  In large-scale online services, crucial metrics, a.k.a., key performance
indicators (KPIs), are monitored periodically to check their running statuses.
Generally, KPIs are aggregated along multiple dimensions and derived by complex
calculations among fundamental metrics from the raw data. Once abnormal KPI
values are observed, root cause analysis (RCA) can be applied to identify the
reasons for anomalies, so that we can troubleshoot quickly. Recently, several
automatic RCA techniques were proposed to localize the related dimensions (or a
combination of dimensions) to explain the anomalies. However, their analyses
are limited to the data on the abnormal metric and ignore the data of other
metrics which may be also related to the anomalies, leading to imprecise or
even incorrect root causes. To this end, we propose a cross-metric
multi-dimensional root cause analysis method, named CMMD, which consists of two
key components: 1) relationship modeling, which utilizes graph neural network
(GNN) to model the unknown complex calculation among metrics and aggregation
function among dimensions from historical data; 2) root cause localization,
which adopts the genetic algorithm to efficiently and effectively dive into the
raw data and localize the abnormal dimension(s) once the KPI anomalies are
detected. Experiments on synthetic datasets, public datasets and online
production environment demonstrate the superiority of our proposed CMMD method
compared with baselines. Currently, CMMD is running as an online service in
Microsoft Azure.
