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kubewise-operator

This project is designed to provide a Kubernetes operator framework that automates the deployment, management, and scaling of cloud cost optimization resources in a Kubernetes cluster.

Description

Kubewise-operator helps users save costs by dynamically managing cloud resources related to Kubernetes workloads. This operator implements custom resource definitions (CRDs) to extend Kubernetes capabilities, making it easy to optimize workloads on various cloud providers.

Features

  • Automatic resource optimization based on historical usage metrics
  • Configurable analysis intervals and cost-saving thresholds
  • Support for ignoring specific resources (Deployments, StatefulSets, DaemonSets) from optimization
  • Integration with Prometheus for metric collection
  • Discord notifications for optimization recommendations

Roadmap

Implemented Features

  • Basic CloudCostOptimizer CRD definition
  • Resource analysis based on historical metrics
  • Configurable analysis intervals
  • Ignore specific resources (Deployments, StatefulSets, DaemonSets)
  • Prometheus integration for metric collection
  • Discord notifications for optimization recommendations

Planned Features

  • Cost-saving threshold configuration
  • Machine learning-based predictive optimization
  • Integration with cloud-specific cost management APIs
  • User interface for visualization and manual control
  • Multi-cluster support
  • Custom metric support beyond Prometheus
  • Automated rollback of optimizations if performance degrades
  • Integration with other notification platforms (Slack, Email, etc.)
  • Cost allocation and chargeback features
  • Support for optimizing storage resources

Project Structure

The project follows a standard Kubernetes operator structure:

  • api/v1alpha1/: Contains the API definitions for the CloudCostOptimizer CRD
  • controllers/: Contains the main logic for the operator
  • config/: Contains Kubernetes manifests for deploying the operator
  • Dockerfile: Defines the container image for the operator
  • main.go: The entry point of the operator

Key files:

  • api/v1alpha1/cloudcostoptimizer_types.go: Defines the CloudCostOptimizer CRD
  • controllers/cloudcostoptimizer_controller.go: Main reconciliation loop
  • controllers/cloudcostoptimizer_analyze.go: Resource analysis and optimization logic
  • config/samples/optimization_v1alpha1_cloudcostoptimizer.yaml: Sample CloudCostOptimizer resource

Getting Started

Prerequisites

Optional:

  • Discord webhook URL and bot token (for notifications)

Development Environment Setup

  1. Install Go, Docker, kubectl, and kustomize using the links provided above.
  2. Install the Operator SDK: 
    export ARCH=$(case $(uname -m) in x86_64) echo -n amd64 ;; aarch64) echo -n arm64 ;; *) echo -n $(uname -m) ;; esac)
export OS=$(uname | awk '{print tolower($0)}')
export OPERATOR_SDK_DL_URL=https://github.com/operator-framework/operator-sdk/releases/download/v1.28.0
curl -LO ${OPERATOR_SDK_DL_URL}/operator-sdk_${OS}_${ARCH}
chmod +x operator-sdk_${OS}_${ARCH} && sudo mv operator-sdk_${OS}_${ARCH} /usr/local/bin/operator-sdk
  3. Clone the kubewise-operator repository: 
    git clone https://github.com/your-org/kubewise-operator.git
cd kubewise-operator

To Deploy on the cluster

Build and push your image to the location specified by IMG:

make docker-build docker-push IMG=<some-registry>/kubewise-operator:tag

NOTE: This image ought to be published in the personal registry you specified. And it is required to have access to pull the image from the working environment. Make sure you have the proper permission to the registry if the above commands don't work.

Install the CRDs into the cluster:

make install

Deploy the Manager to the cluster with the image specified by IMG:

make deploy IMG=<some-registry>/kubewise-operator:tag

NOTE: If you encounter RBAC errors, you may need to grant yourself cluster-admin privileges or be logged in as admin.

Create instances of your solution You can apply the samples (examples) from the config/sample:

kubectl apply -k config/samples/

NOTE: Ensure that the samples has default values to test it out.

Configuration

The CloudCostOptimizer custom resource allows you to configure various aspects of the optimization process. Here's an example configuration:

apiVersion: optimization.dwarvesf.com/v1alpha1
kind: CloudCostOptimizer
metadata:
  name: cloudcostoptimizer-sample
spec:
  analysisInterval: "1h"
  targets:
    - resources: ["pods"]
      namespaces: ["default", "kube-system"]
      automateOptimization: false
      ignoreResources:
        deployment: ["important-deployment", "critical-app"]
        statefulSet: ["database"]
        daemonSet: ["monitoring-agent"]
  costSavingThreshold: 10
  prometheusConfig:
    serverAddress: "http://prometheus-server.monitoring"
    historicalMetricDuration: 6h
  communication:
    discord:
      webhookURL: "https://discord.com/api/webhooks/your-webhook-url"
      botToken: "your-discord-bot-token"

This configuration sets up the CloudCostOptimizer to:

  • Analyze resources every hour
  • Target pods in the "default" and "kube-system" namespaces
  • Ignore specific deployments, statefulsets, and daemonsets
  • Set a cost-saving threshold of 10%
  • Use Prometheus for historical metrics
  • Send notifications to Discord using both a webhook URL and a bot token

ArgoCD Integration

When using the automateOptimization feature with ArgoCD, you need to configure ArgoCD to ignore the changes made by the CloudCostOptimizer. This is because the operator will automatically adjust resource configurations, which may conflict with ArgoCD's sync process.

To configure ArgoCD to ignore these changes, add the following ignoreDifferences section to your Application or ApplicationSet:

spec:
  ignoreDifferences:
    - group: apps
      kind: Deployment
      jsonPointers:
        - /spec/template/spec/containers/0/resources

This configuration tells ArgoCD to ignore differences in the container resources specification, which is where the CloudCostOptimizer makes its optimizations. You can adjust the jsonPointers as needed to match the specific resources you want ArgoCD to ignore.

By applying this configuration, you can ensure that ArgoCD and the CloudCostOptimizer work together harmoniously, allowing the operator to make resource optimizations without triggering unnecessary syncs or conflicts in ArgoCD.

To Uninstall

Delete the instances (CRs) from the cluster:

kubectl delete -k config/samples/

Delete the APIs(CRDs) from the cluster:

make uninstall

UnDeploy the controller from the cluster:

make undeploy

Project Distribution

Following are the steps to build the installer and distribute this project to users.

  1. Build the installer for the image built and published in the registry:
make build-installer IMG=<some-registry>/kubewise-operator:tag

NOTE: The makefile target mentioned above generates an 'install.yaml' file in the dist directory. This file contains all the resources built with Kustomize, which are necessary to install this project without its dependencies.

  1. Using the installer

Users can just run kubectl apply -f to install the project, i.e.:

kubectl apply -f https://raw.githubusercontent.com/<org>/kubewise-operator/<tag or branch>/dist/install.yaml

Contributing

To contribute to this project, please follow these guidelines:

  1. Fork the repository.
  2. Create a feature branch.
  3. Make your changes and commit them.
  4. Push your branch to GitHub.
  5. Create a pull request.

For detailed information on contribution processes and coding standards, please refer to the CONTRIBUTING.md file (to be created).

NOTE: Run make help for more information on all potential make targets

More information can be found via the Kubebuilder Documentation

License

Copyright 2024.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.