Droplet cluster autoscaling uses the power of DigitalOcean Functions to enable a developer to create a cluster of Droplets that automatically scale based on load. To use cluster autoscaling, a developer creates a base Droplet, takes a snapshot of the Droplet and adds the Droplet to a load balancer. Clones of the base Droplet can be automatically added or removed from the cluster as load changes using a DigitalOcean Function written in Go.

• Architecture diagram
• How to use
• Environment variables
• Deploy the function
• Benchmarks

Introduction

• Droplet cluster autoscaling is one file of code called “scale.go” that can run as a scheduled or standalone function. It must be passed configuration parameters including the droplet tag for the cluster, load balancer name, a DigitalOcean API key, etc. and an operation to perform.

About DigitalOcean Droplets

DigitalOcean Droplets are Linux-based virtual machines (VMs) that run on top of virtualized hardware. Each Droplet you create is a new server you can use, either standalone or as part of a larger, cloud-based infrastructure.

About DigitalOcean Functions

Functions are blocks of code that run on demand without the need to manage any infrastructure. Develop on your local machine, test your code from the command line (using doctl), then deploy to a production namespace or App Platform — no servers required.

Architecture diagram

Prerequisites

1. A DigitalOcean account (Log in)
2. doctl CLI - How to install
3. To use doctl with our serverless Functions product, you must first install a software extension, then use it to connect to the development namespace.

Additionally you will need:

1. A Base Droplet, this is the first Droplet that will be used for scaling
2. A Snapshot of an image you want to scale. To use droplet autoscaling, you must take a snapshot of your base droplet and that snapshot will be the system image used by all clone droplets. The snapshot should have server(s) that automatically start when the new system boots.

How to use

The operation to perform (op) must be one of:

• info return internal configuration information about the cluster
• status return the status of the cluster including load metrics
• up scale the cluster up manually by adding a droplet
• down scale the cluster down manually by deleting the last droplet added
• auto autoscale the cluster up or down based on load metrics

Example:

• <.. function url path..>/scale?op=info

Deploy a DigitalOcean Droplet

How to create a Droplet using the DigitalOcean CLI

To create a Droplet via the command line, follow these steps:

1. Install doctl, the DigitalOcean command-line tool.

2. Create a personal access token, and save it with doctl.

3. Use the token to grant doctl access to your DigitalOcean account.

   doctl auth init

4. Finally, create a Droplet with doctl compute droplet create.

   doctl compute droplet create <droplet-name>... [flags]

• `base-image`` is the name of our Droplet
• cluster_tag is the name of the cluster tag

Create a snapshot to be used as Droplet template image

You must take a snapshot of your base droplet and that snapshot will be the system image used by all clone droplets. The snapshot should have server(s) that automatically start when the new system boots. The snapshotName is the name of this base droplet snapshot.

Tag your base Droplet

The base droplet must have a tag. All clones that are created will have this tag on them.

Create a Load Balancer

Droplet autoscaling requires a load balancer. You can find the DigitalOcean load balancer under the Network section of the DigitalOcean user-interface. The base droplet must be added to the load balancer. Clone droplets will automatically be added and removed from the load balancer cluster as the system scales up and down. The loadBalancerName is the name of this load balancer.

Deploy the function

1. Create a Functions namespace

   doctl serverless namespaces create --label "scale-ns" --region "lon1"

2. Connect to your namespace

   doctl serverless connect

3. Clone the function from this repo git clone https://github.com/DO-Solutions/droplet-autoscale

4. Deploy the function

   doctl serverless deploy droplet-autoscale

Environmental Variables

The following environment variables are required:

• API_TOKEN a DigitalOcean API token
• BASE_DROPLET the name of the base droplet to scale
• SNAPSHOT To use droplet autoscaling, you must take a snapshot of your base droplet and that snapshot will be the system image used by all clone droplets. The snapshot should have server(s) that automatically start when the new system boots. The snapshotName is the name of this base droplet snapshot.
• CLUSTER_TAG the droplet tag for the cluster. The base droplet must have this tag. All clones that are created will have this tag on them.
• CLONE_PREFIX the name of clone droplets is the dropletNamePrefix and the number of the clone. The first clone will be 01, the second 02, etc. An example of a droplet name prefix is “auto”
• LOAD_BALANCER Droplet autoscaling requires a load balancer. You can find the DigitalOcean load balancer under the Network section of the DigitalOcean user-interface. The base droplet must be added to the load balancer. Clone droplets will automatically be added and removed from the load balancer cluster as the system scales up and down. The loadBalancerName is the name of this load balancer.

The following environment variables are optional:

• MAX_DROPLETS_IN_CLUSTER Defaults to 5. The maximum number of droplets in the cluster inclusive of the base droplet. The system will not scale up beyond this number of droplets.
• SCALING_INTERVAL Defaults to 120 seconds. After a clone droplet is added or removed, the system will stop autoscaling up or down until this interval has passed. The system adds or removes clones automatically based on load metrics. These metrics take some time to stabilize after a clone is added or removed. The scalingInterval is the amount of time it takes the system to stabilize, in seconds, after a droplet is added or removed and should not only allow the system to stabilize but also account for the time it takes the new load metrics from the droplets to be available.
• SCALE_UP_WHEN_LOAD When the average CPU load metric is greater than this value, the system will recommend scaling up.
• SCALE_UP_WHEN_FREE_MEMORY When the average droplet free memory is below this value, in MB, the system will recommend scaling up.
• SCALE_UP_WHEN_OUTBOUND_BANDWIDTH When the average droplet bandwidth, in MBps, is greater than this value, the system will recommend scaling up.
• SCALE_DOWN_WHEN_LOAD When the average CPU load metric is less than this value and all other SCALE_DOWN metric values are within range, the system will recommend scaling down.
• SCALE_DOWN_WHEN_FREE_MEMORY When the average droplet free memory is above this value and all other SCALE_DOWN metric values are within range, in MB, the system will recommend scaling down.
• SCALE_DOWN_WHEN_OUTBOUND_BANDWIDTH When the average droplet bandwidth, in MBps, is less than this value and all other SCALE_DOWN metric values are within range, the system will recommend scaling down.

Examples

Status

Return the status of the cluster including load metrics

curl -X GET "https://faas-lon1-917a94a7.doserverless.co/api/v1/web/fn-01b08248-2b36-420c-ba27-56b52d3ac42f/autoscale/scale?op=status" \
  -H "Content-Type: application/json"

{
 "dropletMetrics": [
  {
   "ID": "scale-me",
   "valid": true,
   "load": 0,
   "freeMemory": 235.454464,
   "outboundBandwidth": 0.002357839671014176
  }
 ],
 "clusterMetrics": {
  "ID": "",
  "valid": true,
  "load": 0,
  "freeMemory": 235.454464,
  "outboundBandwidth": 0.002357839671014176
 },
 "scaleUp": true,
 "scaleDown": false
}

References

Many thanks to the original DigitalOcean engineer that crafted this code who is no longer with us.

If you wish to learn more about DigitalOcean's services, you are welcome to reach out to the sales team at [email protected]. A global team of talented engineers will be happy to provide assistance.