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https://github.com/jthomperoo/predictive-horizontal-pod-autoscaler

Horizontal Pod Autoscaler built with predictive abilities using statistical models
https://github.com/jthomperoo/predictive-horizontal-pod-autoscaler

autoscaler autoscaling go golang horizontal-pod-autoscaler kubernetes operator operator-framework operator-sdk predictions predictive-analytics python replicas statistical-models statsmodels

Last synced: 6 days ago
JSON representation

Horizontal Pod Autoscaler built with predictive abilities using statistical models

Host: GitHub
URL: https://github.com/jthomperoo/predictive-horizontal-pod-autoscaler
Owner: jthomperoo
License: apache-2.0
Created: 2019-12-08T23:55:40.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2023-10-11T23:24:48.000Z (over 1 year ago)
Last Synced: 2025-01-14T06:07:51.132Z (13 days ago)
Topics: autoscaler, autoscaling, go, golang, horizontal-pod-autoscaler, kubernetes, operator, operator-framework, operator-sdk, predictions, predictive-analytics, python, replicas, statistical-models, statsmodels
Language: Go
Homepage:
Size: 438 KB
Stars: 336
Watchers: 8
Forks: 29
Open Issues: 4
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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README

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# Predictive Horizontal Pod Autoscaler

Predictive Horizontal Pod Autoscalers (PHPAs) are Horizontal Pod Autoscalers (HPAs) with extra predictive capabilities,
allowing you to autoscale using statistical models for ahead of time predictions.

## Why would I use it?

PHPAs can better scaling results by making proactive decisions to scale up ahead of demand, meaning that a
resource does not have to wait for performance to degrade before autoscaling kicks in.

## What systems would need it?

Any systems that have regular/predictable demand peaks/troughs.

Some use cases:

* A service that sees demand peak between 3pm and 5pm every week day, this is a regular and predictable load which
could be pre-empted.
* A service which sees a surge in demand at 12pm every day for 10 minutes, this is such a short time interval that
by the time a regular HPA made the decision to scale up there could already be major performance/availablity issues.

PHPAs are not a silver bullet, and require tuning using real data for there to be any benefits of using it. A poorly
tuned PHPA could easily end up being worse than a normal HPA.

## How does it work?

This project works by doing the same calculations as the Horizontal Pod Autoscaler does to determine how many replicas
a resource should have, then applies statistical models against the calculated replica count and the replica history.

## Supported Kubernetes versions

The minimum Kubernetes version the autoscaler can run on is `v1.23` because it relies on the `autoscaling/v2` API which
was only available in `v1.23` and above.

The autoscaler is only tested against the latest Kubernetes version - if there are bugs that affect older Kubernetes
versions we will try to fix them, but there is no guarantee of support.

## Features

* Functionally identical to Horizontal Pod Autoscaler for calculating replica counts without prediction.
* Choice of statistical models to apply over Horizontal Pod Autoscaler replica counting logic.
* Holt-Winters Smoothing
* Linear Regression
* Allows customisation of Kubernetes autoscaling options without master node access. Can therefore work on managed
solutions such as EKS or GCP.
* CPU Initialization Period.
* Downscale Stabilization.
* Sync Period.

## What does a Predictive Horizontal Pod Autoscaler look like?

PHPAs are designed to be as similar in configuration to Horizontal Pod Autoscalers as possible, with extra
configuration options.

PHPAs have their own custom resource:

```yaml
apiVersion: jamiethompson.me/v1alpha1
kind: PredictiveHorizontalPodAutoscaler
metadata:
name: simple-linear
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: php-apache
minReplicas: 1
maxReplicas: 10
behavior:
scaleDown:
stabilizationWindowSeconds: 0
metrics:
- type: Resource
resource:
name: cpu
target:
averageUtilization: 50
type: Utilization
models:
- type: Linear
name: simple-linear
linear:
lookAhead: 10000
historySize: 6
```

This PHPA acts like a Horizontal Pod Autoscaler and autoscales to try and keep the target resource's CPU utilization at
50%, but with the extra predictive layer of a linear regression model applied to the results.

## Installation

The operator for managing Predictive Horizontal Pod Autoscalers can be installed using Helm:

```bash
VERSION=v0.13.2
HELM_CHART=predictive-horizontal-pod-autoscaler-operator
helm install ${HELM_CHART} https://github.com/jthomperoo/predictive-horizontal-pod-autoscaler/releases/download/${VERSION}/predictive-horizontal-pod-autoscaler-${VERSION}.tgz
```

## Quick start

Check out the [getting started
guide](https://predictive-horizontal-pod-autoscaler.readthedocs.io/en/latest/user-guide/getting-started/) and the
[examples](./examples/) for ways to use Predictive Horizontal Pod Autoscalers.

## More information

See the [wiki for more information, such as guides and
references](https://predictive-horizontal-pod-autoscaler.readthedocs.io/en/latest/).

See the [`examples/` directory](./examples) for working code samples.

## Developing this project

Developing this project requires these dependencies:

* [Go](https://golang.org/doc/install) >= `1.20`
* [Python](https://www.python.org/downloads/) == `3.8.x`
* [Helm](https://helm.sh/) == `3.9.x`

Any Python dependencies must be installed by running:

```bash
pip install -r requirements-dev.txt
```

This extensively uses the the [jthomperoo/k8shorizmetrics](https://github.com/jthomperoo/k8shorizmetrics) library
to gather metrics and to evaluate them as the Kubernetes Horizontal Pod Autoscaler does.

It is recommended to test locally using a local Kubernetes managment system, such as
[k3d](https://github.com/rancher/k3d) (allows running a small Kubernetes cluster locally using Docker).

You can deploy a PHPA example (see the [`examples/` directory](./examples) for choices) to test your changes.

### Commands

* `make run` - runs the PHPA locally against the cluster configured in your kubeconfig file.
* `make docker` - builds the PHPA image.
* `make lint` - lints the code.
* `make format` - beautifies the code, must be run to pass the CI.
* `make test` - runs the unit tests.
* `make doc` - hosts the documentation locally at .
* `make coverage` - opens up any generated coverage reports in the browser.