  As modern software systems continue to grow in terms of complexity and
volume, anomaly detection on multivariate monitoring metrics, which profile
systems' health status, becomes more and more critical and challenging. In
particular, the dependency between different metrics and their historical
patterns plays a critical role in pursuing prompt and accurate anomaly
detection. Existing approaches fall short of industrial needs for being unable
to capture such information efficiently. To fill this significant gap, in this
paper, we propose CMAnomaly, an anomaly detection framework on multivariate
monitoring metrics based on collaborative machine. The proposed collaborative
machine is a mechanism to capture the pairwise interactions along with feature
and temporal dimensions with linear time complexity. Cost-effective models can
then be employed to leverage both the dependency between monitoring metrics and
their historical patterns for anomaly detection. The proposed framework is
extensively evaluated with both public data and industrial data collected from
a large-scale online service system of Huawei Cloud. The experimental results
demonstrate that compared with state-of-the-art baseline models, CMAnomaly
achieves an average F1 score of 0.9494, outperforming baselines by 6.77% to
10.68%, and runs 10X to 20X faster. Furthermore, we also share our experience
of deploying CMAnomaly in Huawei Cloud.
