AI-Powered Autonomic Cloud Management: Challenges and Future Directions

Authors

  • Arjun Patil Data Scientist, Zensar Technologies, India Author

DOI:

https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P101

Keywords:

Autonomic Computing, Cloud Management, Artificial Intelligence, Resource Allocation, Quality of Service, Security Challenges, Future Directions

Abstract

The integration of artificial intelligence (AI) into autonomic cloud management systems represents a transformative shift in how cloud resources are managed, optimized, and secured. Autonomic computing leverages AI to create self-managing systems that can autonomously handle resource allocation, fault detection, and security threats, thereby enhancing operational efficiency and reliability. However, the implementation of these systems faces several challenges, including complexity in resource management across diverse environments, maintaining quality of service (QoS), and ensuring seamless integration with existing infrastructures. Future directions for research and development in this field should focus on enhancing the adaptability of autonomic systems to dynamic workloads, improving cross-cloud management capabilities, and exploring the potential of quantum computing to augment decision-making processes. Additionally, addressing the security implications of AI-driven automation will be crucial as organizations increasingly rely on these technologies for critical operations. By overcoming these challenges, AI-powered autonomic cloud management can significantly improve the scalability, resilience, and cost-effectiveness of cloud services, paving the way for advanced applications in various sectors

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Published

2022-07-10

Issue

Vol. 3 No. 3 (2022)

Section

Articles

How to Cite

1.
Patil A. AI-Powered Autonomic Cloud Management: Challenges and Future Directions. IJAIDSML [Internet]. 2022 Jul. 10 [cited 2025 Sep. 14];3(3):1-11. Available from: https://ijaidsml.org/index.php/ijaidsml/article/view/36