| Project Overview | This project direction follows recent AIOps and LLM-agent research for cloud and DevOps automation. The work can process logs, metrics, build failures, runbooks, or cloud usage data to support monitoring, incident triage, and operational decision-making. The reference paper, 'AIOpsLab: Building AI Agents for Autonomous Clouds', provides the academic base for the topic. Instead of copying the paper abstract directly, this page keeps the same research intent in a safe paraphrased form: the system applies AI to logs, metrics, incidents, and cloud operations to detect anomalies and support incident response. The final student implementation can include dataset preparation, model/API integration, dashboard or app interface, result explanation, and a short documentation-ready workflow. |
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| Research Paper Title | AIOpsLab: Building AI Agents for Autonomous Clouds |
| Research Paper / PDF Link | Open Paper / PDF |
| Year | 2024 |
| Project Area | Cloud Computing & DevOps |
| Project Type | Cloud Project |
| Required Tools / Software | Docker, GitHub Actions, Python, Flask/FastAPI, Prometheus/Grafana, Cloud APIs, LLM API |
| Main Features / Working Principle | Collect or upload relevant data, preprocess it, apply an AI/ML/LLM/RAG/software workflow, and present the result through a dashboard or application interface for Cloud Computing & DevOps. |
| Expected Output | A working B.Tech project prototype for Cloud Computing & DevOps with input, processing, result display, and explanation/report sections. |
| Possible Add-ons | Admin panel, PDF report export, model comparison, source citations, login system, WhatsApp help button, and deployment on cloud/hosting. |
| Get Help | Get Help on WhatsApp
Message: Hi FE, I need help with "Cloud Cost Optimization Recommendation System" in "Computer Science & Engineering" |
This B.Tech Computer Science & Engineering project resource connects a recent research direction with a practical implementation plan, tools, expected output, and possible extensions.
