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

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arxiv 2304.04661 v1 pith:URGYKKMI submitted 2023-04-10 cs.LG cs.DCcs.SE

classification cs.LGcs.DCcs.SE
keywords aiopsoperationschallengesopportunitiesclouddatadiscussinsights
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FixItFlow: Automated Troubleshooting Guide Generation from Cloud Incidents

    cs.CL 2026-05 reject novelty 5.0 of 10

    An LLM pipeline generates structured troubleshooting guides from incident comments under a character-exact command-grounding rule; a 26-engineer survey shows clarity but low factual-accuracy, satisfaction, and adoptio...

  2. Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs

    cs.AI 2025-05 reject novelty 5.0 of 10

    ARCA uses multi-modal retrieval-augmented generation with logs, telemetry, and descriptions to achieve 92% triage and 72% mitigation-plan accuracy on synthetic cloud-incident reports.

  3. A Survey of AIOps in the Era of Large Language Models

    cs.SE 2025-06 conditional novelty 3.0 of 10

    A systematic survey that categorizes LLM-based AIOps research into four dimensions: data sources, tasks, methods, and evaluation, claiming to be the first comprehensive such overview.

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