  As software systems grow increasingly intricate, Artificial Intelligence for
IT Operations (AIOps) methods have been widely used in software system failure
management to ensure the high availability and reliability of large-scale
distributed software systems. However, these methods still face several
challenges, such as lack of cross-platform generality and cross-task
flexibility. Fortunately, recent advancements in large language models (LLMs)
can significantly address these challenges, and many approaches have already
been proposed to explore this field. However, there is currently no
comprehensive survey that discusses the differences between LLM-based AIOps and
traditional AIOps methods. Therefore, this paper presents a comprehensive
survey of AIOps technology for failure management in the LLM era. It includes a
detailed definition of AIOps tasks for failure management, the data sources for
AIOps, and the LLM-based approaches adopted for AIOps. Additionally, this
survey explores the AIOps subtasks, the specific LLM-based approaches suitable
for different AIOps subtasks, and the challenges and future directions of the
domain, aiming to further its development and application.
