AI-Driven Self-Optimizing and Self-Replicating Cloud Architectures: Paving the Way for Autonomous, Efficient, and Scalable Cloud Systems

Author(s)	Subhasis Kundu
Country	United States
Abstract	This study explores the transformative potential of AI-powered self-optimizing and self-replicating cloud architectures in the realm of cloud computing. It introduces an innovative method that combines machine-learning algorithms with autonomous resource management to create cloud systems that are both highly efficient and scalable. The proposed architecture leverages AI to continuously enhance resource allocation, forecast workload trends, and dynamically modify system settings. This study also presents the idea of self-replicating cloud ecosystems that can autonomously expand or contract in response to demand. The experimental findings demonstrated notable enhancements in energy efficiency, resource utilization, and overall system performance. This study examines the implications of this technology for sustainable computing and the future of cloud infrastructure, as well as the potential challenges and areas for further investigation.
Keywords	AI-Driven Cloud, Self-Optimization, Self-Replication, Autonomous Systems, Resource Management, Scalability, Cloud Computing, Machine Learning, Predictive Analytics, Energy Efficiency
Field	Engineering
Published In	Volume 13, Issue 1, January-March 2022
Published On	2022-03-07
DOI	https://doi.org/10.71097/IJSAT.v13.i1.2816

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology