Autonomous Anomaly Detector (BETA)

This page describes StackState version 4.1.

The StackState 4.1 version range is End of Life (EOL) and no longer supported. We encourage customers still running the 4.1 version range to upgrade to a more recent release.

Go to the documentation for the latest StackState release.

What is the Autonomous Anomaly Detector StackPack?

The Autonomous Anomaly Detector add-on is in BETA.

Anomaly detection identifies incidents in your fast-changing IT environment and provides insights into their root cause. This directs the attention of IT operators to the root cause of incidents.

Installing the Autonomous Anomaly Detector StackPack will enable the Autonomous Anomaly Detector (AAD). The AAD analyzes metric streams in search of any anomalous behavior based on its past. Upon detecting an anomaly, the AAD will mark the stream under inspection with an annotation that is easily visible in the StackState interface. An Anomaly Event for the incident will also be generated and stored, this can be inspected at a later date on the Events Perspective.

The AAD requires zero configuration. It is fully autonomous in selecting the metric streams it will apply anomaly detection to and the appropriate machine learning algorithms to use for each.

How does the AAD decide what to work on?

The AAD scales to large environments by autonomously prioritizing metric streams based on its knowledge of the 4T data model and user feedback. Streams with the highest priority will be examined first. The prioritization of streams is computed by an algorithm that learns to maximize the probability of preventing an IT issue. To operate in large environments, attention must be allocated where it matters the most. The AAD achieves this based on its knowledge of streams that are intrinsically important (such as KPIs and SLAs), ongoing and historical issues, relations between streams and other relevant factors.

The stream selection algorithm works as follows:

You cannot directly control the stream selected, but you can steer the selection by starring Views and setting the priority of streams to high.

How fast are anomalies detected?

The AAD ensures that prioritized metric streams are checked for anomalies in a timely fashion. Anomalies occurring in the highest prioritized metric streams are detected within about 5 minutes.

How do I know what the AAD is working on?

The status UI of the AAD Kubernetes service provides various metrics and indicators, including details of what it is currently doing.


The AAD StackPack can only be installed within a Kubernetes setup. Please make sure that this is supported by your StackState installation.

If you are not sure that you have a Kubernetes setup or would you like to know more, contact StackState support.

Node sizing

A minimal deployment of the AAD Kubernetes service with the default options requires one of the following instance types:

  • Amazon EKS: 1 instance of type m4.xlarge

  • Azure AKS: 1 instance of type F4s v2 (Intel or AMD CPUs)

  • Self-hosted Kubernetes: 1 instance with 4 CPUs and 6 Gb memory

To handle more streams or to reduce detection latency, the service can be scaled. If you want to find out how to scale the service, contact StackState support.

The AAD Kubernetes service is stateless and survives restarts. It can be relocated on a different Kubernetes node or bounced. To take full advantage of this capability it is recommended to run the service on low cost AWS Spot Instances and Azure Low Priority VM.


Install the Autonomous Anomaly Detector (AAD) StackPack

Install the AAD StackPack from the StackPacks page in StackState.

To use the AAD StackPack, the AAD Kubernetes service must also be installed.

Install the Autonomous Anomaly Detector (AAD) Kubernetes service

After installing the AAD StackPack, install the AAD Kubernetes service.

1. Get access to

To be able to pull the Docker image, you will need access to Access credentials can be requested from StackState support.

2. Install Helm

helm repo add stackstate`

3. Get the latest AAD Kubernetes service

helm fetch stackstate/anomaly-detection

4. Configure AAD Kubernetes service

Create the file values.yaml file including the configuration described below and save it to disk:

  • image:

    • tag - the image version e.g 4.1.0-release

    • pullSecretUsername - the image registry username (from step 1)

  • stackstate:

    • instance - the StackState instance url. This must be a StackState internal url to keep traffic inside the Kubernetes network and namespace. e.g http://stackstate-server-headless:7070/ or http://<releasename>-stackstate-server-headless:7070/

  • ingress: - Ingress provides access to the technical interface of the AAD Kubernetes service, this is useful for troubleshooting. The example below shows how to configure an nginx-ingress controller. Setting up the controller itself is beyond the scope of this document. More information about how to set up Ingress can be found at:

    tag: <image tag>
    pullSecretUsername: <image registry username>

    # Stackstate instance URL
    instance: <stackstate instance url>

# status UI ingress (the configuration below is example for nginx ingress controller)
    enabled: true
    hostname: <domain name> # e.g.
    port: 8090              # status page will be available on
    annotations:        <domain name> # e.g. nginx nginx
        - host: <domain name>  # e.g.

Details of all configuration options are available in the anomaly-detection chart documentation.

helm show all stackstate/anomaly-detection

5. Install the AAD Kubernetes service

Run the command below, specifying the StackState namespace and the image registry password. Note that the AAD Kubernetes service must be installed in the same namespace as StackState.

helm upgrade anomaly-detector stackstate/anomaly-detection \
    --install \
    --namespace <stackstate-namespace> \
    --set image.pullSecretPassword=<image registry password>
    --values ./values.yaml

Upgrade the AAD Kubernetes service

The AAD Kubernetes service is released independently from StackState, therefore you may benefit from upgrading often. To upgrade the AAD Kubernetes service:

  1. Check the release notes section at the bottom of this page to find the required image tag and helm chart version for the release.

    • Update the image tag in values.yaml.

    • Fetch the helm chart version:

        helm fetch stackstate/anomaly-detection --version 4.1.18
  2. Run the upgrade command below, specifying the necessary parameters as described in the install section.

     helm upgrade anomaly-detector stackstate/anomaly-detection \
         --namespace <stackstate-namespace> \
         --set image.pullSecretPassword=<image registry password>
         --values ./values.yaml

Deactivate the AAD Kubernetes service

To deactivate the AAD Kubernetes service, uninstall the AAD StackPack. The AAD Kubernetes service will continue to run and reserve its compute resources, but anomaly detection will not be executed.

To re-enable the AAD Kubernetes service, you can simply install the AAD StackPack again. It is not necessary to repeat the installation of the AAD Kubernetes service.

Full uninstall

  • Uninstall the AAD Kubernetes service:

    helm delete anomaly-detector
  • Uninstall the AAD StackPack


The status UI provides details on the technical state of the AAD Kubernetes service. You can use it to retrieve information about scheduling progress, possible errors, the ML models selected and job statistics.

To access the status UI, the status interface Ingress must be configured in the anomaly-detection chart (see the Install section).

Common questions that can be answered in the status UI:

Is the AAD Kubernetes service running? If the status UI is accessible: The service is running. If the status UI is not available: Either the service is not running, or the Ingress has not been configured (See the install section).

Can the AAD Kubernetes service reach StackState? Check the status UI sections Top errors and Last stream polling results. Errors here usually indicate connection problems.

Has the AAD Kubernetes service selected streams for anomaly detection? The status UI section Anomaly Detection Summary shows the total time of all registered streams, if no streams are selected it will be zero.

Is the AAD Kubernetes service detecting anomalies? The status UI section Top Anomalous Streams shows the streams with the highest number of anomalies. No streams in this section means that no anomalies have been detected. The status UI section Anomaly Detection Summary shows other relevant metrics, such as total time of all registered streams, total checked time and total time of all anomalies detected.

Is the AAD Kubernetes service scheduling streams? The status UI tab Job Progress shows a ranked list of streams with scheduling progress, including the last time each stream was scheduled.

Release Notes

Release notes for the AAD Kubernetes service are included below. AAD StackPack release notes can be found in the StackPack.

BETA Release

AAD Kubernetes service 4.1.2

Helm chart version: 4.1.24 Image tag: 4.1.2-release Release date: 2020-11-27

Changes in this version:

  • Improved stream selection and ranking. Stream selection is able to handle timeouts gracefully. Stream ranking applies heuristic based stream prioritization.

AAD Kubernetes service 4.1.1

Helm chart version: 4.1.18 Image tag: 4.1.1-release Release date: 2020-10-09

Changes in this version:

  • Upgraded various ML libraries.

  • Added support for username/password authentication.

  • Improved model selection efficiency.

  • Fixed various minor bugs.

AAD Kubernetes service 4.1.0

Helm chart version: 4.1.15 Image tag: 4.1.0-release Release date: 2020-09-04

Changes in this version:

  • Releasing Autonomous Anomaly Detector service Beta.

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