An enterprise is implementing an automated provisioning and elasticity strategy using Terraform to deploy a mission-critical, stateless serving workload across multiple Google Cloud regions. The deployment must satisfy the following architectural requirements: Ensure identical and reproducible virtual machine (VM) configurations across all target regions. Provide automated resilience against single-zone outages within each region by maintaining balanced instance counts across zones. Automatically scale capacity up or down based on compute demand while maintaining zone distribution. Automatically detect and repair unresponsive or crashed application instances without manual intervention. Which configuration should you define in your Terraform templates to achieve this architecture?

Define a global `google_compute_instance_template`, deploy a `google_compute_region_instance_group_manager` in each region with an `EVEN` target distribution shape and an application health check autohealing policy, and attach a `google_compute_region_autoscaler`.: ### What Regional MIGs with Instance Templates and Autoscalers Are A **regional Managed Instance Group (MIG)** manages homogeneous Compute Engine virtual machine instances across multiple zones within a single region. The group's VM configuration, including machine type, boot disk image, network settings, and startup metadata, is defined using a **global instance template**. Elasticity and resilience are managed by coupling the regional MIG manager with a **regional autoscaler** and an **application autohealing policy**. ### How It Meets the Requirements - **Consistent Regional Deployments**: Using a **global instance template** allows Terraform to reuse a single, standardized, and deterministic VM definition across all target regions, guaranteeing configuration consistency. - **Zonal Resilience & Balanced Capacity**: Configuring the regional MIG with a target distribution shape of **`EVEN`** guarantees that instance counts across selected zones do not differ by more than one instance, ensuring adequate failover capacity during a zonal outage. - **Automated Elasticity**: Attaching a **`google_compute_region_autoscaler`** allows the group to scale out or in dynamically based on metrics such as CPU utilization while respecting the `EVEN` multi-zone distribution shape. - **Automatic Instance Recovery**: Defining an **autohealing policy** with an application-based health check allows the MIG to detect crashed or frozen applications and automatically delete and recreate failing instances. ### Key Advantages - **Regional High Availability**: Protects workloads from localized data center or zonal hardware failures by distributing traffic and compute across multiple physical zones. - **Automated Lifecycle Management**: Handles VM creation, health monitoring, autohealing, and automated rolling updates declaratively through Terraform. - **Elastic Efficiency**: Dynamically optimizes cloud infrastructure costs by matching instance count precisely to incoming traffic loads. - **Standardized Infrastructure as Code (IaC)**: Eliminates human configuration drift across multi-regional environments. ### Why This Is Optimal This pattern represents the Google Cloud architectural best practice for stateless, high-availability web tiers. Regional MIGs combined with `EVEN` distribution shapes, regional autoscaling, and dedicated autohealing health checks deliver multi-zone fault tolerance and zero-touch elasticity natively.

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Automated Provisioning Regional Mig Autoscaling Terraform

Section: Compute resource provisioning

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An enterprise is implementing an automated provisioning and elasticity strategy using Terraform to deploy a mission-critical, stateless serving workload across multiple Google Cloud regions. The deployment must satisfy the following architectural requirements:

Ensure identical and reproducible virtual machine (VM) configurations across all target regions.
Provide automated resilience against single-zone outages within each region by maintaining balanced instance counts across zones.
Automatically scale capacity up or down based on compute demand while maintaining zone distribution.
Automatically detect and repair unresponsive or crashed application instances without manual intervention.

Which configuration should you define in your Terraform templates to achieve this architecture?

Define a global google_compute_instance_template, deploy a google_compute_region_instance_group_manager in each region configured with an ANY_SINGLE_ZONE distribution shape, and attach a google_compute_region_autoscaler.

Define separate zonal google_compute_instance_template resources per zone, create individual zonal google_compute_instance_group_manager resources in each zone, and attach individual google_compute_autoscaler resources.

Define a global google_compute_instance_template, deploy a google_compute_region_instance_group_manager in each region with an EVEN target distribution shape and an application health check autohealing policy, and attach a google_compute_region_autoscaler.

Define a global google_compute_instance_template, deploy a stateful google_compute_region_instance_group_manager in each region configured with stateful disks, and attach a google_compute_region_autoscaler to manage scaling.

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Automated Provisioning Regional Mig Autoscaling Terraform

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