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

Authors

  • Prasanna Kumar Natta Sacred Heart University, USA Author

DOI:

https://doi.org/10.15680/IJCTECE.2025.0802010

Keywords:

AI-driven cloud orchestration, multi-cloud resource optimization, predictive scaling, cloud security automation, self-healing infrastructure

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.

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Published

2025-04-22

How to Cite

AI-Powered Cloud Orchestration: Automating Multi- Cloud & Hybrid Cloud Workloads. (2025). International Journal of Computer Technology and Electronics Communication, 8(2), 10409-10419. https://doi.org/10.15680/IJCTECE.2025.0802010