The data normalization and topology discovery should be seen as the pre-processing and context support for the anomaly engine.
The anomaly engine learns the behavior of your business based on thousands of performance metrics and identifies anomalies that impact your business.
Anomaly Detection is a fundamental requirement to succeed in IT Operations as the IT complexities keep increasing.
In this e-book, we deep dive into the key components, variants, fundamental differences between approaches, and conclude with what you need to consider before implementing Anomaly Detection.
AIMS automatically builds and continuously updates normal behavior patterns for each of your performance metrics.
This allows dynamically setting alerting thresholds based on the actual cyclical behavior of your business - represented by thousands to millions of metrics. These patterns represent a high-resolution digital DNA of your business and allow you to detect deviations in real-time.
Anomalies are triggered based on a set of criteria including relative deviation vs expected behavior, a climb rate of behavior, how fast the situation escalates, and the number of metrics that show correlated deviations.
The Anomaly shows each metric deviation, the system impacted, the time it started, the relative deviation vs expected behavior which allows you to easily understand the cause and impact.
The Anomaly Timeline view allows you to understand the sequence of deviations occurring.
Anomaly Timeline allows you to easily identify the originating deviation and the follow-on consequences and escalation.
AIMS' modern IT monitoring solution leverages machine learning and AI to automate installation, configuration, insights and alerting while delivering time-to-value in hours and days - not weeks and months.