Dataflow job flex resource scheduling goal set remediation

Using Console

Using CLI

The Flex Resource Scheduling Goal is a setting in Google Cloud Platform (GCP) that allows you to balance your usage and cost by automatically adjusting the number of virtual machines (VMs) running in a managed instance group (MIG) based on demand. Here are the steps to remediate this misconfiguration in GCP using the GCP CLI:

Open your terminal and authenticate with your GCP account using the command:

gcloud auth login

Set the default project that you want to work with:

gcloud config set project [PROJECT_ID]

Check the status of the MIG for which you want to enable the Flex Resource Scheduling Goal using the command:

gcloud compute instance-groups managed describe [MIG_NAME] --region [REGION]

If the Flex Resource Scheduling Goal is not set, enable it using the command:

gcloud compute instance-groups managed set-autoscaling [MIG_NAME] --region [REGION] --max-num-replicas [MAX_REPLICAS] --min-num-replicas [MIN_REPLICAS] --cool-down-period [COOL_DOWN_PERIOD] --update-stackdriver-metrics

Replace the placeholders with the following values:

[MIG_NAME]: The name of the MIG for which you want to enable the Flex Resource Scheduling Goal.
[REGION]: The region where the MIG is located.
[MAX_REPLICAS]: The maximum number of VMs that can be running in the MIG.
[MIN_REPLICAS]: The minimum number of VMs that should be running in the MIG.
[COOL_DOWN_PERIOD]: The amount of time, in seconds, to wait before making further adjustments to the number of VMs running in the MIG.

Verify that the Flex Resource Scheduling Goal has been enabled using the command:

gcloud compute instance-groups managed describe [MIG_NAME] --region [REGION]

You should see the following line in the output:

autoscalingPolicy:
  coolDownPeriodSec: [COOL_DOWN_PERIOD]
  maxNumReplicas: [MAX_REPLICAS]
  minNumReplicas: [MIN_REPLICAS]
  ...

That’s it! You have successfully remediated the misconfiguration by enabling the Flex Resource Scheduling Goal for the MIG in GCP using the GCP CLI.

Using Python

To remediate the “Flex Resource Scheduling Goal Should Be Set” misconfiguration in GCP using Python, you can follow these steps:

Install the necessary libraries:

pip install google-cloud-resource-manager google-auth google-auth-oauthlib google-auth-httplib2

Authenticate with GCP using service account credentials:

from google.oauth2 import service_account
from google.cloud import resource_manager

credentials = service_account.Credentials.from_service_account_file('path/to/service-account-key.json')
client = resource_manager.Client(credentials=credentials)

Get the project for which you want to set the flex resource scheduling goal:

project_id = 'your-project-id'
project = client.fetch_project(project_id)

Set the flex resource scheduling goal for the project:

from google.cloud.resourcemanager_v3.types import Project

project_labels = project.labels
if project_labels is None:
    project_labels = {}

project_labels['flex-scheduling'] = 'true'

update_mask = {'paths': ['labels']}

updated_project = Project(name=project.name, labels=project_labels)
client.update_project(project=updated_project, update_mask=update_mask)

This code sets the “flex-scheduling” label to “true” for the project, which enables the flex resource scheduling goal. You can modify the label name and value as needed.Note: This code requires the Google Cloud Resource Manager API to be enabled for your project.

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Dataflow job flex resource scheduling goal set remediation

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Remediation

GCP Introduction

GCP Pricing

GCP Threats

GCP Misconfigurations

Resources

GCP Threats

​Triage and Remediation

​Remediation

Triage and Remediation

Remediation