Home / Current Issue / Paper 1707065
Review on Accelerating Web Performance: The Role of AI-Driven Content Delivery Networks
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
This review brings the tremendous growth of the internet has propelled faster, safer, more efficient Content Distribution Networks (CDNs). Conventional CDNs, while their efficiency, could find it challenging to meet the demands of modern online applications especially given the growing complexity of material and user activity. Emphasising AI-driven architectures that enhance online performance, scalability, and security, this article explores how artificial intelligence (AI) might change CDNs. The major objective of this article is to investigate how artificial intelligence technologies?machine learning, deep learning, and real-time analytics?are changing content delivery, cutting latency, and allowing more tailored experiences for customers. This article also examines the possibilities these systems have in enhancing user interaction and content delivery optimisation as well as the challenges in adopting AI-driven CDNs?including infrastructure needs, data privacy issues, and security threats. The method demands for a thorough examination of present research, case studies, and most recent breakthroughs in artificial intelligence-driven CDN solutions. Key findings reveal fairly remarkably that integration of artificial intelligence enhances dynamic content distribution, resource allocation, and real-time danger identification. Moreover more scalable, operationally effective, and help to raise user happiness are AI-driven CDNs. The last section of the paper discusses expected improvements and changes in CDN performance in future developments: Edge computing and more intricate artificial intelligence models' integration.
Keywords
AI-driven CDNs, Content Delivery Networks, Machine Learning, Web Performance Optimization, Security and Scalability
References
[1] C. Code, “p1 Review on Intelligent Software Agents for Continuous Delivery ( First ).,” 2025.
[2] C. Challoumis, “FROM INVESTMENT TO PROFIT - EXPLORING THE AI-DRIVEN CYCLE OF,” no. November, 2024.
[3] S. Dodda, A. D. Processing, S. Narne, M. Mohan, and T. Ayyalasomayajula, “AI-Driven Decision Support Systems in Management : Enhancing Strategic Planning and Execution,” no. March, 2024.
[4] A. T. Aderamo, “AI-Driven HSE management systems for risk mitigation in the oil and gas industry AI-Driven HSE management systems for risk mitigation in the oil and gas industry,” no. October, 2024, doi: 10.57219/crret.2024.2.1.0059.
[5] “AI - Driven Quality Control in PCB Manufacturing : Enhancing Production Efficiency and Precision,” no. October, 2024, doi: 10.18535/ijsrm/v12i10.ec06.
[6] Enoch Oluwademilade Sodiya et al., “Reviewing the role of AI and machine learning in supply chain analytics,” GSC Adv. Res. Rev., vol. 18, no. 2, pp. 312–320, 2024, doi: 10.30574/gscarr.2024.18.2.0069.
[7] M. Reiners and W. Van Der Bijl, “Content delivery networks,” J. Commun. Netw., vol. 3, no. 3, p. 152, Jul. 2004, doi: 10.1145/3380613.
[8] H. H. Loeffler et al., “Reinvent 4: Modern AI–driven generative molecule design,” J. Cheminform., vol. 16, no. 1, pp. 1–16, 2024, doi: 10.1186/s13321-024-00812-5.
[9] Joseph Nnaemeka Chukwunweike, Moshood Yussuf, Oluwatobiloba Okusi, Temitope Oluwatobi Bakare, and Ayokunle J. Abisola, “The role of deep learning in ensuring privacy integrity and security: Applications in AI-driven cybersecurity solutions,” World J. Adv. Res. Rev., vol. 23, no. 2, pp. 1778–1790, 2024, doi: 10.30574/wjarr.2024.23.2.2550.
[10] M. Gutierrez Lopez, C. Porlezza, G. Cooper, S. Makri, A. MacFarlane, and S. Missaoui, “A Question of Design: Strategies for Embedding AI-Driven Tools into Journalistic Work Routines,” Digit. Journal., vol. 11, no. 3, pp. 484–503, 2023, doi: 10.1080/21670811.2022.2043759.
[11] J. Willems, M. J. Schmid, D. Vanderelst, D. Vogel, and F. Ebinger, “AI-driven public services and the privacy paradox: do citizens really care about their privacy?,” Public Manag. Rev., vol. 25, no. 11, pp. 2116–2134, 2023, doi: 10.1080/14719037.2022.2063934.
[12] Y. Wu, L. Zhang, Z. Gu, H. Lu, and S. Wan, “Edge-AI-Driven Framework with Efficient Mobile Network Design for Facial Expression Recognition,” ACM Trans. Embed. Comput. Syst., vol. 22, no. 3, 2023, doi: 10.1145/3587038.
[13] Y. A. Ivanenkov et al., “Chemistry42: An AI-Driven Platform for Molecular Design and Optimization,” J. Chem. Inf. Model., vol. 63, no. 3, pp. 695–701, 2023, doi: 10.1021/acs.jcim.2c01191.
[14] B. Markus et al., “Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design,” ACS Catal., vol. 13, no. 21, pp. 14454–14469, 2023, doi: 10.1021/acscatal.3c03417.
[15] J. C. Liang, G. J. Hwang, M. R. A. Chen, and D. Darmawansah, “Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach,” Interact. Learn. Environ., vol. 31, no. 7, pp. 4270–4296, 2023, doi: 10.1080/10494820.2021.1958348.
[16] A. Ethan, “AI-Driven Anomaly Detection in NoSQL Databases for Enhanced Security AI-Driven Anomaly Detection in NoSQL Databases for Enhanced Security Hemanth Gadde,” no. November, 2024.
[17] M. A. Faheem, “AI-Driven Risk Assessment Models : Revolutionizing Credit Scoring and Default AI-Driven Risk Assessment Models : Revolutionizing Credit Scoring and Default Prediction,” no. October, 2024, doi: 10.13140/RG.2.2.21281.01128.
[18] N. Alkassab, C. T. Huang, and T. L. Botran, “DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery Networks,” Proc. - Int. Conf. Comput. Commun. Networks, ICCCN, 2024, doi: 10.1109/ICCCN61486.2024.10637652.
[19] S. Kumar, W. M. Lim, U. Sivarajah, and J. Kaur, “Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis,” Inf. Syst. Front., vol. 25, no. 2, pp. 871–896, 2023, doi: 10.1007/s10796-022-10279-0.
[20] O. C. Oyeniran, A. O. Adewusi, and A. G. Adeleke, “AI-driven devops : Leveraging machine learning for automated software deployment and maintenance,” no. December 2023, 2024, doi: 10.51594/estj.v4i6.1552.
[21] T. Muhammad, “A Comprehensive Study on Software-Defined Load Balancers: Architectural Flexibility & Application Service Delivery in On-Premises Ecosystems,” Int. J. Comput. Sci. Technol., vol. 6, no. 1, 2022, [Online]. Available: https://www.researchgate.net/publication/376046455
[22] E. Aguas et al., “Towards network resiliency with AI driven automated load sharing in content delivery environments To cite this version : HAL Id : hal-04165399 Towards Network Resiliency with AI Driven Automated Load Sharing in Content Delivery Environments,” 2023.
[23] P. Nair, S. Sharma, R. Sharma, and A. Gupta, “Leveraging Reinforcement Learning and Collaborative Filtering for Enhanced AI-Driven Targeted Content Delivery Authors :,” pp. 1–25.
[24] “The Evolution Of Content Delivery Networks (cdns) - FasterCapital.” https://fastercapital.com/topics/the-evolution-of-content-delivery-networks-%28cdns%29.html (accessed Jan. 16, 2025).
[25] R. Kaul et al., “The role of AI for developing digital twins in healthcare: The case of cancer care,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 13, no. 1, pp. 1–13, 2023, doi: 10.1002/widm.1480.
[26] F. Jiang, L. Dong, K. Wang, K. Yang, and C. Pan, “Distributed Resource Scheduling for Large-Scale MEC Systems: A Multiagent Ensemble Deep Reinforcement Learning with Imitation Acceleration,” IEEE Internet Things J., vol. 9, no. 9, pp. 6597–6610, 2022, doi: 10.1109/JIOT.2021.3113872.
[27] A. Barnwal, H. Cho, and T. Hocking, “Survival Regression with Accelerated Failure Time Model in XGBoost,” J. Comput. Graph. Stat., vol. 31, no. 4, pp. 1292–1302, 2022, doi: 10.1080/10618600.2022.2067548.
[28] N. Tsolakis, D. Zissis, S. Papaefthimiou, and N. Korfiatis, “Towards AI driven environmental sustainability: an application of automated logistics in container port terminals,” Int. J. Prod. Res., vol. 60, no. 14, pp. 4508–4528, 2022, doi: 10.1080/00207543.2021.1914355.
[29] D. Atkins et al., “Accelerating Battery Characterization Using Neutron and Synchrotron Techniques: Toward a Multi-Modal and Multi-Scale Standardized Experimental Workflow,” Adv. Energy Mater., vol. 12, no. 17, 2022, doi: 10.1002/aenm.202102694.
[30] A. Al-Surmi, M. Bashiri, and I. Koliousis, “AI based decision making: combining strategies to improve operational performance,” Int. J. Prod. Res., vol. 60, no. 14, pp. 4464–4486, 2022, doi: 10.1080/00207543.2021.1966540.
[31] S. Datta et al., “A new paradigm for accelerating clinical data science at Stanford Medicine,” 2020, [Online]. Available: http://arxiv.org/abs/2003.10534
[32] S. Zheng et al., “The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies,” 2020, [Online]. Available: http://arxiv.org/abs/2004.13332
[33] O. H. Chi, G. Denton, and D. Gursoy, “Artificially intelligent device use in service delivery: a systematic review, synthesis, and research agenda,” J. Hosp. Mark. Manag., vol. 29, no. 7, pp. 757–786, 2020, doi: 10.1080/19368623.2020.1721394.
[34] Y. Cheng and H. Jiang, “How Do AI-driven Chatbots Impact User Experience? Examining Gratifications, Perceived Privacy Risk, Satisfaction, Loyalty, and Continued Use,” J. Broadcast. Electron. Media, vol. 64, no. 4, pp. 592–614, 2020, doi: 10.1080/08838151.2020.1834296.
[35] M. Priestley, T. J. Sluckin, and T. Tiropanis, “Innovation on the web: the end of the S-curve?,” Internet Hist., vol. 4, no. 4, pp. 390–412, 2020, doi: 10.1080/24701475.2020.1747261.
[36] B. Guembe, A. Azeta, S. Misra, V. C. Osamor, L. Fernandez-Sanz, and V. Pospelova, The Emerging Threat of Ai-driven Cyber Attacks: A Review, vol. 36, no. 1. Taylor & Francis, 2022. doi: 10.1080/08839514.2022.2037254.
[37] O. Hennigh et al., “NVIDIA SimNetTM: An AI-Accelerated Multi-Physics Simulation Framework,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12746 LNCS, pp. 447–461, 2021, doi: 10.1007/978-3-030-77977-1_36.
[38] P. Bhattacharya et al., “AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US,” Proc. - 2021 IEEE Int. Conf. Big Data, Big Data 2021, no. April, pp. 1566–1574, 2021, doi: 10.1109/BigData52589.2021.9671811.
[39] L. Casalino et al., “AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics,” Int. J. High Perform. Comput. Appl., vol. 35, no. 5, pp. 432–451, 2021, doi: 10.1177/10943420211006452.
[40] X. Rodríguez-Martínez, E. Pascual-San-José, and M. Campoy-Quiles, “Accelerating organic solar cell material’s discovery: high-throughput screening andbig data,” Energy Environ. Sci., vol. 14, no. 6, pp. 3301–3322, 2021, doi: 10.1039/d1ee00559f.
[41] S. Reich et al., “Novel AI driven approach to classify infant motor functions,” Sci. Rep., vol. 11, no. 1, pp. 1–13, 2021, doi: 10.1038/s41598-021-89347-5.
[42] M. C. R. Melo, J. R. M. A. Maasch, and C. de la Fuente-Nunez, “Accelerating antibiotic discovery through artificial intelligence,” Commun. Biol., vol. 4, no. 1, pp. 1–13, 2021, doi: 10.1038/s42003-021-02586-0.
How to cite this paper
@article{1707065,
author = {Gireesh Kambala},
title = {Review on Accelerating Web Performance: The Role of AI-Driven Content Delivery Networks},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {2},
pages = {1021-1030},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707065.pdf},
abstract = {This review brings the tremendous growth of the internet has propelled faster, safer, more efficient Content Distribution Networks (CDNs). Conventional CDNs, while their efficiency, could find it challenging to meet the demands of modern online applications especially given the growing complexity of material and user activity. Emphasising AI-driven architectures that enhance online performance, scalability, and security, this article explores how artificial intelligence (AI) might change CDNs. The major objective of this article is to investigate how artificial intelligence technologies?machine learning, deep learning, and real-time analytics?are changing content delivery, cutting latency, and allowing more tailored experiences for customers. This article also examines the possibilities these systems have in enhancing user interaction and content delivery optimisation as well as the challenges in adopting AI-driven CDNs?including infrastructure needs, data privacy issues, and security threats. The method demands for a thorough examination of present research, case studies, and most recent breakthroughs in artificial intelligence-driven CDN solutions. Key findings reveal fairly remarkably that integration of artificial intelligence enhances dynamic content distribution, resource allocation, and real-time danger identification. Moreover more scalable, operationally effective, and help to raise user happiness are AI-driven CDNs. The last section of the paper discusses expected improvements and changes in CDN performance in future developments: Edge computing and more intricate artificial intelligence models' integration.},
keywords = {AI-driven CDNs, Content Delivery Networks, Machine Learning, Web Performance Optimization, Security and Scalability},
month = {August},
}