README
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Crane: Cloud Resource Analytics and Economics
Crane (FinOps Crane) is a cloud native open source project which manages cloud resources on Kubernetes stack, it is inspired by FinOps concepts.
Introduction
The goal of Crane is to provide a one-stop-shop project to help Kubernetes users to save cloud resource usage with a rich set of functionalities:
- Time Series Prediction based on monitoring data
- Usage and Cost visibility
- Usage & Cost Optimization including:
- R2 (Resource Re-allocation)
- R3 (Request & Replicas Recommendation)
- Effective Pod Autoscaling (Effective Horizontal & Vertical Pod Autoscaling)
- Cost Optimization
- Enhanced QoS based on Pod PriorityClass
Features
Time Series Prediction
Crane predictor fetches metric data, and then outputs the prediction results. The prediction result can be consumed by other crane components, like EHPA and Analytics.
Please see this document to learn more.
Effective HorizontalPodAutoscaler
EffectiveHorizontalPodAutoscaler helps you manage application scaling in an easy way. It is compatible with native HorizontalPodAutoscaler but extends more features like prediction-driven autoscaling.
Please see this document to learn more.
Analytics
Analytics model analyzes the workload and provide recommendations about resource optimize.
Two Recommendations are currently supported:
- ResourceRecommend: Recommend container requests & limit resources based on historic metrics.
- Effective HPARecommend: Recommend which workloads are suitable for autoscaling and provide optimized configurations such as minReplicas, maxReplicas.
QoS Ensurance
Repositories
Crane is composed of the following components:
- craned. - main crane control plane.
- Predictor - Predicts resources metrics trends based on historical data.
- AnalyticsController - Analyzes resources and generate related recommendations.
- RecommendationController - Recommend Pod resource requests and autoscaler.
- NodeResourceController - Re-allocate node resource based on prediction result.
- EffectiveHPAController - Effective HPA based on prediction result.
- metric-adaptor. - Metric server for driving the scaling.
- crane-agent. - Ensure critical workloads SLO based on abnormally detection.
- gocrane/api. This repository defines component-level APIs for the Crane platform.
- gocrane/fadvisor Financial advisor which collect resource prices from cloud API.
Getting Started
Prerequisites
- Kubernetes 1.18+
- Helm 3.1.0
Installation
Installing prometheus components with helm chart
Note: If you already deployed prometheus, kube-state-metric, prometheus-node-exporter, then you can skip this step.
Crane use prometheus to be the default metric provider. Using following command to install prometheus with the release name [RELEASE_NAME]. It will also install dependent chart: kube-state-metric.
helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
helm install [RELEASE_NAME] -n [NAMESPACE] --create-namespace prometheus-community/prometheus
Using following command to install prometheus-node-exporter with another release name [NODE_EXPORTER_RELEASE_NAME].
helm install [NODE_EXPORTER_RELEASE_NAME] -n [NAMESPACE] --create-namespace prometheus-community/prometheus-node-exporter
Configure Prometheus Address
The following command will configure your prometheus http address for crane, please change YOUR_PROMETHEUS to actual Prometheus address. If you're following above guide to install prometheus with helm chart then YOUR_PROMETHEUS would be: http:\/\/[RELEASE_NAME]-prometheus-server.[NAMESPACE].svc.cluster.local
PROMETHEUS_ADDRESS="YOUR_PROMETHEUS" && sed -i '' "s/PROMETHEUS_ADDRESS/${YOUR_ADDRESS}/" deploy/craned/deployment.yaml
Deploying Crane
You can deploy Crane by apply YAML declaration.
kubectl apply -f deploy/manifests
kubectl apply -f deploy/craned
kubectl apply -f deploy/metric-adapter
Deploying Crane-agent
If you want to try QoS Ensurance, then deploy Crane-agent by apply YAML declaration which will create a DaemonSet in your cluster.
kubectl apply -f deploy/crane-agent
Directories