Autonomous Anomaly Detector (BETA)
This page describes StackState version 4.2.
The StackState 4.2 version range is End of Life (EOL) and no longer supported. We encourage customers still running the 4.2 version range to upgrade to a more recent 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:
Components in Views that have the most stars are selected.
From those components, only high priority metric streams are selected. See how to set the priority for a stream.
Metric streams with a configured baseline will not be selected. See anomaly detection with baselines.
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.
Prerequisites
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.xlargeAzure 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.
Installation
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 quay.io
To be able to pull the Docker image, you will need access to quay.io. Access credentials can be requested from StackState support.
2. Install Helm
Install Helm (version 3). See the Helm docs https://helm.sh/docs/intro/install
Add the StackState Helm repo:
3. Get the latest AAD Kubernetes service
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-releasepullSecretUsername - 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/orhttp://<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:
EKS Official docs (not using nginx)
EKS blog post (using nginx)
Details of all configuration options are available in the anomaly-detection chart documentation.
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.
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:
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:
Run the upgrade command below, specifying the necessary parameters as described in the install section.
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:
Uninstall the AAD StackPack
Troubleshooting
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 StackPack and the AAD Kubernetes service are available below.
AAD StackPack
AAD StackPack v0.6 BETA (13-10-2020)
Documentation fixes and minor maintenance work.
AAD StackPack v0.2.2 BETA (04-09-2020)
Releasing Autonomous Anomaly Detector service BETA.
AAD Kubernetes service
AAD Kubernetes service v4.3.0-pre.1 BETA
Helm chart version: 4.3.0-pre.1 Image tag: 4.3.0-pre.1 Release date: 2021-01-14
Changes in this version:
Detecting long anomalies and level changes.
AAD Kubernetes service v4.2.0 BETA
Helm chart version: 4.1.27 Image tag: 4.2.0-release Release date: 2020-12-11
Changes in this version:
Performance, stability and other bug fixes.
AAD Kubernetes service v4.1.2 BETA
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 v4.1.1 BETA
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 v4.1.0 BETA
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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