标题： 大型语言模型时代的AIOps综述 显示英文标题

标题： A Survey of AIOps in the Era of Large Language Models

摘要： 随着大型语言模型（LLMs）日益复杂和普及，它们在各种人工智能运维（AIOps）任务中的应用引起了广泛关注。然而，对LLMs在AIOps中的影响、潜力和局限性的全面理解仍处于初期阶段。为弥补这一差距，我们对LLM4AIOps进行了详细的调查，重点研究LLMs如何优化该领域的流程并提高结果。我们分析了2020年1月至2024年12月期间发表的183篇研究论文，以回答四个关键研究问题（RQs）。在RQ1中，我们考察了使用的各种故障数据源，包括基于先进LLM的处理技术对遗留数据的处理以及由LLMs启用的新数据源的整合。RQ2探讨了AIOps任务的演变，突出了新任务的出现以及这些任务的出版趋势。RQ3研究了用于解决AIOps挑战的各种基于LLM的方法。最后，RQ4回顾了用于评估集成LLM的AIOps方法的评价方法。基于我们的发现，我们讨论了最先进的进展和趋势，识别了现有研究中的差距，并提出了未来探索的有前景的方向。 显示英文摘要

摘要： As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered significant attention. However, a comprehensive understanding of the impact, potential, and limitations of LLMs in AIOps remains in its infancy. To address this gap, we conducted a detailed survey of LLM4AIOps, focusing on how LLMs can optimize processes and improve outcomes in this domain. We analyzed 183 research papers published between January 2020 and December 2024 to answer four key research questions (RQs). In RQ1, we examine the diverse failure data sources utilized, including advanced LLM-based processing techniques for legacy data and the incorporation of new data sources enabled by LLMs. RQ2 explores the evolution of AIOps tasks, highlighting the emergence of novel tasks and the publication trends across these tasks. RQ3 investigates the various LLM-based methods applied to address AIOps challenges. Finally, RQ4 reviews evaluation methodologies tailored to assess LLM-integrated AIOps approaches. Based on our findings, we discuss the state-of-the-art advancements and trends, identify gaps in existing research, and propose promising directions for future exploration.