substrate/demos/autoscaled-workerpool/README.md

Autoscaling WorkerPools with an HPA

This describes how to autoscale a WorkerPool on how many of its workers are currently assigned, using a Kubernetes HorizontalPodAutoscaler (HPA) fed by the ate_workerpool_workers metric through prometheus-adapter.

Prerequisites

• A local kind cluster with Agent Substrate installed (./hack/install-ate-kind.sh --deploy-ate-system). Note: this demo is currently only supported on kind.
• ko installed for building images.
• A GCS bucket for storing snapshots (configured via BUCKET_NAME env var).

Architecture (kind)

ate-api-server :9090/metrics          (ate_workerpool_workers, OTel -> Prometheus gauge)
        │  scrape (prometheus.io/scrape annotation, 15s)
        ▼
Prometheus                            manifests/ate-install/monitoring/prometheus.yaml
        │  PromQL
        ▼
prometheus-adapter                    demos/autoscaled-workerpool/prometheus-adapter.yaml
        │  external.metrics.k8s.io
        ▼
HorizontalPodAutoscaler
        │  /scale  (writes WorkerPool.spec.replicas)
        ▼
atecontroller  ──►  Deployment.spec.replicas  ──►  worker pods

HPA Configuration (External + AverageValue)

The example HPA uses an External metric with target type AverageValue:

desiredReplicas = ceil( metricValue / target.averageValue )

where metricValue = max(ate_workerpool_workers{namespace=<ns>, name=<pool>, state=assigned}): the pool's assigned-worker count. WorkerPool names are only unique within a namespace, so the selector must pin both.

averageValue is the target assigned-workers-per-replica:

averageValue	Meaning	Example (assigned=7)
"0.7" (700m)	~70% assigned / 30% idle headroom (default)	ceil(7/0.7) = 10
"1"	pack to 100%, no idle headroom	ceil(7/1) = 7
"0.5" (500m)	lots of headroom, ~2× replicas	ceil(7/0.5) = 14

Lower averageValue → more idle headroom → more replicas.

Scale-up/scale-down behavior

The example HPAs set an aggressive scaleUp (no stabilization window, selectPolicy: Max, Percent 100 / Pods 10 steps) so a burst is served in a batch rather than creeping up one worker at a time, and a slow scaleDown (300s stabilization) to avoid flapping. behavior only sets the rate of change; the HPA still scales to what the metric dictates.

How to Run on Agent Substrate

1. Build and Deploy

# Local dev (kind)
./hack/install-ate-kind.sh --deploy-demo-autoscaled-workerpool

This command will:

• Create the ate-demo-autoscaled-workerpool namespace and one WorkerPool (counter, starting with 5 replicas).
• Create the ate-demo-autoscaled-workerpool atespace and the counter actor template in it (autoscaled-workerpool-template.yaml.tmpl, applied with kubectl ate create actor-template), waiting until the pool is rolled out and the template's golden snapshot is built.
• Deploy prometheus-adapter into ate-demo-autoscaled-workerpool to serve ate_workerpool_workers on external.metrics.k8s.io, and one HorizontalPodAutoscaler (counter).

2. Verify Monitoring Stack & External Metric

Confirm that prometheus-adapter is serving the external metric:

# 1. Adapter is serving the External Metrics API
kubectl get apiservice v1beta1.external.metrics.k8s.io          # Available=True

# 2. The external metric resolves for the counter pool
kubectl get --raw "/apis/external.metrics.k8s.io/v1beta1/namespaces/ate-demo-autoscaled-workerpool/ate_workerpool_workers?labelSelector=ate_worker_state%3Dassigned,ate_workerpool_namespace%3Date-demo-autoscaled-workerpool,ate_workerpool_name%3Dcounter"

How to Use

We can trigger autoscaling by spawning multiple actors and sending traffic to assign workers in the pool. The actors go in the demo's atespace (ate-demo-autoscaled-workerpool) — --template names the template, resolved in the actor's atespace:

1. Spawn load actors

# Install the CLI as a kubectl plugin if not already installed
go install ./cmd/kubectl-ate

# Create 15 actors to generate load
for i in {001..015}; do
  kubectl ate create actor c$i -a ate-demo-autoscaled-workerpool --template counter
done

2. Port-forward the atenet router and send traffic

kubectl port-forward -n ate-system svc/atenet-router 8000:80 &

In a separate terminal, send requests in a retry loop to activate the actors and keep them active while the pool scales up. Each request sets the Actor name for that loop iteration and the demo's Atespace in the routing header:

for attempt in {1..10}; do
  for i in {001..015}; do
  curl -s -H "ate-target-actor: ate-demo-autoscaled-workerpool/c$i" http://localhost:8000 >/dev/null
  done
  sleep 2
done

3. Watch the HPA scale up

As the assigned worker count increases, watch the HPA scale up the pool's replicas:

kubectl -n ate-demo-autoscaled-workerpool get hpa counter -w
kubectl -n ate-demo-autoscaled-workerpool get workerpool counter -w

4. Trigger scale-down

Suspend the actors to drop the assigned worker count. After the 300s stabilization window, the HPA will scale down the pool:

for i in {001..015}; do
  kubectl ate suspend actor c$i -a ate-demo-autoscaled-workerpool
done

How to Uninstall

Remove the demo — this deletes the actors, the template, the atespace, and then the pool and its namespace:

./hack/install-ate-kind.sh --delete-demo-autoscaled-workerpool