High Significant Fault Detection in Azure Core Workload Insights
Pranay Lohia Laurent Boue Sharath Rangappa Vijay Agneeswaran
Azure Core workload insights have time-series data with different metric
units. Faults or Anomalies are observed in these time-series data owing to
faults observed with respect to metric name, resources region, dimensions, and
its dimension value associated with the data. For Azure Core, an important task
is to highlight faults or anomalies to the user on a dashboard that they can
perceive easily. The number of anomalies reported should be highly significant
and in a limited number, e.g., 5-20 anomalies reported per hour. The reported
anomalies will have significant user perception and high reconstruction error
in any time-series forecasting model. Hence, our task is to automatically
identify 'high significant anomalies' and their associated information for user
perception.