  Recently, AIOps (Artificial Intelligence for IT Operations) has been well
studied in academia and industry to enable automated and effective software
service management. Plenty of efforts have been dedicated to AIOps, including
anomaly detection, root cause localization, incident management, etc. However,
most existing works are evaluated on private datasets, so their generality and
real performance cannot be guaranteed. The lack of public large-scale
real-world datasets has prevented researchers and engineers from enhancing the
development of AIOps. To tackle this dilemma, in this work, we introduce three
public real-world, large-scale datasets about AIOps, mainly aiming at KPI
anomaly detection, root cause localization on multi-dimensional data, and
failure discovery and diagnosis. More importantly, we held three competitions
in 2018/2019/2020 based on these datasets, attracting thousands of teams to
participate. In the future, we will continue to publish more datasets and hold
competitions to promote the development of AIOps further.
