mlsanityData to 14 Oct 2026

AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

arXiv 2304.04661 ↗First posted 10 Apr 2023cs.LG cs.DC cs.SEcited by 15

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Abstract (arXiv)

Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availability. There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency. Here we provide a review of the AIOps vision, trends challenges and opportunities, specifically focusing on the underlying AI techniques. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions. We discuss the problem formulation for each task, and then present a taxonomy of techniques to solve these problems. We also identify relatively under explored topics, especially those that could significantly benefit from advances in AI literature. We also provide insights into the trends in this field, and what are the key investment opportunities.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 57 of its 226 references that are in the map. First posted on arXiv 10 Apr 2023.

Builds on

57 of 226 references.

References · 57

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38 more
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50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 1; 2025: 5; 2026: 5.

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  1. 2023RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Modelscited by 65
  2. 2023Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domaincited by 23
  3. 2025Agentic Systems: A Guide to Transforming Industries with Vertical AI Agentscited by 20
  4. 2025Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Modelscited by 19
  5. 2025TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systemscited by 13
  6. 2026Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safetycited by 2
  7. 2023AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Studycited by 1
  8. 2024KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networkscited by 1
  9. 2025Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracycited by 1
  10. 2026EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systemscited by 1
  11. 2026Crypto x AI, AI x Crypto: A Surveycited by 1
  12. 2023AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarioscited by 0
3 more
  1. 2025Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changescited by 0
  2. 2026Agent System Operations: Categorization, Challenges, and Future Directionscited by 0
  3. 2026Anomaly Detection and Root Cause Analysis for Microservice Systemscited by 0