IMPROVING MECHANISMS FOR CONTENT PLACEMENT AND MANAGEMENT BASED ON ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.5281/zenodo.21412180Keywords:
Artificial Intelligence (AI), Content Delivery Network (CDN), Content Management, Content Placement, Digital Economy, Machine Learning, Econometric Modeling, Digital Platform, Key Performance Indicators (KPI), Real-Time Monitoring, Forecasting, Digital Infrastructure, Decision Support SystemAbstract
The rapid advancement of the digital economy and the widespread adoption of Internet
technologies have significantly transformed the processes of content creation, distribution, and management.
The increasing volume of digital information and the growing demand for personalized services require the
development of intelligent mechanisms capable of improving the efficiency of content management systems.
This study aims to improve content placement and management mechanisms through the application of Artificial
Intelligence (AI) technologies integrated with Content Delivery Network (CDN) infrastructure.
The research employs a combination of comparative analysis, system analysis, econometric modeling,
artificial intelligence algorithms, and digital platform development methods. Based on these approaches, the
AI-CDN Economic Efficiency Platform was developed as an integrated digital solution that combines realtime
monitoring, Key Performance Indicator (KPI) evaluation, AI-based optimization, econometric analysis,
forecasting, and scientific reporting within a unified information environment. The platform utilizes Machine
Learning, Natural Language Processing, and intelligent optimization algorithms to automate content distribution,
optimize server resource allocation, and support evidence-based decision-making.
The findings indicate that the proposed platform may improve content delivery performance by supporting
more efficient server resource utilization, lower network latency, reduced operational costs, and greater economic
efficiency of the CDN infrastructure. The econometric results indicated an acceptable level of explanatory and
forecasting performance within the scope of the study, while the integrated forecasting module generated
optimistic, realistic, and pessimistic development scenarios through 2030. The results suggest that integrating
artificial intelligence with CDN management may contribute to improvements in infrastructure performance,
service quality, and strategic planning capabilities.
The proposed AI-CDN Economic Efficiency Platform represents an integrated digital solution that combines
artificial intelligence, econometric analysis, and real-time monitoring within a unified framework for intelligent
content placement and management. The platform provides both theoretical and practical contributions to
the development of AI-driven digital content management systems and supports the digital transformation of
modern information infrastructure.
References
Goodfellow, I. Deep Learning / I. Goodfellow, Y. Bengio, A. Courville. — Cambridge, MA : MIT Press, 2016.
— 800 p.
Gujarati, D. N. Basic Econometrics / D. N. Gujarati, D. C. Porter, M. Pal. — 6th Indian ed. — Chennai :
McGraw Hill Education, 2021. — XXIV, 910 p.
Jurafsky, D. Speech and Language Processing: An Introduction to Natural Language Processing,
Computational Linguistics, and Speech Recognition / D. Jurafsky, J. H. Martin. — 3rd ed., draft of January
, 2023. — Electronic text data. — Stanford University, 2023. — Accessed: 16.07.2026.
Kaur, D. Trustworthy Artificial Intelligence: A Review / D. Kaur, S. Uslu, K. J. Rittichier, A. Durresi // ACM
Computing Surveys. — 2022. — Vol. 55, no. 2. — Article 39. — P. 1–38. — DOI: 10.1145/3491209.
Mao, H. Resource Management with Deep Reinforcement Learning / H. Mao, M. Alizadeh, I. Menache, S.
Kandula // Proceedings of the 15th ACM Workshop on Hot Topics in Networks (HotNets ’16). — New York
: Association for Computing Machinery, 2016. — P. 50–56. — DOI: 10.1145/3005745.3005750.
Porter, M. E. How Smart, Connected Products Are Transforming Companies / M. E. Porter, J. E. Heppelmann
// Harvard Business Review. — 2015. — Vol. 93, no. 10. — P. 96–114.
Recommender Systems Handbook / ed. by F. Ricci, L. Rokach, B. Shapira. — 3rd ed. — New York :
Springer, 2022. — XI, 1060 p. — DOI: 10.1007/978-1-0716-2197-4.
Russell, S. Artificial Intelligence: A Modern Approach / S. Russell, P. Norvig. — 4th ed., Global ed. —
Harlow : Pearson, 2021.
Sarker, I. H. AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation,
Intelligent and Smart Systems / I. H. Sarker // SN Computer Science. — 2022. — Vol. 3. — Article 158. —
DOI: 10.1007/s42979-022-01043-x.
Zhang, C. Deep Learning in Mobile and Wireless Networking: A Survey / C. Zhang, P. Patras, H. Haddadi //
IEEE Communications Surveys & Tutorials. — 2019. — Vol. 21, no. 3. — P. 2224–2287. — DOI: 10.1109/
COMST.2019.2904897.
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