Llm aiops guide
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Papers on LLMs for IT operations and AIOps research
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LLM for AIOps Guide
Overview
A curated collection of research on applying LLMs to IT Operations (AIOps) โ log analysis, anomaly detection, incident management, root cause analysis, and automated remediation. Tracks how foundation models are transforming traditional rule-based operations tooling into intelligent, adaptive systems. Relevant for CS researchers at the intersection of systems, NLP, and operations.
Research Areas
LLM for AIOps
โโโ Log Analysis
โ โโโ Log parsing (template extraction)
โ โโโ Anomaly detection (from log sequences)
โ โโโ Log summarization
โ โโโ Root cause from logs
โโโ Incident Management
โ โโโ Incident triage and routing
โ โโโ Severity classification
โ โโโ Similar incident retrieval
โ โโโ Resolution recommendation
โโโ Root Cause Analysis
โ โโโ Topology-aware diagnosis
โ โโโ Multi-signal correlation
โ โโโ Causal inference
โโโ Monitoring & Alerting
โ โโโ Metric anomaly detection
โ โโโ Alert correlation
โ โโโ Noise reduction
โ โโโ Capacity planning
โโโ Automated Remediation
โโโ Runbook generation
โโโ Script generation
โโโ Self-healing systems
โโโ Change impact analysis
Key Papers
| Paper | Year | Focus |
|---|---|---|
| LogPPT | 2023 | Few-shot log parsing with prompt tuning |
| OpsEval | 2024 | Benchmark for evaluating LLMs in AIOps |
| D-Bot | 2024 | LLM-based database diagnosis |
| RCAgent | 2024 | Agent for root cause analysis |
| LogAgent | 2024 | Autonomous log analysis agent |
Use Cases
- Literature tracking: Follow LLM-AIOps research evolution
- System design: Learn intelligent operations patterns
- Benchmark comparison: Evaluate AIOps approaches
- Research planning: Identify under-explored AIOps problems
- Industry applications: Bridge research to production AIOps