Application Usage Patterns (AUP)
A core component of Appnomic’s approach to advanced analytics applied to IT is the ability to view application transactions and data center metrics from the end user "layer" of the application stack down into the lower layers of the application stack.
AppsOne’s ABL identifies naturally occurring combinations of end user transaction types and volumes of these transactions in short monitoring intervals. If an adjacent monitoring interval shows a similar, but slightly different mix. AppsOne® will identify if that combination is a statistically different pattern or essentially the same.
Each of these patterns is then correlated to “good” behavior of the infrastructure components that support the application. In deployment of the software, this occurs when the software is placed in learning mode.
When the application usage pattern is seen again, if performance metrics associated with the application stack components are out of alignment (above or below baseline trigger levels established by AppsOne), then AppsOne® knows there is a problem and provides an alert. The platform also simultaneously begins to capture diagnostic and forensic information to provide IT operations professionals (see Forensics).
Application Usage Pattern (AUP) Definition: The volumes of concurrently occurring transaction types that are contending for underlying infrastructure component resources. This workload is like a fingerprint that can be correlated to the same underlying infrastructure component performance. When there is a variance in the infrastructure component performance compared to what AppsOne® has identified as the expected behavior, this deviation results in an Early Warning Alert™ that enables IT operations professionals to pre-empt incidents from occurring.
AUPs have proven insightful for developers as well who can sometimes get visibility into unanticipated combinations of concurrent transaction types which can lead to application redesign and optimization. They also are used for application load testing to generate a more typical set of real production environment combinations of concurrent transaction types generating load within the application and on the data center components.