Crane: Cloud Resource Analytics and Economics

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Crane (FinOps Crane) is a cloud native open source project which manages cloud resources on Kubernetes stack, it is inspired by FinOps concepts.

• Crane: Cloud Resource Analytics and Economics
  • Introduction
  • Features
    • TimeSeriesPrediction
    • Effective HorizontalPodAutoscaler
    • Analytics
    • QoS Ensurance
  • Repositories
  • Getting Started
    • Installation
    • Get your Kubernetes Cost Report
    • Analytics and Recommend Pod Resources
    • Analytics and Recommend HPA
  • RoadMap
  • Contributing
  • Code of Conduct

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

Kubernetes is capable of starting multiple pods on same node, and as a result, some of the user applications may be impacted when there are resources(e.g. cpu) consumption competition. To mitigate this, Crane allows users defining PrioirtyClass for the pods and QoSEnsurancePolicy, and then detects disruption and ensure the high priority pods not being impacted by resource competition.

Avoidance Actions:

• Disable Schedule: disable scheduling by setting node taint and condition
• Throttle: throttle the low priority pods by squeezing cgroup settings
• Evict: evict low priority pods

Please see this document to learn more.

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

Installation

Prerequisites

• Kubernetes 1.18+
• Helm 3.1.0

Helm Installation

Please refer to Helm's documentation for installation.

Installing prometheus and grafana with helm chart

Note: If you already deployed prometheus, grafana in your environment, then skip this step.

Crane use prometheus to be the default metric provider. Using following command to install prometheus components: prometheus-server, node-exporter, kube-state-metrics.

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm install prometheus -n crane-system --set pushgateway.enabled=false --set alertmanager.enabled=false --set server.persistentVolume.enabled=false -f https://raw.githubusercontent.com/gocrane/helm-charts/main/integration/prometheus/override_values.yaml --create-namespace  prometheus-community/prometheus

Fadvisor use grafana to present cost estimates. Using following command to install a grafana.

helm repo add grafana https://grafana.github.io/helm-charts
helm install grafana -f https://raw.githubusercontent.com/gocrane/helm-charts/main/integration/grafana/override_values.yaml -n crane-system --create-namespace grafana/grafana

Deploying Crane and Fadvisor

helm repo add crane https://gocrane.github.io/helm-charts
helm install crane -n crane-system --create-namespace crane/crane
helm install fadvisor -n crane-system --create-namespace crane/fadvisor

Verify Installation

Check deployments are all available by running:

kubectl get deploy -n crane-system

The output is similar to:

NAME                            READY   UP-TO-DATE   AVAILABLE   AGE
craned                          1/1     1            1           60m
fadvisor                        1/1     1            1           60m
grafana                         1/1     1            1           60m
metric-adapter                  1/1     1            1           60m
prometheus-kube-state-metrics   1/1     1            1           61m
prometheus-server               1/1     1            1           61m

you can see this to learn more.

Customize Installation

Deploy Crane by apply YAML declaration.

git checkout v0.2.0
kubectl apply -f deploy/manifests 
kubectl apply -f deploy/craned 
kubectl apply -f deploy/metric-adapter

The following command will configure prometheus http address for crane if you want to customize it. Specify CUSTOMIZE_PROMETHEUS if you have existing prometheus server.

export CUSTOMIZE_PROMETHEUS=
if [ $CUSTOMIZE_PROMETHEUS ]; then sed -i '' "s/http:\/\/prometheus-server.crane-system.svc.cluster.local:8080/${CUSTOMIZE_PROMETHEUS}/" deploy/craned/deployment.yaml ; fi

Get your Kubernetes Cost Report

Get the Grafana URL to visit by running these commands in the same shell:

export POD_NAME=$(kubectl get pods --namespace crane-system -l "app.kubernetes.io/name=grafana,app.kubernetes.io/instance=grafana" -o jsonpath="{.items[0].metadata.name}")
kubectl --namespace crane-system port-forward $POD_NAME 3000

visit Cost Report here with account(admin:admin).

Analytics and Recommend Pod Resources

Create an Resource Analytics to give recommendation for deployment: craned and metric-adapter as a sample.

kubectl apply -f https://raw.githubusercontent.com/gocrane/crane/main/examples/analytics/analytics-resource.yaml
kubectl get analytics -n crane-system

The output is:

NAME                      AGE
craned-resource           15m
metric-adapter-resource   15m

You can get created recommendation from analytics status:

kubectl get analytics craned-resource -n crane-system -o yaml

The output is similar to:

apiVersion: analysis.crane.io/v1alpha1
kind: Analytics
metadata:
  name: craned-resource
  namespace: crane-system
spec:
  completionStrategy:
    completionStrategyType: Periodical
    periodSeconds: 86400
  resourceSelectors:
  - apiVersion: apps/v1
    kind: Deployment
    labelSelector: {}
    name: craned
  type: Resource
status:
  lastSuccessfulTime: "2022-01-12T08:40:59Z"
  recommendations:
  - name: craned-resource-resource-j7shb
    namespace: crane-system
    uid: 8ce2eedc-7969-4b80-8aee-fd4a98d6a8b6    

The recommendation name presents on status.recommendations[0].name. Then you can get recommendation detail by running:

kubectl get recommend -n crane-system craned-resource-resource-j7shb -o yaml

The output is similar to:

apiVersion: analysis.crane.io/v1alpha1
kind: Recommendation
metadata:
  name: craned-resource-resource-j7shb
  namespace: crane-system
  ownerReferences:
  - apiVersion: analysis.crane.io/v1alpha1
    blockOwnerDeletion: false
    controller: false
    kind: Analytics
    name: craned-resource
    uid: a9e6dc0d-ab26-4f2a-84bd-4fe9e0f3e105
spec:
  completionStrategy:
    completionStrategyType: Periodical
    periodSeconds: 86400
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: craned
    namespace: crane-system
  type: Resource
status:
  conditions:
  - lastTransitionTime: "2022-01-12T08:40:59Z"
    message: Recommendation is ready
    reason: RecommendationReady
    status: "True"
    type: Ready
  lastSuccessfulTime: "2022-01-12T08:40:59Z"
  lastUpdateTime: "2022-01-12T08:40:59Z"
  resourceRequest:
    containers:
    - containerName: craned
      target:
        cpu: 114m
        memory: 120586239m

The status.resourceRequest is recommended by crane's recommendation engine.

Something you should know about Resource recommendation:

• Resource Recommendation use historic prometheus metrics to calculate and propose.
• We use Percentile algorithm to process metrics that also used by VPA.
• If the workload is running for a long term like several weeks, the result will be more accurate.

Analytics and Recommend HPA

Create an HPA Analytics to give recommendation for deployment: craned and metric-adapter as an sample.

kubectl apply -f https://raw.githubusercontent.com/gocrane/crane/main/examples/analytics/analytics-hpa.yaml
kubectl get analytics -n crane-system 

The output is:

NAME                      AGE
craned-hpa                5m52s
craned-resource           18h
metric-adapter-hpa        5m52s
metric-adapter-resource   18h

You can get created recommendation from analytics status:

kubectl get analytics craned-hpa -n crane-system -o yaml

The output is similar to:

apiVersion: analysis.crane.io/v1alpha1
kind: Analytics
metadata:
  name: craned-hpa
  namespace: crane-system
spec:
  completionStrategy:
    completionStrategyType: Periodical
    periodSeconds: 86400
  resourceSelectors:
  - apiVersion: apps/v1
    kind: Deployment
    labelSelector: {}
    name: craned
  type: HPA
status:
  lastSuccessfulTime: "2022-01-13T07:26:18Z"
  recommendations:
  - apiVersion: analysis.crane.io/v1alpha1
    kind: Recommendation
    name: craned-hpa-hpa-2f22w
    namespace: crane-system
    uid: 397733ee-986a-4630-af75-736d2b58bfac

The recommendation name presents on status.recommendations[0].name. Then you can get recommendation detail by running:

kubectl get recommend -n crane-system craned-resource-resource-j7shb -o yaml

The output is similar to:

apiVersion: analysis.crane.io/v1alpha1
kind: Recommendation
metadata:
  name: craned-hpa-hpa-2f22w
  namespace: crane-system
  ownerReferences:
  - apiVersion: analysis.crane.io/v1alpha1
    blockOwnerDeletion: false
    controller: false
    kind: Analytics
    name: craned-hpa
    uid: b216d9c3-c52e-4c9c-b9e9-9d5b45165b1d
spec:
  completionStrategy:
    completionStrategyType: Periodical
    periodSeconds: 86400
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: craned
    namespace: crane-system
  type: HPA
status:
  conditions:
  - lastTransitionTime: "2022-01-13T07:51:18Z"
    message: 'Failed to offer recommend, Recommendation crane-system/craned-hpa-hpa-2f22w
      error EHPAAdvisor prediction metrics data is unexpected, List length is 0 '
    reason: FailedOfferRecommend
    status: "False"
    type: Ready
  lastUpdateTime: "2022-01-13T07:51:18Z"

The status.resourceRequest is recommended by crane's recommendation engine. The fail reason is demo workload don't have enough run time.

Something you should know about HPA recommendation:

• HPA Recommendation use historic prometheus metrics to calculate, forecast and propose.
• We use DSP algorithm to process metrics.
• We recommend using Effective HorizontalPodAutoscaler to execute autoscaling, you can see this document to learn more.
• The Workload need match following conditions:
  • Existing at least one ready pod
  • Ready pod ratio should larger that 50%
  • Must provide cpu request for pod spec
  • The workload should be running for at least a week to get enough metrics to forecast
  • The workload's cpu load should be predictable, too low or too unstable workload often is unpredictable

RoadMap

Please see this document to learn more.

Contributing

Contributors are welcomed to join Crane project. Please check CONTRIBUTING about how to contribute to this project.

Code of Conduct

Crane adopts CNCF Code of Conduct.