• application performance management case studies

Automated Threshold Discovery (ATD) and Dynamic Alerts

ABL automatically discovers and sets the trigger levels for alerting and eliminates the need for users or administrators to do this work. The alerting trigger settings result from the identification, by AppsOne analytics, of Application Usage Patterns.

AppsOne analytics correlates those patterns to underlying infrastructure component behavior (e.g., servers, databases, hosts) -- their performance KPIs. When load is high, alerting trigger levels are set high – for example when a batch job is running in parallel with other production end-user activity. When load is low, alerting trigger points are low. Unlike typical monitoring systems in the market today, these alerts are dynamic as the trigger levels change throughout the day based on what application usage pattern is active at any given time. In the Illustration below[this may need to say to the right based on where it ends up on the website], Application Usage Patterns are indicated in the top colored horizontal bar where each color represents a different AUP occurring over time (X axis of these charts). The lower colored view is showing the green trigger level and trigger range for expected bahvior of an infrastructure component supporting this application and these usage patterns. When the actual behavior of the components – represented here with the black line – is outside of one standard deviation of expected behavior (out of the green and into the yellow range), then the system alerts the client.