Configuring Auto-Scaling Policies
Overview
Configure auto-scaling policies for cloud workloads across AWS Auto Scaling Groups, GCP Managed Instance Groups, Azure VMSS, and Kubernetes Horizontal Pod Autoscaler (HPA). Generate scaling configurations based on CPU, memory, request rate, or custom metrics with appropriate thresholds, cooldown periods, and scale-in protection.
Prerequisites
- Cloud provider CLI installed and authenticated (
aws, gcloud, or az)
- For Kubernetes HPA:
kubectl configured with cluster access and metrics-server deployed
- Baseline performance data for the target workload (average CPU, memory, request rate)
- Understanding of traffic patterns (steady, bursty, scheduled)
- IAM permissions to create/modify scaling policies and CloudWatch/Stackdriver alarms
Instructions
- Identify the scaling target: EC2 Auto Scaling Group, GCP MIG, Azure VMSS, or Kubernetes Deployment
- Analyze current workload metrics to establish baseline utilization and peak patterns
- Define scaling boundaries: minimum instances/pods, maximum instances/pods, desired count
- Select scaling metric(s): CPU utilization, memory, request count, queue depth, or custom metrics
- Set target thresholds: scale-out trigger (e.g., CPU > 70%), scale-in trigger (e.g., CPU < 30%)
- Configure cooldown periods to prevent flapping (typically 300s scale-out, 600s scale-in)
- Add scale-in protection for stateful workloads or leader nodes if needed
- Generate the scaling policy configuration in the appropriate format (Terraform, YAML, or CLI commands)
- Validate by simulating load and confirming scaling events fire correctly
Output
- Terraform HCL for AWS ASG scaling policies with CloudWatch alarms
- Kubernetes HPA manifests (YAML) with resource or custom metric targets
- GCP autoscaler configurations for Managed Instance Groups
- Scaling policy JSON/YAML for Azure VMSS
- CloudWatch or Stackdriver alarm definitions tied to scaling actions
Error Handling
| Error |
Cause |
Solution |
No scaling activity despite high load |
Metric not reaching threshold or cooldown active |
Verify metric source in CloudWatch/Stackdriver; check cooldown timer with describe-scaling-activities |
Scaling too aggressively (flapping) |
Cooldown too short or threshold too sensitive |
Increase cooldown period and widen the gap between scale-out and scale-in thresholds |
Max capacity reached |
Instance/pod limit hit during traffic spike |
Raise max_size or implement request queuing as a backpressure mechanism |
HPA unable to compute replica count |
Metrics server not deployed or metric unavailable |
Install metrics-server and verify kubectl top pods returns data |
FailedScaleUp: insufficient capacity |
Cloud provider out of capacity in selected AZ/region |
Add multiple AZs to the ASG or use mixed instance types with allocation strategy |
Examples
- "Configure an AWS ASG with target tracking at 65% CPU, min 2 / max 20 instances, and 5-minute cooldown."
- "Create a Kubernetes HPA for a deployment that scales from 3 to 50 pods based on requests-per-second using a custom Prometheus metric."
- "Set up scheduled scaling for a GCP MIG: scale to 10 instances at 8am UTC and back to 2 at 10pm."
Resources
1---2name: configuring-auto-scaling-policies-23description: Configure use when you need to work with auto-scaling. This skill provides auto-scaling configuration with comprehensive guidance and automation. Trigger with phrases like "configure auto-scaling", "set up elastic scaling", or "implement scaling".4license: MIT5---6# Configuring Auto-Scaling Policies
7
8## Overview
9
10Configure auto-scaling policies for cloud workloads across AWS Auto Scaling Groups, GCP Managed Instance Groups, Azure VMSS, and Kubernetes Horizontal Pod Autoscaler (HPA). Generate scaling configurations based on CPU, memory, request rate, or custom metrics with appropriate thresholds, cooldown periods, and scale-in protection.
11
12## Prerequisites
13
14- Cloud provider CLI installed and authenticated (`aws`, `gcloud`, or `az`)
15- For Kubernetes HPA: `kubectl` configured with cluster access and metrics-server deployed
16- Baseline performance data for the target workload (average CPU, memory, request rate)
17- Understanding of traffic patterns (steady, bursty, scheduled)
18- IAM permissions to create/modify scaling policies and CloudWatch/Stackdriver alarms
19
20## Instructions
21
221. Identify the scaling target: EC2 Auto Scaling Group, GCP MIG, Azure VMSS, or Kubernetes Deployment
232. Analyze current workload metrics to establish baseline utilization and peak patterns
243. Define scaling boundaries: minimum instances/pods, maximum instances/pods, desired count
254. Select scaling metric(s): CPU utilization, memory, request count, queue depth, or custom metrics
265. Set target thresholds: scale-out trigger (e.g., CPU > 70%), scale-in trigger (e.g., CPU < 30%)
276. Configure cooldown periods to prevent flapping (typically 300s scale-out, 600s scale-in)
287. Add scale-in protection for stateful workloads or leader nodes if needed
298. Generate the scaling policy configuration in the appropriate format (Terraform, YAML, or CLI commands)
309. Validate by simulating load and confirming scaling events fire correctly
31
32## Output
33
34- Terraform HCL for AWS ASG scaling policies with CloudWatch alarms
35- Kubernetes HPA manifests (YAML) with resource or custom metric targets
36- GCP autoscaler configurations for Managed Instance Groups
37- Scaling policy JSON/YAML for Azure VMSS
38- CloudWatch or Stackdriver alarm definitions tied to scaling actions
39
40## Error Handling
41
42| Error | Cause | Solution |
43|-------|-------|---------|
44| `No scaling activity despite high load` | Metric not reaching threshold or cooldown active | Verify metric source in CloudWatch/Stackdriver; check cooldown timer with `describe-scaling-activities` |
45| `Scaling too aggressively (flapping)` | Cooldown too short or threshold too sensitive | Increase cooldown period and widen the gap between scale-out and scale-in thresholds |
46| `Max capacity reached` | Instance/pod limit hit during traffic spike | Raise `max_size` or implement request queuing as a backpressure mechanism |
47| `HPA unable to compute replica count` | Metrics server not deployed or metric unavailable | Install metrics-server and verify `kubectl top pods` returns data |
48| `FailedScaleUp: insufficient capacity` | Cloud provider out of capacity in selected AZ/region | Add multiple AZs to the ASG or use mixed instance types with allocation strategy |
49
50## Examples
51
52- "Configure an AWS ASG with target tracking at 65% CPU, min 2 / max 20 instances, and 5-minute cooldown."
53- "Create a Kubernetes HPA for a deployment that scales from 3 to 50 pods based on requests-per-second using a custom Prometheus metric."
54- "Set up scheduled scaling for a GCP MIG: scale to 10 instances at 8am UTC and back to 2 at 10pm."
55
56## Resources
57
58- AWS Auto Scaling: https://docs.aws.amazon.com/autoscaling/ec2/userguide/
59- Kubernetes HPA: https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
60- GCP Autoscaler: https://cloud.google.com/compute/docs/autoscaler
61- Azure VMSS Autoscale: https://learn.microsoft.com/en-us/azure/virtual-machine-scale-sets/virtual-machine-scale-sets-autoscale-overview
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