# Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight

arXiv: https://arxiv.org/abs/2407.08694 · HTML: https://mlsanity.com/paper/2407.08694/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs](https://mlsanity.com/paper/2402.15627/) (2024)
- [Greedy Relaxations of the Sparsest Permutation Algorithm](https://mlsanity.com/paper/2206.05421/) (2022)
- [Causal Discovery with Language Models as Imperfect Experts](https://mlsanity.com/paper/2307.02390/) (2023)
- [Exploring LLM-based Agents for Root Cause Analysis](https://mlsanity.com/paper/2403.04123/) (2024)
- [Understanding Causality with Large Language Models: Feasibility and Opportunities](https://mlsanity.com/paper/2304.05524/) (2023)
- [Efficient Causal Graph Discovery Using Large Language Models](https://mlsanity.com/paper/2402.01207/) (2024)
- [FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices](https://mlsanity.com/paper/2008.08509/) (2020)
- [DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models](https://mlsanity.com/paper/2206.06821/) (2022)
- [Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach](https://mlsanity.com/paper/2402.01454/) (2024)
- [Case Studies of Causal Discovery from IT Monitoring Time Series](https://mlsanity.com/paper/2307.15678/) (2023)
- [Discovering and Reasoning of Causality in the Hidden World with Large Language Models](https://mlsanity.com/paper/2402.03941/) (2024)
- [Applying Large Language Models for Causal Structure Learning in Non Small Cell Lung Cancer](https://mlsanity.com/paper/2311.07191/) (2023)
- [PyRCA: A Library for Metric-based Root Cause Analysis](https://mlsanity.com/paper/2306.11417/) (2023)

## Built on by (most cited first, 12 of 12)

- [Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models](https://mlsanity.com/paper/2501.14170/) (2025)
- [A Survey of AIOps in the Era of Large Language Models](https://mlsanity.com/paper/2507.12472/) (2025)
- [Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices](https://mlsanity.com/paper/2601.02732/) (2026)
- [Hypothesize-Then-Verify: Speculative Root Cause Analysis for Microservices with Pathwise Parallelism](https://mlsanity.com/paper/2601.02736/) (2026)
- [Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training](https://mlsanity.com/paper/2509.03018/) (2025)
- [GALA: Can Graph-Augmented Large Language Model Agentic Workflows Elevate Root Cause Analysis?](https://mlsanity.com/paper/2508.12472/) (2025)
- [StepFly: Agentic Troubleshooting Guide Automation for Incident Diagnosis](https://mlsanity.com/paper/2510.10074/) (2025)
- [Root Cause Analysis of Outliers in Unknown Cyclic Graphs](https://mlsanity.com/paper/2510.06995/) (2025)
- [Towards In-Depth Root Cause Localization for Microservices with Multi-Agent Recursion-of-Thought](https://mlsanity.com/paper/2605.14866/) (2026)
- [TRUST: A Framework for Decentralized AI Service v.0.1](https://mlsanity.com/paper/2604.27132/) (2026)
- [EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis](https://mlsanity.com/paper/2607.27290/) (2026)
- [Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition](https://mlsanity.com/paper/2609.36686/) (2026)

## Abstract (arXiv)

Runtime failure and performance degradation is commonplace in modern cloud systems. For cloud providers, automatically determining the root cause of incidents is paramount to ensuring high reliability and availability as prompt fault localization can enable faster diagnosis and triage for timely resolution. A compelling solution explored in recent work is causal reasoning using causal graphs to capture relationships between varied cloud system performance metrics. To be effective, however, systems developers must correctly define the causal graph of their system, which is a time-consuming, brittle, and challenging task that increases in difficulty for large and dynamic systems and requires domain expertise. Alternatively, automated data-driven approaches have limited efficacy for cloud systems due to the inherent rarity of incidents. In this work, we present Atlas, a novel approach to automatically synthesizing causal graphs for cloud systems. Atlas leverages large language models (LLMs) to generate causal graphs using system documentation, telemetry, and deployment feedback. Atlas is complementary to data-driven causal discovery techniques, and we further enhance Atlas with a data-driven validation step. We evaluate Atlas across a range of fault localization scenarios and demonstrate that Atlas is capable of generating causal graphs in a scalable and generalizable manner, with performance that far surpasses that of data-driven algorithms and is commensurate to the ground-truth baseline.