<commit_before># Cluster Autoscaler scalability testing report
##### Authors: aleksandra-malinowska, bskiba

## Introduction

As a part of Cluster Autoscaler graduation to GA we want to guarantee a certain level of scalability limits that Cluster Autoscaler supports. We declare that Cluster Autoscaler scales to 1000 nodes with 30 pods per node. This document further defines what it means that CA scales to 1000 nodes, describes test scenarios and test setup used to measure scalability of CA and outlines its performance measured at this scale.

## CA scales to 1000 nodes

Cluster Autoscaler scales up to a certain number of nodes if it stays responsive. It performs scales up and scale down operations on the cluster within reasonable time frame. If CA is not responsive it can be killed by the liveness probe or fail to provide/release computational resources in cluster when needed, resulting in inability of the cluster to handle additional workload, or in higher cloud provider bills. 

## Expected performance

Cluster Autoscaler needs to be responsive from the user perspective. This means that the changes in cluster state have to be picked up by Cluster Autoscaler as soon as possible, so that it's reaction time is short. To be able to do that, every iteration (cluster state analysis to see if cluster size needs to be changed and according adjustment of the cluster size) needs to finish relatively quickly. Thus, we set the upper bound for iteration duration to 30 seconds.
<commit_msg>Typo fix: "it's reaction time is short"->"its reaction time is short"<commit_after># Cluster Autoscaler scalability testing report
##### Authors: aleksandra-malinowska, bskiba

## Introduction

As a part of Cluster Autoscaler graduation to GA we want to guarantee a certain level of scalability limits that Cluster Autoscaler supports. We declare that Cluster Autoscaler scales to 1000 nodes with 30 pods per node. This document further defines what it means that CA scales to 1000 nodes, describes test scenarios and test setup used to measure scalability of CA and outlines its performance measured at this scale.

## CA scales to 1000 nodes

Cluster Autoscaler scales up to a certain number of nodes if it stays responsive. It performs scales up and scale down operations on the cluster within reasonable time frame. If CA is not responsive it can be killed by the liveness probe or fail to provide/release computational resources in cluster when needed, resulting in inability of the cluster to handle additional workload, or in higher cloud provider bills. 

## Expected performance

Cluster Autoscaler needs to be responsive from the user perspective. This means that the changes in cluster state have to be picked up by Cluster Autoscaler as soon as possible, so that its reaction time is short. To be able to do that, every iteration (cluster state analysis to see if cluster size needs to be changed and according adjustment of the cluster size) needs to finish relatively quickly. Thus, we set the upper bound for iteration duration to 30 seconds.

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