Cluster Autoscaler

Related topics

• Cloud Monitoring works with Cluster Autoscaler From the official documentation: “GKE cluster autoscaler exposes metrics to Cloud Monitoring that provide insights into its decisions, especially in relation to ComputeClasses.”
• Cluster Autoscaler is part of GKE From the official documentation: “GKE's *cluster autoscaler* automatically resizes the number of nodes in a given node pool, based on the demands of your workloads.”
• Cloud Logging works with Cluster Autoscaler From the official documentation: “The cluster autoscaler emits events detailing scale-up, scale-down, and other significant decisions. These events are available as log entries in Cloud Logging.”
• Cluster Autoscaler is part of Google Kubernetes Engine (GKE) From the official documentation: “- Automatically adjust the size of GKE clusters based on the current load by using [cluster autoscaler](/kubernetes-engine/docs/concepts/cluster-autoscaler).”
• Cluster Autoscaler works with Horizontal Pod Autoscaler From the official documentation: “It's a best practice to enable CA whenever you are using either HPA or VPA.”
• Cluster Autoscaler configures managed instance group From the official documentation: “Cluster autoscaler increases or decreases the size of the node pool automatically by adding or removing virtual machine (VM) instances in the underlying Compute Engine [Managed Instance Group (MIG)](/compute/docs/instance-groups#managed_instance_groups) for the node pool.”
• Cluster Autoscaler works with Spot VMs From the official documentation: “Both the cluster autoscaler and node auto-provisioning support using Spot VMs.”
• Managed Service for Apache Airflow depends on Cluster Autoscaler From the official documentation: “Managed Airflow autoscaling uses three different autoscalers provided by GKE: - [Horizontal Pod Autoscaler (HPA)](/kubernetes-engine/docs/concepts/horizontalpodautoscaler) - [Cluster Autoscaler (CA)](/kubernetes-engine/docs/concepts/cluster-autoscaler)”
• BigQuery works with Cloud SQL From the official documentation: “Add credentials to connect BigQuery to Cloud SQL.”
• BigQuery works with Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• BigQuery works with Cloud Monitoring From the official documentation: “You can use Monitoring to export your metrics to BigQuery.”
• Cloud Logging works with GKE From the official documentation: “Enable Cloud Logging on GKE Clusters”
• Cloud Monitoring works with GKE From the official documentation: “Enable Cloud Monitoring on GKE Clusters”
• Cloud Run works with Cloud SQL From the official documentation: “Connecting from Cloud Run (fully managed) to Cloud SQL”
• Cloud SQL works with Compute Engine From the official documentation: “Cloud SQL uses Compute Engine virtual machines (VMs) with persistent storage disks.”
• Compute Engine is an alternative to GKE From the official documentation: “You can also deploy a third-party model to either Vertex AI Model Garden or self-host on GKE or Compute Engine.”
• Artifact Registry works with Cloud Run From the official documentation: “Cloud Run recommends using Artifact Registry.”
• Cloud SQL works with GKE From the official documentation: “The app uses Cloud SQL, Vertex AI, and either Google Kubernetes Engine (GKE) or Cloud Run.”
• Cloud Logging works with Cloud Monitoring From the official documentation: “You can store and view query-response activity logs in Cloud Logging and set up logs-based monitoring by using Cloud Monitoring.”
• Cloud Logging works with Cloud SQL From the official documentation: “Database Migration Service and Cloud SQL use Cloud Logging.”
• BigQuery works with Managed Service for Apache Airflow From the official documentation: “You can use Managed Airflow to orchestrate services in your data pipelines, such as triggering a job in BigQuery or starting a Dataflow pipeline.”
• Cloud Logging works with Compute Engine From the official documentation: “Compute Engine sends you Cloud Logging events for scheduled, started, or completed maintenance.”
• Cloud Run is an alternative to GKE From the official documentation: “For your containerized websites, you can also deploy them to [Cloud Run](/run) in addition to [using GKE](#kubernetes-engine).”
• Compute Engine works with Managed Service for Apache Spark From the official documentation: “Google Compute Engine provides a runtime environment for all features provided by Managed Service for Apache Spark and Gemini Enterprise Agent Platform.”
• GKE depends on Compute Engine From the official documentation: “For example, GKE deploys clusters on Compute Engine VMs.”
• Artifact Registry works with GKE From the official documentation: “In the next tutorial, you deploy those images from the Artifact Registry repository to a GKE cluster:”
• Artifact Registry works with Managed Service for Apache Airflow From the official documentation: “Don't modify or delete the images built by Managed Airflow in Artifact Registry or the repository itself.”
• BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• 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 Monitoring works with Cloud Run From the official documentation: “Cloud Monitoring records metrics regarding the execution of Cloud Run.”
• Cloud Monitoring works with Cloud SQL From the official documentation: “You can also get more details by viewing the Cloud Monitoring logs for your Cloud SQL destination instance.”
• Cloud Monitoring works with Managed Service for Apache Airflow From the official documentation: “To learn about Cloud Logging and Cloud Monitoring for your Managed Airflow environment, see [Monitor environments](/composer/docs/composer-2/monitor-environments).”
• 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.”
• GKE works with Managed Service for Apache Airflow From the official documentation: “Managed Airflow runs your workloads on GKE.”
• 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.”
• 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.”
• Managed Service for Apache Airflow depends on Cloud SQL From the official documentation: “Managed Airflow uses Cloud SQL to run Airflow database.”
• Managed Service for Apache Airflow works with Managed Service for Apache Spark From the official documentation: “This tutorial shows how to use Managed Service for Apache Airflow to create an [Apache Airflow](http://airflow.apache.org) DAG (Directed Acyclic Graph) that runs an Apache Hadoop wordcount job on a Managed Service for Apache Spark cluster.”
• Managed Service for Apache Spark depends on Compute Engine From the official documentation: “Managed Service for Apache Spark clusters are built on [Compute Engine](/compute) instances.”
• Spot VMs is part of Compute Engine From the official documentation: “Spot VMs are virtual machine (VM) instances that are excess Compute Engine capacity.”
• Artifact Registry works with Cloud Logging From the official documentation: “Artifact Registry sends the following types of audit log data to Cloud Logging:”
• Artifact Registry works with Cloud Monitoring From the official documentation: “By default, Artifact Registry is configured to do the following: - Send system logs and audit logs to Cloud Logging - Send system metrics and resource metrics to Cloud Monitoring”
• Artifact Registry secures Cloud Run From the official documentation: “You can use Artifact Registry together with other Google Cloud products and features to improve the security posture of developer workflows and tools, software dependencies, CI/CD systems used to build and deploy your software, and runtime environments such as Google Kubernetes…”
• Artifact Registry works with Compute Engine From the official documentation: “Compute Engine can pull containers directly from Artifact Registry repositories so you can run containerized workloads on virtual machine (VM) instances.”
• 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.”
• 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.”
• BigQuery is an alternative to Managed Service for Apache Spark From the official documentation: “An Analytics Workload refers to the migration of new Data Lake and Data Warehouse environments from on-premises (e.g., Teradata, Cloudera) and cloud platforms (e.g., Snowflake, Redshift, Databricks) to BigQuery or Managed Service for Apache Spark on Google Cloud.”
• Cloud Logging is an alternative to Cloud Monitoring From the official documentation: “To see the status of your invalidation request or find a definitive list of recently submitted requests, you can use Cloud Logging or Cloud Monitoring.”
• Cloud Logging works with Cloud Run From the official documentation: “Cloud Logging captures, stores, and analyzes Cloud Run logs.”
• Cloud Logging works with Horizontal Pod Autoscaler From the official documentation: “The horizontal Pod autoscaler emits decision events, which are stored as log entries in Cloud Logging.”
• Cloud Logging works with Managed Service for Apache Airflow From the official documentation: “To learn about Cloud Logging and Cloud Monitoring for your Managed Airflow environment, see [Monitor environments](/composer/docs/composer-2/monitor-environments).”
• Cloud Logging works with Managed Service for Apache Spark From the official documentation: “Enables (`true`) or disables (`false`) Managed Service for Apache Spark job driver logs in [Cloud Logging](/logging/docs).”
• Cloud Monitoring works with Compute Engine From the official documentation: “Many Google services like Compute Engine send telemetry to Cloud Monitoring.”
• Cloud Monitoring works with Managed Service for Apache Spark From the official documentation: “Monitoring uses workload metadata and metrics to provide insights into the health and performance of Managed Service for Apache Spark workloads.”
• Cloud Run depends on Artifact Registry From the official documentation: “The containerized function is stored in Artifact Registry and deployed to Cloud Run as a service.”
• Cloud Run works with Compute Engine From the official documentation: “This lets Cloud Run clients use this backend service route to send requests directly to Compute Engine instances.”
• Cloud Run is an alternative to Google Kubernetes Engine (GKE) From the official documentation: “The architecture shows [Cloud Run](/run/docs/configuring/services/gpu-best-practices) and [GKE](/kubernetes-engine/docs/concepts/machine-learning/inference) as alternative runtimes for the AI model that you choose to use.”
• Cloud SQL depends on Compute Engine From the official documentation: “Cloud SQL uses Compute Engine virtual machines (VMs) with persistent storage disks.”
• Cloud SQL works with Managed Service for Apache Airflow From the official documentation: “For Private IP environments, Managed Airflow assigns only private IP ([RFC 1918](https://www.ietf.org/rfc/rfc1918.txt)) addresses to the managed Google Kubernetes Engine and Cloud SQL VMs in your environment.”