Deploying AI Data Advisor

You can deploy AI Data Advisor (AIDA) by using Docker or Kubernetes.

AIDA is composed of two major components: AIDA Exporter and AIDA Engine. Each component contains a number of services:

AIDA Exporter
Exporter
Through HCL Workload Automation APIs, exports KPIs metrics from HCL Workload Automation (according to OpenMetrics standard) and stores them into AIDA OpenSearch database.

Also, it exports Alert definitions from HCL Workload Automation and imports them into OpenSearch.

AIDA Engine
Predictor
Calculates the expected values of each KPI, also considering special days.
Anomaly detection and alert generation
Detects anomalies in KPIs trend by comparing observed KPI data points with expected values, and generates alerts when trigger conditions are met.
Email notification
Sends email notification when alerts are generated.
Orchestrator
Orchestrates KPI prediction and anomaly detection.
UI
AIDA User Interface.
Internal event manager
Manages communication among AIDA services.
Also, AIDA uses:
OpenSearch (an Elasticsearch technology)
To store and analyze data.
Keycloak (optional)
To manage security and user access in AIDA (Docker deployment only). Keycloak is optional. If not deployed, the Dynamic Workload Console user authentication roles will be used.
Nginx
As a reverse proxy for its components.
Deploying AIDA using Docker
To monitor HCL Workload Automation and HCL Workload Automation for Z engines, you can deploy AIDA with Docker by using AIDA.sh script. This script provides options to run Docker Compose operations and AIDA configuration steps.

For details, see the following readme file for Docker:

Deploying AIDA using Kubernetes

To monitor HCL Workload Automation engines only (not HCL Workload Automation for Z engines), you can deploy AIDA by using an helm chart. This helm chart is included in the Workload Automation product helm chart that allows you to deploy Workload Automation and all its components in one shot.

For details, see the following readme file for Kubernetes:
Note: Horizontal pod autoscaling based on memory and network usage is supported for Anomaly detection and alert generation and Predictor services. In case of high memory utilization, Kubernetes replicates pods. When memory usage decreases, pods are deleted.
Deploying AIDA on Amazon Web Services (AWS) Marketplace
You can use Amazon Web Services (AWS) Marketplace to subscribe to HCL Workload Automation and deploy your environment on the AWS secure cloud platform, including AIDA as optional component.

For more information see Deploying from Amazon Web Services (AWS) Marketplace.