Basic concepts

A few basic concepts are necessary when you use AIDA.

KPIs (Key Performance Indicators)

KPIs for HCL Workload Automation processes that are constantly monitored by AIDA. For example, the number of completed jobs in the current plan.

AIDA groups the KPIs in categories and sub-categories.

For more information about HCL Workload Automation KPIs managed by AIDA, see: KPIs for HCL Workload Automation.

Anomaly Source KPI

The KPI whose anomalous trend has triggered an alert.

For more information about how to analyze an anomaly source KPI, see: Analyzing an alert instance.

Correlated KPI

KPI correlated with the anomalous KPI. You can add one or more correlated KPIs to the anomaly data analysis.

For more information about how to add correlated KPIs to the anomaly data analysis, see: Analyzing an alert instance.

Metrics

The full set of measurements of a KPI.

Data Point

Each singular observation of a KPI.

Anomaly

Unexpected KPI data point.

AIDA detects an anomaly when a KPI falls outside the expected range of values which is statistically defined based on KPI historical data.

For more information about anomaly data analysis, see Analyzing an alert instance.

Alert

An alert is defined by a set of conditions (see Alert trigger). For example: 3 consecutive KPI data points that fall outside the expected range of values.

An alert definition is usually based on one KPI, but multiple KPIs can also be used.

For more information, see: Alert definitions.

Alert Instance

A single occurrence of an alert, given its definition. As AIDA continuously monitors KPIs, when an alert is triggered, a record is created into OpenSearch database with the alert instance information.

For more information about alert instances, see: Alert overview dashboard.

Alert Severity

For each detected anomaly, AIDA calculates its percent deviation from the interval estimation. When an alert is generated, given its definition, the alert severity is calculated as average of percent deviations of the anomalies that concur to the alert generation. Alert severity classification by severity is:

High, when the average of percent deviations is > 30
Medium, when the average of percent deviations falls in the interval 20-30
Low, when the average of percent deviations is < 20

Anomaly Bounds

The upper and lower bounds of the expected range of values for a KPI.

Alert Trigger

Set of conditions that define an alert. For example: 3 consecutive KPI data points that fall outside the expected range of values.

When triggering conditions are satisfied, a new alert instance is created inside OpenSearch database.

Alerts are notified on the Workload Dashboard or via email.

For more information about receiving alert notifications, see: Receiving alert notifications.

Anomaly %

The percentage of observed KPI data points that fall outside the expected range of values in the reference time interval:

< 6 : Low
6-10: Medium
>10: High

A KPI trend can show some anomalies, however an alert might not be issued if the trigger condition is not met.

Anomaly Data Analysis

Anomaly Data Analysis is part of AIDA User Interface. When anomalies in a KPI trend generate an alert, you can compare the anomalous trend with trends in one or more different time intervals. You can also add correlated KPIs to the anomaly data analysis to find root causes faster.

For more information about anomaly data analysis, see Analyzing an alert instance.

Alert Definition Details

Alert Definition Details provides detailed information about an alert definition, its status, and history.

For more information, see: Alert definition details.

Alert History

Calendar graph showing previous alert instances and related severity.

For more information, see: Alert definition details.

Periodicity

The time interval whose anomalies must be considered to detect an alert (for example: the last hour, or the last 10 minutes). This parameter is included in the Alerts Definition json file received by HCL Workload Automation and cannot be changed by users.

Timerange

How often KPIs are checked to detect anomalies. AIDA Orchestrator checks KPIs once a day. This parameter is not customizable.

Special Days

Special days are days on which a KPI trend is affected by seasonality factors such as national holidays, vacation, business cycles, recurring events. To avoid false positive alerts, the special days are included in AIDA prediction model with a higher tolerance level than the standard days.

For more information, see: Managing special days.