Using kubernetes autoscaling with networking.istio.io

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Pulumi · Accepted Answer

When you want to utilize Kubernetes autoscaling in a setup that involves Istio, you're typically looking at combining the Kubernetes Horizontal Pod Autoscaler (HPA) resource with Istio's networking features. The HPA automatically scales the number of pods in a deployment, replication controller, replica set, or stateful set based on observed CPU utilization (or, with custom metrics support, on some other application-provided metrics).

Istio, on the other hand, is an open platform to connect, manage, and secure microservices, which includes services for traffic management, service identity and security, policy enforcement, and telemetry, but it does not provide native autoscaling.

To integrate autoscaling into an Istio-powered service mesh, you would configure the HPA to monitor the metrics of interest. Istio can help generate metrics that Kubernetes can use, thanks to its rich telemetry capabilities. For instance, it can expose the amount of HTTP traffic a pod is handling. You can then use these metrics as a basis for scaling decisions.

Below is a Pulumi program written in TypeScript that demonstrates how you might declare a Kubernetes Horizontal Pod Autoscaler that targets a Deployment which is part of an Istio service mesh. This example assumes that you've got an existing Kubernetes cluster with Istio installed, and a Deployment that you wish to autoscale based on CPU utilization.

Before using this code, make sure you have Pulumi installed, along with the `@pulumi/kubernetes` package. You should also have `kubectl` configured to communicate with your cluster.

```typescript
import * as k8s from '@pulumi/kubernetes';

// Let's assume you have a Kubernetes Deployment that you want to autoscale.
// The following is an example of an HPA resource targeting a deployment called 'my-deployment'
// in the 'default' namespace, and scaling based on the CPU utilization reaching 50%.

const hpa = new k8s.autoscaling.v2beta2.HorizontalPodAutoscaler('my-hpa', {
    metadata: {
        name: 'my-deployment-hpa',
        namespace: 'default', // Namespace where your deployment is located.
    },
    spec: {
        scaleTargetRef: {
            apiVersion: 'apps/v1',
            kind: 'Deployment',
            name: 'my-deployment', // The name of the deployment you're targeting for autoscaling.
        },
        minReplicas: 1, // Minimum number of replicas.
        maxReplicas: 10, // Maximum number of replicas.
        metrics: [{
            type: 'Resource',
            resource: {
                name: 'cpu',
                target: {
                    type: 'Utilization',
                    averageUtilization: 50, // Target CPU utilization percentage to trigger a scale-up.
                },
            },
        }],
    },
}, {provider: istioProvider}); // Make sure to provide the correct K8s provider configured for your Istio-enabled cluster.

// Export the name of the HPA so we can easily retrieve it with Pulumi CLI.
export const hpaName = hpa.metadata.name;
```

In the above code:
1. We define an HPA with a target CPU utilization of 50% -- this means that if the CPU utilization goes above this threshold, the HPA will start creating more pods to handle the load, up to a maximum of 10.
2. We set the minimum number of pod replicas to 1, ensuring that there will always be at least one instance of the pod running.
3. We use the `v2beta2.HorizontalPodAutoscaler` API version, which allows us to use more advanced autoscaling features such as custom metrics, although in this case, we're only using CPU utilization for simplicity.

### Important considerations
- Ensure Istio's sidecar injection mechanism is enabled for the deployment's pods so that they're properly part of the Istio service mesh.
- Set up Istio's metrics exporters to ensure that metrics used for HPA decisions are accurately reported.

This setup allows you to balance the responsiveness of your application under load with efficient resource usage, all within the Istio service mesh.