Skip to content

Latest commit

 

History

History
138 lines (110 loc) · 4.88 KB

README.md

File metadata and controls

138 lines (110 loc) · 4.88 KB

Multi-dimensional Pod Autoscaler (MPA)

Intro

Multi-dimensional Pod Autoscaler (MPA) combines Kubernetes HPA and VPA so that scaling actions can be considered and actuated together in a holistic manner. MPA separates the scaling recommendation and actuation completely so that any multi-dimensional autoscaling algorithms can be used as the "recommender". The default recommender is a simple combination of HPA and VPA algorithm which

  1. sets the requests automatically based on usage and
  2. sets the number of replicas based on a target metric.

Like VPA, MPA is configured with a Custom Resource Definition object. The custom resource is called MultidimPodAutoscaler. It specifies which pods should be vertically and horizontally autoscaled as well as if/how the resource recommendations are applied.

To enable multi-dimensional pod autoscaling on your cluster please follow the installation procedure described below.

Prerequisites

  • kubectl should be connected to the cluster you want to install MPA.
  • The metrics server must be deployed in your cluster. Read more about Metrics Server.
  • If you are using a GKE Kubernetes cluster, you will need to grant your current Google identity cluster-admin role. Otherwise, you won't be authorized to grant extra privileges to the MPA system components. 
    $ gcloud info | grep Account    # get current google identity
Account: [[email protected]]

$ kubectl create clusterrolebinding myname-cluster-admin-binding --clusterrole=cluster-admin [email protected]
Clusterrolebinding "myname-cluster-admin-binding" created
  • You should make sure your API server supports Mutating Webhooks. Its --admission-control flag should have MutatingAdmissionWebhook as one of the values on the list and its --runtime-config flag should include admissionregistration.k8s.io/v1beta1=true. To change those flags, ssh to your API Server instance, edit /etc/kubernetes/manifests/kube-apiserver.manifest and restart kubelet to pick up the changes: sudo systemctl restart kubelet.service
  • Please upgrade openssl to version 1.1.1 or higher (needs to support -addext option).

Installation

To install MPA, please download the source code of MPA and run the following command inside the multidimensional-pod-autoscaler directory:

./hack/mpa-up.sh

Note: the script currently reads environment variables: $REGISTRY and $TAG. Make sure they're unset unless you want to use a non-default version of MPA.

The script issues multiple kubectl commands to the cluster that insert the configuration and start all needed pods in the kube-system namespace. It also generates and uploads a secret (a CA cert) used by MPA Admission Controller when communicating with the API server.

Tearing Down

To remove MPA installation:

./deploy/mpa-down.sh

Quick Start

After installation the system is ready to recommend and set resource requests for your pods. In order to use it, you need to insert a MultidimPodAutoscaler resource for each controller that you want to have automatically computed resource requirements. This will be most commonly a Deployment. There are four modes in which MPAs operate, same as VPA modes.

Example Deployment

A simple way to check if Multidimensional Pod Autoscaler is fully operational in your cluster is to create a sample deployment:

kubectl create -f examples/hamster.yaml

The above command creates a deployment with two pods, each running a single container that requests 100 millicores and tries to utilize slightly above 200 millicores.

Example MPA Config

---
apiVersion: "autoscaling.k8s.io/v1alpha1"
kind: MultidimPodAutoscaler
metadata:
  name: hamster-mpa
  namespace: default
spec:
  # recommenders field can be unset when using the default recommender.
  # recommenders:
  #   - name: 'hamster-recommender'
  scaleTargetRef:
    apiVersion: "apps/v1"
    kind: Deployment
    name: hamster
  resourcePolicy:
    containerPolicies:
      - containerName: '*'
        minAllowed:
          cpu: 100m
          memory: 50Mi
        maxAllowed:
          cpu: 1
          memory: 500Mi
        controlledResources: ["cpu", "memory"]
  constraints:
    minReplicas: 1
    maxReplicas: 6
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 30

Create an MPA:

kubectl create -f examples/hamster-mpa.yaml

To see MPA config and current recommended resource requests run:

kubectl describe mpa