Model predictions are compared with the observed telemetry. Any deviations that signal an anomalous condition are recorded as an Alert. This predictive modeling approach determines when the container is out of the correct operational range -- which it learned from demand requests, resource consumption, incoming flows as well as generated requests or flows -- without relying on manual thresholds or misleading statistical outliers.
This predictive analysis is done in parallel for all elements (e.g., containers, nodes, etc) in real-time at the sampling rate of the telemetry. Predictive means decreased MTTD or even avoidance of a brownout or outage since we are not waiting for an SLO threshold to be breached, for which time it is too late to act! Knowing that things are not OK before going off the rails gives that edge to Ops to take preventive action.
In addition to the anomalous scenarios, the model can identify differences between the behaviors of changed containers. OpsCruise can compare blue-green versions using these models and determine whether the new deployment will work, significantly improving agility.