Set up and access Mainframe Assessment Tool

This page describes how to set up and access Mainframe Assessment Tool. Mainframe Assessment Tool can be deployed either as a virtual machine (VM) instance, or on a Google Kubernetes Engine cluster. You can only upgrade Mainframe Assessment Tool instances deployed as a VM instance.

After you deploy Mainframe Assessment Tool, you can connect to and interact with the web-based graphical user interface to analyze your mainframe applications.

Before you begin

Complete the steps to prepare for discovery and assessment.

Option 1: Set up Mainframe Assessment Tool VM instance

To set up a Mainframe Assessment Tool VM instance, follow these steps:

1. In the Google Cloud console, click Activate Cloud Shell.

   Go to the Google Cloud console

2. To create a Mainframe Assessment Tool VM instance, run the following command:

   gcloud compute instances create mainframe-assessment-tool-1-instance \
     --machine-type=MACHINE_TYPE \
     --service-account=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com  \
     --scopes=cloud-platform --image-project=migrate-modernize-public \
     --image-family=mainframe-assessment-tool --zone=ZONE \
     --boot-disk-size=DISK_SIZE
   

   Replace the following:

   • MACHINE_TYPE: the machine type of the VM.
   • SERVICE_ACCOUNT_NAME: the name of the dedicated service account that you created.
   • PROJECT_ID: your Google Cloud project ID.
   • DISK_SIZE: the size of the boot disk.
   • ZONE: the zone where you want to create the VM. All zones in the Google model endpoint locations are supported.

   Use the following table to determine the values for MACHINE_TYPE and DISK_SIZE based on the expected size of your assessment data.

   Assessment zip file size	MACHINE_TYPE	DISK_SIZE
   Smaller than 5MB	e2-standard-4	100GB
   Between 5MB and 50MB	e2-standard-8	100GB
   Larger than 50MB, or multiple assessments in parallel	e2-highmem-16	300GB

   If you've configured the firewall in your project to allow IAP tunnels only for VMs with certain network tags, then you can also add the network tags using the --tags flag. For example, --tags=assessment-iap.

   If your project already has a working Cloud NAT setup, add the --no-address flag to the gcloud command that creates the VM.

Access Mainframe Assessment Tool VM from your computer

You can access Mainframe Assessment Tool from your Linux, macOS, or Windows-based computer by using Google Cloud CLI through an Identity-Aware Proxy tunnel.

To create an encrypted tunnel to a port of Mainframe Assessment Tool VM instance, run the following command:

   gcloud compute start-iap-tunnel VM_NAME 4000 --zone=ZONE \
     --local-host-port=localhost:LOCAL_PORT --project=PROJECT_ID

Replace the following:

• VM_NAME: the name of the VM.
• ZONE: the VM zone.
• LOCAL_PORT: any port number on your local machine that you want to use for the tunneled connection to the Mainframe Assessment Tool.
• PROJECT_ID: the project ID of the VM project.

Google Cloud CLI performs a connectivity test with the VM instance, then opens a tunnel and shows a port number as follows:

   Listening on port 6060.

All traffic sent to localhost:LOCAL_PORT is forwarded to the Mainframe Assessment Tool VM instance. The port is only accessible by applications running on your local computer.

To access Mainframe Assessment Tool from your local computer, open a web browser and go to localhost:LOCAL_PORT.

Option 2: Deploy Mainframe Assessment Tool on a GKE cluster

To deploy Mainframe Assessment Tool on a GKE cluster, follow these steps:

1. In the Google Cloud console, click Activate Cloud Shell.

   Go to the Google Cloud console

2. Enable the Google Kubernetes Engine API for your Google Cloud project.

3. If a GKE cluster does not already exist in your project, create a GKE cluster and make sure that the workload identity pool is configured.

   To create a GKE cluster, run the following command:

     gcloud container clusters create CLUSTER_NAME \
     --project=PROJECT_ID \
     --zone=ZONE \
     --machine-type=MACHINE_TYPE \
     --enable-ip-alias \
     --release-channel="stable" \
     --workload-pool=PROJECT_ID.svc.id.goog \
     --disk-size=DISK_SIZE
   

   Replace the following:

   • CLUSTER_NAME: the name of the new GKE cluster
   • PROJECT_ID: your Google Cloud project ID.
   • MACHINE_TYPE: the machine type of each node.
   • ZONE: the zone where you want to create the cluster. All zones in the Google model endpoint locations are supported.

   • DISK_SIZE: the size of the boot disk.

     Use the following table to determine the values for MACHINE_TYPE and DISK_SIZE based on the expected size of your assessment data:

     Assessment zip file size	MACHINE_TYPE	DISK_SIZE
     Smaller than 5MB	e2-standard-4	100GB
     Between 5MB and 50MB	e2-standard-8	100GB
     Larger than 50MB, or multiple assessments in parallel	e2-highmem-16	300GB

4. Enable Workload Identity Federation for GKE on clusters and node pools.

   Note: In GKE Autopilot, Workload Identity Federation is always enabled.

5. In your environment, create a directory for the deployment scripts:

   mkdir DESTINATION_DIRECTORY
   

   Replace DESTINATION_DIRECTORY with a name for the directory.

6. To download the deployment scripts, run the following command:

   gcloud storage cp "gs://mainframe-ai-release/mat/VERSION/kustomize_deployment.zip" DESTINATION_DIRECTORY
   

   Replace the following:

   • VERSION: the version of Mainframe Assessment Tool that you want to install, for example, mainframe-assessment-2-8-0. *DESTINATION_DIRECTORY: the destination directory on your machine where the script is downloaded.

7. To bind your Google Cloud service account (GSA) to the Kubernetes service account (KSA), run the following command:

   gcloud iam service-accounts add-iam-policy-binding \
   SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com \
   --role="roles/iam.workloadIdentityUser" \
   --member="serviceAccount:PROJECT_ID.svc.id.goog[VERSION/mat-service-account]"
   

   Replace the following:

   • PROJECT_ID: your Google Cloud project ID.
   • VERSION: the version of Mainframe Assessment Tool, for example, mainframe-assessment-2-8-0.

8. Grant read, write, and execute permissions on the destination directory:

   chmod -R u+rwx DESTINATION_DIRECTORY
   

9. Extract the downloaded scripts:

   cd DESTINATION_DIRECTORY \
   unzip kustomize_deployment.zip
   

   Replace DESTINATION_DIRECTORY with the directory where you downloaded the deployment scripts.

10. To create the containers and deploy Mainframe Assessment Tool, run the following command:

    ./deploy.sh PROJECT_ID ZONE \
    CLUSTER_NAME VERSION \
    us-docker.pkg.dev/migrate-modernize-public/mainframe-assessment-prod \
    SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com
    

The containers should be operational within a few minutes.

Access GKE-deployed Mainframe Assessment Tool from your computer

You can access Mainframe Assessment Tool from your Linux, macOS, or Windows computer by using the kubectl command to configure port forwarding.

To create encrypted port forwarding, follow these steps:

1. To configure access to the cluster, run the following command:

   gcloud container clusters get-credentials CLUSTER_NAME \
    --zone ZONE --project PROJECT_ID
   

2. To configure port forwarding, run the following command:

   kubectl port-forward service/assessor-frontend LOCAL_PORT:4000 -n VERSION
   

   Replace the following:

   • LOCAL_PORT: a port number on your local machine used for the tunneled connection to Mainframe Assessment Tool. We recommend using 8080, 8000, or 3000. If these ports are unavailable, use any port number higher than 1024, for example, 3001.
   • VERSION: the version of Mainframe Assessment Tool deployed on the GKE cluster.

What's next

• Learn how to configure settings for Mainframe Assessment Tool.