AI-Powered Cloud Orchestration: Automating Multi-Cloud & Hybrid Cloud Workloads

AI-powered cloud orchestration revolutionizes how enterprises manage and optimize their multi-cloud and hybrid cloud environments. Integrating artificial intelligence into cloud management addresses complexity, manual intervention, and reactive problem-solving challenges that plague traditional orchestration methods. By implementing intelligent algorithms for resource allocation, workload balancing, predictive scaling, security enhancement, and self-healing capabilities, organizations can transform their cloud operations from manually-defined workflows to autonomous systems capable of continuous optimization. These advanced orchestration technologies enable dynamic resource distribution based on usage patterns and forecasted demand while simultaneously identifying cost-saving opportunities through workload consolidation and intelligent scheduling. Security frameworks are significantly strengthened through anomaly detection, predictive threat intelligence, and adaptive access control policies that evolve with changing organizational needs. Perhaps most transformative is the ability of self-healing infrastructure to automatically detect, diagnose, and remediate issues before they cause service disruptions, dramatically reducing the operational burden on technical teams and allowing them to focus on innovation rather than troubleshooting. This technological shift represents a fundamental evolution in cloud management, offering enterprises unprecedented efficiency, reliability, and cost optimization across their distributed computing environments.

Authors

Publication Details

Journal
European Journal of Computer Science and Information Technology
Published
2025-03-15
DOI
https://doi.org/10.37745/ejcsit.2013/vol13n8138147
Citations
1
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
3.25
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article

AI-Powered Cloud Orchestration: Automating Multi-Cloud & Hybrid Cloud Workloads

Prasanna Kumar Natta
1 citations
European Journal of Computer Science and Information Technology
Cloud Computing and Resource Management
3.25
article

AI-Powered Cloud Orchestration: Automating Multi-Cloud & Hybrid Cloud Workloads

Prasanna Kumar Natta
article en
1 citations

Abstract

AI-powered cloud orchestration revolutionizes how enterprises manage and optimize their multi-cloud and hybrid cloud environments. Integrating artificial intelligence into cloud management addresses complexity, manual intervention, and reactive problem-solving challenges that plague traditional orchestration methods. By implementing intelligent algorithms for resource allocation, workload balancing, predictive scaling, security enhancement, and self-healing capabilities, organizations can transform their cloud operations from manually-defined workflows to autonomous systems capable of continuous optimization. These advanced orchestration technologies enable dynamic resource distribution based on usage patterns and forecasted demand while simultaneously identifying cost-saving opportunities through workload consolidation and intelligent scheduling. Security frameworks are significantly strengthened through anomaly detection, predictive threat intelligence, and adaptive access control policies that evolve with changing organizational needs. Perhaps most transformative is the ability of self-healing infrastructure to automatically detect, diagnose, and remediate issues before they cause service disruptions, dramatically reducing the operational burden on technical teams and allowing them to focus on innovation rather than troubleshooting. This technological shift represents a fundamental evolution in cloud management, offering enterprises unprecedented efficiency, reliability, and cost optimization across their distributed computing environments.

European Journal of Computer Science and Information TechnologyVol. 13(8)
Openalex Percentile: Top 9%
Cloud Computing and Resource Management
3.25
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AI-Powered Cloud Orchestration: Automating Multi-Cloud & Hybrid Cloud Workloads — Prasanna Kumar Natta · European Journal of Computer Science and Information Technology (2025) | TGRS Research Map | TGRS