Open Access

The Convergence of Graph-Theoretic Architectures and Agentic Artificial Intelligence in Optimizing Multi-Cloud Ecosystems: A Comprehensive Analysis of Cost Dynamics and Resource Allocation

PDF

Dr. Alistair Sterling ¹ ,

⁴ Department of Computational Systems and Data Science, University of Melbourne, Australia

Abstract

The rapid evolution of cloud computing environments has transitioned from simple storage and compute provisioning to a complex, multi-layered ecosystem characterized by extreme volatility in demand and pricing. This research investigates the synthesis of graph-theoretic conceptual designs and agentic artificial intelligence (AI) to address the persistent challenges of resource allocation, cost optimization, and dynamic pricing within private and hybrid cloud infrastructures. By leveraging foundational principles of directed graphs and predictive analytics, this study explores how autonomous agents can navigate the "data lake" paradigm to provide real-time, cost-effective scaling solutions. The methodology focuses on a descriptive analysis of heuristic algorithms, reinforcement learning models, and deep learning architectures as proposed in contemporary literature. The findings suggest that the integration of AI as an epistemic technology fundamentally alters the scientific approach to cloud management, enabling private providers to reinvigorate their market position through adaptive pricing strategies and anomaly detection. This article provides an extensive theoretical elaboration on the shift from static resource management to a dynamic, AI-driven framework, highlighting the ethical implications and technical hurdles of implementing such systems in a globalized big data landscape.

Keywords

Agentic AI, Cloud Computing, Graph Theory, Dynamic Pricing

References

Al-Hakim L, Kusiak A, Mathew J (2000) A graph-theoretic approach to conceptual design with functional perspectives. Comput Aided Des 32(14):867–875. https://doi.org/10.1016/S0010-4485(00)00075-0

Alvarado, R.: AI as an epistemic technology. Sci. Eng. Ethics 29, 32 (2023). https://doi.org/10.1007/s11948-023-00451-3

Ashabi A, Sahibuddin SB, Haghighi MS (2020) Big data: current challenges and future scope. In: Proceedings of 10th Symposium on Computer Applications Industrial Electronics (ISCAIE 2020), IEEE, pp 131–134. https://doi.org/10.1109/ISCAIE47305.2020.9108826

Bang-Jensen J, Gutin GZ (2008) Digraphs: theory, algorithms and applications. Springer Science & Business Media

Banerjee, R., & Singh, M. (2019). Reinforcement Learning for Autoscaling in Cloud Computing. Proceedings of the International Conference on Artificial Intelligence in Cloud Systems, 120-134.

Belov V, Kosenkov AN, Nikulchev E (2021) Experimental Characteristics Study of Data Storage Formats for Data Marts Development within Data Lakes. Appl Sci 11(18):8651. https://doi.org/10.3390/app11188651

Chawla H, Khattar P (2020) Data Ingestion, Apress, Berkeley, pp 43–85. https://doi.org/10.1007/978-1-4842-6252-8_4

Cormen T, Leiserson C, Rivest R, Stein C (2009) Introduction to algorithms, 3rd ed. MIT Press, p 693

Gupta, K., & Patel, S. (2018). Anomaly Detection in Cloud Cost Management: An Unsupervised Learning Approach. International Journal of Big Data Analytics, 9(2), 102-115.

Kumar, R., & Sharma, P. (2022). Hybrid Cloud Optimization: Clustering and Decision Tree Approaches. Journal of Hybrid Computing Systems, 16(2), 178-192.

Mishra, R., & Sharma, A. (2016). Predictive Models for Optimizing Cloud Resource Allocation. Journal of Cloud Computing Research and Practice, 12(3), 45-56.

Patel, V., & Kumar, A. (2021). Deep Learning Applications for Cloud Cost Prediction and Optimization. Neural Networks in Cloud Infrastructure Research, 7(3), 78-94.

Resnik, D.B., Hosseini, M.: The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool. AI Ethics 5, 1499–1521 (2025). https://doi.org/10.1007/s43681-024-00493-8

Schmidt, E.: This is how AI will transform the way science gets done. MIT Technology Review, July 5, 2023. https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science/ (2023) Accessed 7 May 2025.

Singh, A., & Gupta, N. (2023). AI-Driven Tools for Real-Time Cloud Cost Management. Artificial Intelligence in Cloud Services, 5(1), 45-67.

Brijesh Tripathi. (2025). Dynamic Pricing in the Cloud Era: How Agentic AI Can Reinvigorate Private Cloud Providers. Utilitas Mathematica, 122(2), 1385–1394. Retrieved from https://utilitasmathematica.com/index.php/Index/article/view/2866

Wang, Y., & Li, T. (2017). Dynamic Pricing Adaptation Using Reinforcement Learning in Cloud Environments. IEEE Transactions on Cloud Computing, 15(4), 289-301.

Zhang, L., & Chen, H. (2020). Workload Distribution Optimization in Multi-Cloud Environments Using Predictive Analytics. ACM Cloud Computing Journal, 23(5), 412-428.

Most read articles by the same author(s)

<< < 8 9 10 11 12 13

International Journal of Next-Generation Engineering and Technology

The Convergence of Graph-Theoretic Architectures and Agentic Artificial Intelligence in Optimizing Multi-Cloud Ecosystems: A Comprehensive Analysis of Cost Dynamics and Resource Allocation

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles