SLAs
Features
Germain UX offers different types of SLAs (Service Level Agreements) that can be applied to various KPIs, whether they are preconfigured or custom, and whether they are related to business or technical metrics. Here are the different types of SLAs available in Germain UX:
Fact-Based SLA
This type of SLA evaluates individual data points or facts. It allows for the evaluation of specific metrics against predefined thresholds or targets.
Statistical SLA
Statistical SLAs evaluate collections of data points or facts. Several types of statistical SLAs are available:
Aggregate SLA
It evaluates a specific period of time and compares it to a constant threshold. This type of SLA is useful for monitoring trends or performance over a specified time frame.
Color Percentile SLA
This SLA evaluates a period of time and compares the percentile of data points in the GREEN range to a constant threshold. It helps identify if performance is within an acceptable range.
Percent Change SLA
It evaluates a period of time and compares it to a prior period, measuring the percentage change. This SLA is useful for monitoring performance improvements or regressions over time.
Predictive SLA
This SLA creates predictions based on past data points or historical trends. It helps in forecasting future performance or identifying potential issues based on predictive analytics.
Smart Insight SLA
This SLA automatically identifies trends that are considered abnormal or noteworthy. It evaluates a business period of time (e.g., year-end, quarter-end, month-end, specific weekdays) and compares it to other similar business periods. Smart Insight SLAs provide alerts or notifications for significant deviations or insights.
These SLAs help organizations set performance benchmarks, monitor adherence to targets or thresholds, and gain insights into trends and predictive analytics. By utilizing different types of SLAs, Germain UX enables users to evaluate and track KPIs effectively, both in real-time and historically.
Examples of smart insights in Germain UX include predictions about disk usage exceeding a certain threshold, changes in user errors volume compared to previous periods, sales forecast accuracy predictions, and predictions of visitor increases in eCommerce by month-end.
In summary, Germain UX provides a range of SLA types, including fact-based and statistical SLAs, to monitor and evaluate KPIs. These SLAs enable organizations to set performance targets, identify trends, and gain predictive insights. The Smart Insight SLAs offer automated detection of abnormal trends, providing valuable information for proactive monitoring and decision-making.
Configuration
Via Wizard
Go to Germain Workspace > Left Menu > Wizard > SLA
Via SLA Screen
Go to Germain Workspace > Left Menu > Analytics > SLA
Predictive SLA for your KPI
How-to enable Predictive SLA for your KPI
Log on to Germain UX > Left Menu > Wizards
Select the KPI for which you want to configure a predictive SLA (selection is disabled if you came from KPI View)
Select the “Predictive-Curve” SLA type
Specify the details of the value and timeframe you want to predict
Select the alert template and action(s) you want the system to execute if the previous SLA check fails
How to enable Smart Insights for your KPI
Germain UX > Left Menu > Automation > Alerts > analytical-sla-alert
Germain UX > Left Menu > Analytics > SLA > Smart Insights SLA - Day
In addition to receiving individual email alert whenever a kpi is exceeding SLA, you can receive a summary email once per day that summarizes all the kpi that have exceeded SLA that day.
Germain UX’ Smart Insight engine can be enabled for any of the KPIs that are active in your system. KPIs can be Technology, Process or User Behavior-specific.
Example: Smart Insights Summary Email that is sent once per day.
Attributes
FactBasedSLA Context
The context object available from a FactBasedSLA has the following structure:
sla (Object)
threshold (Object)
kpi (Object)
fact (Object)
value (Double)
resource (String)
violation (boolean)
violationCount (int)
More info on the structure of these objects is available below. For example, if you have a LocalActionExecutor that executes a command when an SLA is breached, you can reference the variables above inside your command or arguments follows:
${context.fact.system.hostname}
${context.kpi.name}
Statistical SLA Context
The context object available from a StatisticalSLA has the following structure:
sla (Object)
kpi (Object)
value (Double)
resource (String)
values (Map<String, Double>)
timestamp (Object)
result (Object)
More info on the structure of these objects is available below. These variables can be accessed in the same way as above.
SLA Object
The above objects have the following structure:
FactBasedSLA:
name (String)
color (Object)
constantValue (String) – this is the SLA threshold
AggregateSLA:
operator (Object)
thresholdValue (Double)
periodCount (Integer)
periodGranularity (Object)
rank (Double)
ColorPercentileSLA:
operator (Object)
thresholdValue (Double)
periodCount (Integer)
periodGranularity (Object)
PercentChangeSLA:
operator (Object)
thresholdValue (Double)
periodCount (Integer)
periodGranularity (Object)
baselineGranularity (Object)
PredictiveSLA:
operator (Object)
thresholdValue (Double)
periodCount (Integer)
periodGranularity (Object)
dataGranularity (Object)
extrapolateValue (boolean)
Threshold Object:
name (String)
color (Color)
thresholdValue (Double)
thresholdExpression (String)
KPI Object
Name (String)
Description (String)
Fact Object
Result Object
CurrentInterval (Object)
start (Object)
end (Object)
baselineInterval (Object)
start (Object)
end (Object)
measure (String)
unit (String)
Service: Analytics
Feature Availability: 8.6.0 or later