- Observability: other versions:
- What is Elastic Observability?
- What’s new in 8.10
- Get started
- Observability AI Assistant
- Application performance monitoring (APM)
- Logs
- Infrastructure monitoring
- AWS monitoring
- Synthetic monitoring
- Get started
- Scripting browser monitors
- Configure lightweight monitors
- Manage monitors
- Work with params and secrets
- Analyze monitor data
- Monitor resources on private networks
- Use the CLI
- Configure projects
- Configure Synthetics settings
- Grant users access to secured resources
- Manage data retention
- Use Synthetics with traffic filters
- Migrate from the Elastic Synthetics integration
- Scale and architect a deployment
- Synthetics support matrix
- Synthetics Encryption and Security
- Troubleshooting
- Uptime monitoring
- Real user monitoring
- Universal Profiling
- Alerting
- Service-level objectives (SLOs) (beta)
- Cases
- CI/CD observability
- Troubleshooting
- Fields reference
- Tutorials
- Monitor Amazon Web Services (AWS) with Elastic Agent
- Monitor Amazon Web Services (AWS) with Beats
- Monitor Google Cloud Platform
- Monitor a Java application
- Monitor Kubernetes
- Monitor Microsoft Azure with Elastic Agent
- Monitor Microsoft Azure with the Azure Native ISV Service
- Monitor Microsoft Azure with Beats
IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
Inspect uptime duration anomalies
editInspect uptime duration anomalies
editEach monitor location is modeled, and when a monitor runs for an unusual amount of time, at a particular time, an anomaly is recorded and highlighted on the Monitor duration chart.
Enable uptime duration anomaly detection
editCreate a machine learning job to detect anomalous monitor duration rates automatically.
- To access this page, go to Observability > Uptime > Monitors, and then click a monitor to view its the details.
-
In the Monitor duration panel, click Enable anomaly detection.
If anomaly detection is already enabled, click Anomaly detection and select to view duration anomalies directly in the Machine Learning app, enable an anomaly rule, or disable the anomaly detection.
- You are prompted to create a response duration anomaly rule for the machine learning job which will carry out the analysis, and you can configure which severity level to create the rule for.
When an anomaly is detected, the duration is displayed on the Monitor duration chart, along with the duration times. The colors represent the criticality of the anomaly: red (critical) and yellow (minor).
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