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Deployment Options

Jason Shaw edited this page Mar 8, 2024 · 16 revisions

Kubeturbo Deployment

NOTE: All Kubeturbo deployment related documentation is now in the official IBM Docs here. This GitHub wiki is no longer being updated, please refer to the official IBM Docs going forward.


NOTE: New Kubeturbo versions are released with and match the Turbonomic Product Version, detailed here


There are four different deployment options for Kubeturbo: Helm chart, Yamls, Operator via Yamls, and Operator via Red Hat OpenShift Operator Hub.

This document gives a brief overview of each method, and links to articles that provide deployment details based on the method you want.

Please make sure to first review the following topics before deciding on a deployment method:

  1. Prerequisites
  2. Release Notes
  3. Turbonomic - CWOM - Kubeturbo version mappings

Using a Helm Chart

Helm charts are an easy way to deploy and update kubeturbo. We provide you a helm chart when you git clone this project (under kubeturbo/deploy/kubeturbo/), index this chart as a local helm repo, and install. You will need to specify a few parameters to complete the install.

For more details go to Helm Deployment.

Deploy with YAMLs

You can deploy the kubeturbo probe and the related resources using yaml files (under kubeturbo/deploy/kubeturbo_yamls/) that define the resources required, along with an example of a single yaml to create all resources.

For more information, go to Yaml Deployment

Deploy with an Operator

A Kubeturbo Operator can be used to deploy kubeturbo. You can deploy the Operator, CRD and related resources such as a sample CR from the yaml files available (under kubeturbo/deploy/kubeturbo-operator/).

For details go to Operator Deployment.

Deploy with an Operator in OpenShift OperatorHub

For Red Hat OpenShift 4.x users, you can use the certified KubeTurbo Operator available via the OpenShift OperatorHub.

For details go to the OpenShift Operator Hub deployment guide for KubeTurbo

Kubeturbo

Introduction
  1. What's new
  2. Supported Platforms
Kubeturbo Use Cases
  1. Overview
  2. Getting Started
  3. Full Stack Management
  4. Optimized Vertical Scaling
  5. Effective Cluster Management
  6. Intelligent SLO Scaling
  7. Proactive Rescheduling
  8. Better Cost Management
  9. GitOps Integration
  10. Observability and Reporting
Kubeturbo Deployment
  1. Deployment Options Overview
  2. Prerequisites
  3. Turbonomic Server Credentials
  4. Deployment by Helm Chart
    a. Updating Kubeturbo image
  5. Deployment by Yaml
    a. Updating Kubeturbo image
  6. Deployment by Operator
    a. Updating Kubeturbo image
  7. Deployment by Red Hat OpenShift OperatorHub
    a. Updating Kubeturbo image
Kubeturbo Config Details and Custom Configurations
  1. Turbonomic Server Credentials
  2. Working with a Private Repo
  3. Node Roles: Control Suspend and HA Placement
  4. CPU Frequency Getter Job Details
  5. Logging
  6. Actions and Special Cases
Actions and how to leverage them
  1. Overview
  2. Resizing or Vertical Scaling of Containerized Workloads
    a. DeploymentConfigs with manual triggers in OpenShift Environments
  3. Node Provision and Suspend (Cluster Scaling)
  4. SLO Horizontal Scaling
  5. Turbonomic Pod Moves (continuous rescheduling)
  6. Pod move action technical details
    a. Red Hat Openshift Environments
    b. Pods with PVs
IBM Cloud Pak for Data & Kubeturbo:Evaluation Edition
Troubleshooting
  1. Startup and Connectivity Issues
  2. KubeTurbo Health Notification
  3. Logging: kubeturbo log collection and configuration options
  4. Startup or Validation Issues
  5. Stitching Issues
  6. Data Collection Issues
  7. Collect data for investigating Kubernetes deployment issue
  8. Changes to Cluster Role Names and Cluster Role Binding Names
Kubeturbo and Server version mapping
  1. Turbonomic - Kubeturbo version mappings
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