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Google Cloud feature
managed instance group
Topic in 5 exam domains
- managed instance group · Feature · Compute Engine
- managed instance group · Concept
- Managed instance group · Feature
Also called: autoscaled managed instance group, Compute Engine managed instance group, Compute Engine MIG, group of managed instances, IGM, managed instance group (stateless), managed instance groups, MIG, MIGs, regional MIGs, stateful MIGs, zonal managed instance groups
A group of virtual machine instances in Compute Engine that are treated and managed as a single entity.
Where this appears on the exam
4 objectives in 3 of 5 domains
Related topics
FoundationsThis topicWorks alongsideBuilds on theseBuilds on theseAppliedCompute EngineServiceCloud StorageServiceCloud LoggingServiceCloud RunService
managed instance groupFeature
Spot VMsFeatureBigQueryServiceGKEServiceCloud Load BalancingServiceApigeeServiceCloud MonitoringServiceinstance templateFeatureJobConceptManaged Service for Apache SparkServicemanaged instance groupsFeatureautoscalerFeaturePoints to what builds on itWorks with or alternative toHover or tap a line to see what connects the two
- instance template works with managed instance group From the official documentation: “Create a global instance template that you can use to create a managed instance group (MIG) of GPU VMs.”
- managed instance group depends on instance template From the official documentation: “All MIGs require an instance template.”
- instance template configures managed instance group From the official documentation: “Create the MIG using the template.”
- managed instance group works with Spot VMs From the official documentation: “This document explains how to create a managed instance group (MIG) that uses Spot VMs.”
- autoscaler configures managed instance group From the official documentation: “You can set a MIG's target size automatically by using an autoscaler or set the size manually.”
- Cloud Load Balancing works with managed instance group From the official documentation: “It configures the load balancer to connect to the instance group.”
- managed instance group is part of Compute Engine From the official documentation: “Checks if a Compute Engine Managed Instance Group (MIG) is configured as Zonal.”
- Apigee works with managed instance group From the official documentation: “There are a set of VMs (with an `apigee-proxy` prefix) running in a managed instance group (MIG) that forward the traffic to the Apigee instance.”
- autoscaler is part of managed instance group From the official documentation: “Although autoscaling is a feature of managed instance groups, it is a separate API resource.”
- autoscaler works with managed instance group From the official documentation: “If you delete a MIG using the gcloud CLI, any autoscalers attached to the MIG are also deleted.”
- instance template is part of managed instance group From the official documentation: “Each VM in a MIG is based on an instance template.”
- Job depends on managed instance group From the official documentation: “Each Batch job runs on a regional managed instance group (MIG), which is a group of one or more matching Compute Engine virtual machine (VM) instances that are each located in one of the included zones.”
- managed instance group is part of managed instance groups From the official documentation: “You can build highly available deployments of stateful workloads on VMs using stateful managed instance groups (stateful MIGs).”
- Managed Service for Apache Spark depends on managed instance group From the official documentation: “If you're using Managed Service for Apache Spark, the process of [launching preemptible worker nodes](/dataproc/docs/concepts/compute/secondary-vms#preemptible_workers) is managed by a managed instance group.”
- BigQuery is an alternative to Cloud Storage From the official documentation: “The `import_result_sink` must be a Cloud Storage path or a BigQuery table.”
- BigQuery works with Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
- BigQuery works with Cloud Storage From the official documentation: “External BigQuery data source backed by Cloud Storage”
- BigQuery works with Cloud Monitoring From the official documentation: “You can use Monitoring to export your metrics to BigQuery.”
- Cloud Run works with Cloud Storage From the official documentation: “Route Cloud Storage events to Cloud Run”
- Cloud Logging works with GKE From the official documentation: “Enable Cloud Logging on GKE Clusters”
- Compute Engine is an alternative to GKE From the official documentation: “To use TPU7x, you can use Google Kubernetes Engine (GKE) or Compute Engine.”
- managed instance groups is part of Compute Engine From the official documentation: “Compute Engine provides a straightforward way to implement autoscaling through managed instance groups.”
- Cloud Monitoring works with GKE From the official documentation: “Enable Cloud Monitoring on GKE Clusters”
- BigQuery works with Managed Service for Apache Spark From the official documentation: “This means you can quickly read and write BigQuery data to and from Managed Service for Apache Spark.”
- Cloud Run is an alternative to GKE From the official documentation: “The app uses Cloud SQL, Vertex AI, and either Google Kubernetes Engine (GKE) or Cloud Run.”
- managed instance groups depends on instance template From the official documentation: “Managed instance groups require that you create an instance template.”
- Spot VMs is part of Compute Engine From the official documentation: “Spot VMs are virtual machine (VM) instances that are excess Compute Engine capacity.”
- BigQuery is an alternative to Managed Service for Apache Spark From the official documentation: “Run queries against your federated data using BigQuery or Managed Service for Apache Spark.”
- Cloud Logging works with Cloud Storage From the official documentation: “Cloud Storage writes various log entries to Cloud Logging in the Google Cloud console.”
- Cloud Storage works with Managed Service for Apache Spark From the official documentation: “Managed Service for Apache Spark accesses these files on Cloud Storage.”
- autoscaler is part of managed instance groups From the official documentation: “Although autoscaling is a feature of MIGs, it is a separate REST resource.”
- BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
- Cloud Logging works with Compute Engine From the official documentation: “Compute Engine sends you Cloud Logging events for scheduled, started, or completed maintenance.”
- Cloud Storage works with Compute Engine From the official documentation: “You can use a web browser, the Google Cloud CLI, or a programming language to transfer files between Cloud Storage and Compute Engine VMs.”
- GKE depends on Compute Engine From the official documentation: “For example, GKE deploys clusters on Compute Engine VMs.”
- instance template is part of Compute Engine From the official documentation: “Creating a Compute Engine instance template and managed instance group (MIG).”
- Managed Service for Apache Spark depends on Cloud Storage From the official documentation: “Managed Service for Apache Spark uses Cloud Storage as its default distributed file system.”
- Apigee works with Compute Engine From the official documentation: “| [Compute Engine](https://console.cloud.google.com/apis/library/compute.googleapis.com) | `compute.googleapis.com` | Used for managing your Apigee runtime. |”
- autoscaler is part of Compute Engine From the official documentation: “This example assumes you are familiar with Docker containers, as well as Compute Engine resources, particularly HTTP load balancing, autoscaling, managed instance groups, and instance templates.”
- BigQuery works with Cloud Run From the official documentation: “You can then use Cloud Run to transcode data present in the bucket and move it to BigQuery.”
- Cloud Load Balancing works with Cloud Monitoring From the official documentation: “Key performance and health metrics reported by Cloud Monitoring for Cloud Load Balancing resources.”
- Cloud Logging works with Cloud Monitoring From the official documentation: “If a log entry matches the filter of the alerting policy, then Cloud Logging extracts labels and forwards the data to Cloud Monitoring.”
- Cloud Logging works with Cloud Run From the official documentation: “Cloud Logging captures, stores, and analyzes Cloud Run logs.”
- Cloud Monitoring works with Cloud Run From the official documentation: “Cloud Monitoring records metrics regarding the execution of Cloud Run.”
- Cloud Monitoring works with Compute Engine From the official documentation: “Many Google services like Compute Engine send telemetry to Cloud Monitoring.”
- Cloud Run works with GKE From the official documentation: “Leverage Google Kubernetes Engine (GKE) and Cloud Run to deploy applications to the Google Cloud, optimizing costs, performance, and management overhead.”
- Compute Engine works with Managed Service for Apache Spark From the official documentation: “Yes, Managed Service for Apache Spark has native and automatic integrations with Compute Engine, Cloud Storage, Bigtable, BigQuery, Logging, and Cloud Monitoring.”
- Job is part of BigQuery From the official documentation: “Jobs are actions that BigQuery runs on a user's behalf to load, export, query, or copy data.”
- Job is part of Cloud Run From the official documentation: “On Cloud Run, your code can run as a *service*, *job*, *worker pool*, or *instance*.”
- Apigee works with BigQuery From the official documentation: “You can use the Apigee API to export all analytics data for an entire day from Apigee to your own data repository, such as Google Cloud Storage or BigQuery.”
- Apigee works with Cloud Load Balancing From the official documentation: “Load balancer configured to send API calls to Apigee”
- Apigee works with Cloud Logging From the official documentation: “This page describes how to find and use [Cloud Logging](/logging/docs) to query and view maintenance logs for an Apigee instance.”
- Apigee works with Cloud Monitoring From the official documentation: “If you are behind a corporate proxy or firewall, Apigee may not be able to send metrics to Cloud Monitoring with its default settings.”
- Apigee works with Cloud Run From the official documentation: “Apigee supports using [Google OAuth tokens](https://developers.google.com/identity/protocols/oauth2) or [OpenID Connect tokens](https://developers.google.com/identity/protocols/oauth2/openid-connect) to authenticate with Google services such as [Cloud…”
- BigQuery depends on Cloud Storage From the official documentation: “BigQuery relies on Cloud Storage for staging file data and supports reading from and writing to several other sources and Google Cloud services.”
- BigQuery works with Compute Engine From the official documentation: “Compute Engine integrates with Google Cloud technologies such as Cloud Storage, Google Kubernetes Engine, and BigQuery, to extend beyond the basic computational capability to create more complex and sophisticated applications.”
- BigQuery works with GKE From the official documentation: “Subsequently, by integrating BigQuery with GKE, you can use BigQuery to efficiently handle massive datasets (like Google Analytics) and the model's natural language understanding capabilities to generate meaningful insights.”
- Cloud Load Balancing works with Cloud Logging From the official documentation: “This page shows you how to configure and use [Cloud Logging](/logging) and [Cloud Monitoring](/monitoring) with Service Extensions callouts for Cloud Load Balancing.”
- Cloud Load Balancing works with Cloud Run From the official documentation: “If you are using Cloud Load Balancing with Cloud Run, configure custom error responses at the load balancer level.”
- Cloud Load Balancing works with Cloud Storage From the official documentation: “Use a Cloud Storage bucket as a load balancer backend:”
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