Proactive Scaling: Anticipating Demand for Smoother User Experiences
We all know the drill: applications need to scale. Modern infrastructure, especially in the cloud, allows us to dynamically adjust resources based on demand. The standard approach is reactive scaling—if traffic surges, we spin up more servers (or pods in Kubernetes). This works, but what happens during peak times? Scaling takes time, and those precious seconds can lead to slow response times, frustrated users, and potentially lost revenue. What if we could anticipate these peaks and scale before they hit?
The Challenge
In a previous role, I faced this exact problem. Our e-commerce site experienced predictable traffic surges from 12 PM to 9 PM daily. While Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler handled scaling, there was a noticeable delay, impacting performance. We needed a proactive approach.
The Solution: Scheduled Scaling with Kubernetes CronJobs
We leveraged Kubernetes’ API and CronJobs to schedule scaling before traffic spikes. Here’s how it works:
1. Define the HPA
A standard Horizontal Pod Autoscaler (HPA) definition ensures automatic scaling while setting a higher baseline to handle regular traffic.
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: ecommerce-site
namespace: default
spec:
maxReplicas: 2000
minReplicas: 200
metrics:
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 60
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: ecommerce-site
2. Grant Permissions
The scaling script needs permission to modify the HPA. We create a Role, ServiceAccount, and RoleBinding:
# Role
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
name: scaling-role
namespace: default
rules:
- apiGroups: ["*"]
resourceNames: ["ecommerce-site"]
resources: ["horizontalpodautoscalers"]
verbs: ["patch"]
# Service Account
apiVersion: v1
kind: ServiceAccount
metadata:
name: scaling-sa
namespace: default
# Role Binding
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: scaling-rolebinding
namespace: default
roleRef:
apiGroup: rbac.authorization.k8s.io
kind: Role
name: scaling-role
subjects:
- kind: ServiceAccount
name: scaling-sa
namespace: default
3. Schedule Scaling with a CronJob
We use a CronJob to modify minReplicas before peak hours.
apiVersion: batch/v1
kind: CronJob
metadata:
name: scaling-cronjob
namespace: default
spec:
schedule: '0 12 * * *' # Run daily at 12 PM
jobTemplate:
spec:
template:
spec:
serviceAccountName: scaling-sa
containers:
- name: ubuntu
image: ubuntu
imagePullPolicy: IfNotPresent
command:
- /bin/sh
- -c
- |
apt-get update -y && apt-get install curl -y && \
KUBE_TOKEN=$(cat /var/run/secrets/kubernetes.io/serviceaccount/token) && \
curl -k -X PATCH \
-H "Authorization: Bearer $KUBE_TOKEN" \
-H "Content-Type: application/merge-patch+json" \
--data '{"spec": {"minReplicas": 500}}' \
https://$KUBERNETES_SERVICE_HOST:$KUBERNETES_PORT_443_TCP_PORT/apis/autoscaling/v1/namespaces/default/horizontalpodautoscalers/ecommerce-site
The Result
This proactive scaling approach:
- Ensured a smooth user experience during peak hours.
- Reduced the load on the Cluster Autoscaler.
- Kept the application responsive without delays.
By anticipating demand and adjusting resources in advance, we built a reliable, high-performance system.