If my systems are performing poorly, won Netuitive model the bad behavior?
In theory, yes. However, Netuitive’s Behavior Analysis Engine has a built-in fast learning algorithm that detects abnormal change and can accelerate the pace at which environmental changes are learned. Bottom line: if detected problems are not addressed in a timely way, it can unlearn as fast as it learned a bad behavior. Furthermore, Netuitive has complemented its purely mathematical approach by a set of user-defined policies and domain-specific heuristics and rules that prevent leaving a bad situation unalarmed. For instance, you can define a policy threshold forcing a deviation if the CPU Utilization exceeds a certain value (e.g. 95%), even if there is no statistical ground for that (e.g. low variability of CPU Utilization near the 100% ceiling). Similarly, you can define a policy filter that cancels any statistical deviation that happens in a considered-safe zone (e.g. below 30% for CPU Utilization), thus reducing the alarming noise.
