International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | CloudIQ: AI-Enhanced Infrastructure for High Availability and Resilience |
|---|---|
| ABSTRACT | This project proposes an AI-driven cloud infrastructure system designed to enhance reliability, security, and efficiency by autonomously detecting and resolving issues. The system integrates AI agents, machine learning models, and cloud computing to monitor infrastructure, identify faults, and implement real-time self-repair mechanisms. It employs predictive AI models for failure anticipation, real-time patching for security vulnerabilities, and automated scaling to handle traffic surges. The backend leverages AWS services (e.g., CloudWatch, CloudTrail) for monitoring and uses containerized deployment via Docker or Kubernetes for scalability. By automating issue detection, patching, and scaling, the system reduces downtime, enhances resilience, and minimizes human intervention, making cloud operations more robust and efficient. |
| AUTHOR | ANUSHA A, KARTHIK S, MOHAN B, MONIKA B S, SYED MUSAIB Assistant Professor, Dept. of CSE., Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India Dept. of CSE., Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312014 |
| pdf/14_CloudIQ AI-Enhanced Infrastructure for High Availability and Resilience.pdf | |
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