, M. (2014). Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks. arXiv. — Pioneering work emphasizing proactive and edge caching for 5G systems.
10. Surveys on ICN / In-Network and Wireless/Small-Cell Caching (2014–2016). ACM Digital Library. — Multiple survey papers reviewing in-network caching frameworks, edge content distribution, and mobile caching challenges.
11. Tatarinov, I., Liu, L., & Others (Late 1990s). Static Caching of Web Servers / Server-Side Caching Studies. ACM Digital Library / Astrophysics Data System. — Early foundational studies analyzing static caching and content placement for web servers.
12. Dehghan, M., Seetharam, A., He, T., Salonidis, T., Kurose, J., & Towsley, D. (2014). Optimal Caching and Routing in Hybrid Networks. arXiv / MILCOM Proceedings. — Joint optimization of caching and routing strategies for hybrid MANET–cellular environments.
13. QoS & Policy-Management Surveys / Whitepapers (2010–2018). Scribd. — Surveys and technical whitepapers covering QoS frameworks, policy enforcement, and QoE validation in mobile data networks.
14. B. Zolfaghari, G. Srivastava, S. Roy, H. R. Nemati, F. Afghah, T. Koshiba, A. Razi, K. Bibak, P. Mitra and B. K. Rai, “Content Delivery Networks: State of the Art, Trends, and Future Roadmap,” ACM Computing Surveys, vol. 53, no. 2, article 3380613, Jun. 2020.
15. S. Ioannidis and E. Yeh, “Jointly Optimal Routing and Caching for Arbitrary Network Topologies,” IEEE Journal on Selected Areas in Communications, vol. 36, no. 6, pp. 1258–1275, Jun. 2018, doi: 10.1109/JSAC.2018.2844958.
