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Lab 9 - Advanced feature - Auto-scaling

Auto-scaling in action

As described in the documentation OpenFaaS ships with auto-scaling. In this lab we will have a look how the auto-scaling works in action.


  • Having completed the set-up of OpenFaaS in Lab 1 you will have everything required to trigger auto-scaling.

  • Multiple tools can be used to create enough traffic to trigger auto-scaling - in this example curl will be used as it is easily available for Mac, Linux and is packaged with Git Bash on Windows.

Background on auto-scaling

Out of the box OpenFaaS is configured such that it will auto-scale based upon the request per seconds metric as measured through Prometheus. This measure is captured as traffic passes through the API Gateway. If the defined threshold for request per seconds is exceeded the AlertManager will fire. This threshold should be reconfigured to an appropriate level for production usage as for demonstration purposes it has been set to a low value in this example.

Find more information on auto-scaling in the documentation site.

Each time the alert is fired by AlertManager the API Gateway will add a certain number of replicas of your function into the cluster. OpenFaaS has two configuration options that allow to specify the starting/minimum amount of replicas and also allows to ceil the maximum amount of replicas:

You can control the minimum amount of replicas for function by setting com.openfaas.scale.min, the default value is currently 1.

You can control the maximum amount of replicas that can spawn for a function by setting com.openfaas.scale.max, the default value is currently 20.

Note: If you set com.openfaas.scale.min and com.openfaas.scale.max to the same value you are disabling the auto-scaling feature.

Check out Prometheus

You will need to run this port-forwarding command in order to be able to access Prometheus on

$ kubectl port-forward deployment/prometheus 9090:9090 -n openfaas

Now add a graph with all successful invocation of the deployed functions. We can do this by executing rate( gateway_function_invocation_total{code="200"} [20s]) as a query. Resulting in a page that looks like this:

Go ahead an open a new tab in which you navigate to the alert section using On this page, you can later see when the threshold for the request per seconds is exceeded.

Trigger scaling of a Go function

First the "echo-fn" function from Alex Ellis:

$ git clone \
 && cd echo-fn \
 && faas-cli template store pull golang-http \
 && faas-cli deploy \
  --label com.openfaas.scale.max=10 \
  --label com.openfaas.scale.min=1

Now check the UI to see when the go-echo function goes from Not Ready to Ready. You can also check this with faas-cli describe go-echo

Use this script to invoke the go-echo function over and over until you see the replica count go from 1 to 5 and so on. You can monitor this value in Prometheus by adding a graph for gateway_service_count or by viewing the API Gateway with the function selected.

$ for i in {0..10000};
   echo -n "Post $i" | faas-cli invoke go-echo && echo;

Note: If you're running on Kubernetes, use $OPENFAAS_URL instead of

Monitor for alerts

You should now be able to see an increase in invocations of the go-echo function in the graph that was created earlier. Move over to the tab where you have open the alerts page. After a time period, you should start seeing that the APIHighInvocationRate state (and colour) changes to Pending before then once again changing to Firing. You are also able to see the auto-scaling using the $ faas-cli list or over the ui

Now you can verify using $ docker service ps go-echo that new replicas of go-echo have been started.

Now stop the bash script and you will see the replica count return to 1 replica after a few seconds.


If you believe that your auto-scaling is not triggering, then check the following:

  • The Alerts page in Prometheus - this should be red/pink and say "FIRING" - i.e. at
  • Check the logs of the core services i.e. the gateway, Prometheus / AlertManager

To get logs for the core services run docker service ls then docker service logs <service-name>.

Load-testing (optional)

It is important to note that there is a difference between applying a scientific method and tooling to a controlled environment and running a Denial Of Service attack on your own laptop. Your laptop is not suitable for load-testing because generally you are running OpenFaaS in a Linux VM on a Windows or Mac host which is also a single node. This is not representative of a production deployment.

See the documentation on constructing a proper performance test.

If curl is not generating enough traffic for your test, or you'd like to get some statistics on how things are broken down then you can try the hey tool. hey can generate a structured load by requests per second or a given duration.

Here's an example of running on a 1GHz 2016 12" MacBook with Docker Desktop. This is a very low-powered computer and as described, not representative of production performance.

$ hey -z=30s -q 5 -c 2 -m POST -d=Test
  Total:        30.0203 secs
  Slowest:      0.0967 secs
  Fastest:      0.0057 secs
  Average:      0.0135 secs
  Requests/sec: 9.9932

  Total data:   1200 bytes
  Size/request: 4 bytes

Response time histogram:
  0.006 [1]     |
  0.015 [244]   |■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■
  0.024 [38]    |■■■■■■
  0.033 [10]    |■■
  0.042 [4]     |■
  0.051 [1]     |
  0.060 [0]     |
  0.069 [0]     |
  0.078 [0]     |
  0.088 [0]     |
  0.097 [2]     |

Latency distribution:
  10% in 0.0089 secs
  25% in 0.0101 secs
  50% in 0.0118 secs
  75% in 0.0139 secs
  90% in 0.0173 secs
  95% in 0.0265 secs
  99% in 0.0428 secs

Details (average, fastest, slowest):
  DNS+dialup:   0.0000 secs, 0.0057 secs, 0.0967 secs
  DNS-lookup:   0.0000 secs, 0.0000 secs, 0.0000 secs
  req write:    0.0001 secs, 0.0000 secs, 0.0016 secs
  resp wait:    0.0131 secs, 0.0056 secs, 0.0936 secs
  resp read:    0.0001 secs, 0.0000 secs, 0.0013 secs

Status code distribution:
  [200] 300 responses

The above simulates two active users -c at 5 requests per second -q over a duration -z of 30 seconds.

To use hey you must have Golang installed on your local computer.

See also: hey on GitHub

Try scale from zero

If you scale down your function to 0 replicas, you can still invoke it. The invocation will trigger the gateway into scaling the function to a non-zero value.

Try it out with the following command:

$ kubectl scale deployment --replicas=0 nodeinfo -n openfaas-fn

Open the OpenFaaS UI and check that nodeinfo has 0 replicas, or by kubectl get deployment nodeinfo -n openfaas-fn.

Now invoke the function and check back that it scaled to 1 replicas.

Now move onto Lab 10.