OnCommand Insight contains machine-learning anomaly detection analytics to identify the normal operating workload range for an application and identify when changes in performance are outside of expected levels. The application anomaly detection engine ingests performance metrics collected by Insight and identifies anomalies in the application infrastructure.
Anomaly analysis provides proactive visibility into the infrastructure supporting business critical applications and informs you of performance anomalies before they become service disruptions.
Anomalies are items, events, or observations that do not conform to an expected pattern or other items
in a dataset. You use anomaly detection to perform these tasks:
- Monitor selected critical applications
- Identify significant changes in the application infrastructure behavior
- Track an application through the entire stack and across multiple counters
- Diagnose the cause of periodically recurring events in an application workload
- Provide a more comprehensive set of information compared to data from performance counters using static threshold values
- Monitor in an improved reactive mode, with an end-to-end view of the topology and the ability to focus on the most anomalous behaviors.
The anomaly detection engine uses Insight data for application analysis. When monitoring is first started, up to 14 days of historical performance data can be ingested by the analysis engine. Data is collected for weeks or even months, providing more accurate data about a given resource. The data includes totals for the following counters:
Objects |
Counter |
VM |
Latency, IOPS |
Hypervisor |
CPU utilization, IOPS |
Edge port |
BB credit zero |
Storage Node |
Latency, utilization, IOPS |
Volume |
Latency, IOPS |
Internal volume |
Latency, IOPS |
Storage pool |
IOPS, utilization |