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 | Smart DevOps Monitoring System |
|---|---|
| ABSTRACT | In today’s digitally connected world, the reliability and uptime of IT infrastructure play a crucial role in the success of businesses. Any unplanned system downtime can lead to revenue loss, reduced productivity, and damaged customer trust. While several tools exist to monitor system health, most solutions are reactive—they alert only after an issue has already occurred. To bridge this gap, this project proposes a Smart DevOps Monitoring & Prediction System, which combines the strengths of open-source monitoring tools and AI-driven analytics to enable both real-time monitoring and predictive maintenance. In the current phase (Phase-1), the project establishes a robust foundation for real-time system monitoring using the Prometheus monitoring system and Windows Exporter, which collects machine-level metrics such as CPU usage, memory availability, and disk space from a Windows environment. These metrics are visualized using Grafana, which offers interactive dashboards that allow developers and system administrators to monitor system health at a glance. The system is capable of scraping and storing time-series metrics from servers, making them available for querying and visualization. The use of Prometheus's pull-based data collection and Grafana’s customizable dashboards ensures flexibility and scalability. This forms the basis for the next development phase where AI models—such as anomaly detection and failure prediction using Isolation Forest or LSTM—can be integrated to forecast failures and automatically trigger alerts or recovery mechanisms. By combining DevOps tools with AI, the Smart DevOps Monitoring & Prediction System aims to significantly reduce Mean Time to Detection (MTTD) and Mean Time to Recovery (MTTR), while enabling proactive system maintenance. This project has applications in cloud management, enterprise IT infrastructure, and datacentre operations—where even minutes of downtime can result in high operational costs. In the long run, this system can be deployed across organizations as a cost-effective and intelligent DevOps |
| AUTHOR | DR. MANU M N, PROF. PRARTHANA J V, PROF. GAYATHRI S Dept. of ISE, SJB Institute of Technology, Bengaluru, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407050 |
| pdf/50_Smart DevOps Monitoring System.pdf | |
| KEYWORDS | |
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