Optimizing Cloud Resource Intelligence Using AI-Driven Graph Analytics and Predictive Decision Modeling
Abstract
Modern multi-cloud environments present unprecedented operational complexity due to dynamic provisioning, heterogeneous workloads, and interconnected service dependencies. Traditional monitoring tools often fail to capture complex structural interdependencies and rapid state transitions, leading to sub-optimal resource allocation, inflated cloud expenditure, and performance bottlenecks. This paper proposes a comprehensive framework titled "Optimizing Cloud Resource Intelligence Using AI-Driven Graph Analytics and Predictive Decision Modeling." By modeling cloud infrastructure as dynamic directed property graphs, the framework leverages Graph Neural Networks (GNNs) and graph centrality metrics to map hidden topology, trace cascading failure pathways, and identify critical resource nodes. Integrated with predictive decision modeling—specifically recurrent sequence models and deep reinforcement learning (DRL)—the architecture forecasts future resource demands, automates scaling actions, and balances performance against cost. Empirical evaluations across large-scale simulated and real-world microservices clusters demonstrate that this hybrid approach improves resource utilization by up to 28%, reduces over-provisioning expenses by 34%, and mitigates performance degradation incidents by 42% compared to conventional threshold-based systems. Ultimately, this research provides a scalable, intelligent blueprint for autonomous cloud management, ensuring robust performance guarantees while minimizing operational expenditure in dynamic enterprise cloud environments.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 11 No. 3 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
21056-21063 |
Published |
May 19, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr.M.Sunil Kumar (%2026). Optimizing Cloud Resource Intelligence Using AI-Driven Graph Analytics and Predictive Decision Modeling. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 3 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21056-21063. https://doi.org/10.15662/ijetemr.2026.1103007 |
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