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Auto-Scaling Best Practices in Azure
Auto-Scaling Fundamentals
Key Metrics to Scale On
scaling_metrics:
compute:
primary:
- CPU percentage
- Memory percentage
secondary:
- Request count
- Response time
- Queue depth
app_service:
primary:
- CPU percentage
- Memory percentage
secondary:
- HTTP queue length
- Data in/out
- Request count
aks:
primary:
- CPU utilization
- Memory utilization
secondary:
- Custom metrics (Prometheus)
- External metrics (Azure Monitor)
VMSS Auto-Scaling
resource autoscaleSettings 'Microsoft.Insights/autoscalesettings@2022-10-01' = {
name: 'vmss-autoscale'
location: location
properties: {
name: 'vmss-autoscale'
targetResourceUri: vmss.id
enabled: true
profiles: [
{
name: 'Default'
capacity: {
minimum: '2'
maximum: '20'
default: '4'
}
rules: [
// Scale out on high CPU
{
metricTrigger: {
metricName: 'Percentage CPU'
metricResourceUri: vmss.id
timeGrain: 'PT1M'
statistic: 'Average'
timeWindow: 'PT5M'
timeAggregation: 'Average'
operator: 'GreaterThan'
threshold: 70
}
scaleAction: {
direction: 'Increase'
type: 'ChangeCount'
value: '2'
cooldown: 'PT5M'
}
}
// Scale in on low CPU
{
metricTrigger: {
metricName: 'Percentage CPU'
metricResourceUri: vmss.id
timeGrain: 'PT1M'
statistic: 'Average'
timeWindow: 'PT10M' // Longer window for scale-in
timeAggregation: 'Average'
operator: 'LessThan'
threshold: 30
}
scaleAction: {
direction: 'Decrease'
type: 'ChangeCount'
value: '1'
cooldown: 'PT10M' // Longer cooldown for scale-in
}
}
]
}
// Schedule-based profile for known patterns
{
name: 'BusinessHours'
capacity: {
minimum: '4'
maximum: '20'
default: '6'
}
recurrence: {
frequency: 'Week'
schedule: {
timeZone: 'Pacific Standard Time'
days: ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
hours: [8]
minutes: [0]
}
}
rules: [] // Uses metric rules from Default profile
}
{
name: 'AfterHours'
capacity: {
minimum: '2'
maximum: '10'
default: '2'
}
recurrence: {
frequency: 'Week'
schedule: {
timeZone: 'Pacific Standard Time'
days: ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
hours: [18]
minutes: [0]
}
}
rules: []
}
]
notifications: [
{
operation: 'Scale'
email: {
sendToSubscriptionAdministrator: false
customEmails: ['ops-team@company.com']
}
webhooks: [
{
serviceUri: 'https://hooks.slack.com/...'
}
]
}
]
}
}
Kubernetes Auto-Scaling
Horizontal Pod Autoscaler
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: api-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: api
minReplicas: 3
maxReplicas: 50
metrics:
# CPU-based scaling
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
# Memory-based scaling
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 80
# Custom metric from Prometheus
- type: Pods
pods:
metric:
name: http_requests_per_second
target:
type: AverageValue
averageValue: 100
behavior:
scaleUp:
stabilizationWindowSeconds: 60
policies:
- type: Percent
value: 100
periodSeconds: 60
- type: Pods
value: 4
periodSeconds: 60
selectPolicy: Max
scaleDown:
stabilizationWindowSeconds: 300 # 5 minutes stabilization
policies:
- type: Percent
value: 10
periodSeconds: 60
selectPolicy: Min
Cluster Autoscaler
# AKS cluster with autoscaler
resource aks 'Microsoft.ContainerService/managedClusters@2022-07-01' = {
name: 'aks-cluster'
location: location
properties: {
agentPoolProfiles: [
{
name: 'system'
count: 3
vmSize: 'Standard_D4s_v3'
mode: 'System'
enableAutoScaling: true
minCount: 3
maxCount: 5
}
{
name: 'workload'
count: 5
vmSize: 'Standard_D8s_v3'
mode: 'User'
enableAutoScaling: true
minCount: 3
maxCount: 20
scaleSetPriority: 'Regular'
}
{
name: 'spot'
count: 0
vmSize: 'Standard_D8s_v3'
mode: 'User'
enableAutoScaling: true
minCount: 0
maxCount: 50
scaleSetPriority: 'Spot'
scaleSetEvictionPolicy: 'Delete'
spotMaxPrice: -1
}
]
autoScalerProfile: {
balanceSimilarNodeGroups: 'true'
scanInterval: '10s'
scaleDownDelayAfterAdd: '10m'
scaleDownDelayAfterDelete: '10s'
scaleDownUnneededTime: '10m'
scaleDownUtilizationThreshold: '0.5'
}
}
}
KEDA for Event-Driven Scaling
# KEDA ScaledObject for queue-based scaling
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
name: queue-processor-scaler
spec:
scaleTargetRef:
name: queue-processor
pollingInterval: 15
cooldownPeriod: 300
minReplicaCount: 0 # Scale to zero when no messages
maxReplicaCount: 100
triggers:
- type: azure-servicebus
metadata:
queueName: orders
messageCount: "10" # 1 pod per 10 messages
connectionFromEnv: SERVICEBUS_CONNECTION
App Service Auto-Scaling
// Programmatic auto-scaling configuration
using Azure.ResourceManager.AppService;
using Azure.ResourceManager.Monitor;
public async Task ConfigureAutoScaling(string resourceGroupName, string planName)
{
var autoscaleSettings = new AutoscaleSettingData(
location: AzureLocation.WestUS2,
profiles: new[]
{
new AutoscaleProfile(
name: "Default",
capacity: new ScaleCapacity(minimum: "2", maximum: "10", @default: "2"),
rules: new[]
{
// Scale out
new ScaleRule(
metricTrigger: new MetricTrigger(
metricName: "CpuPercentage",
metricResourceUri: appServicePlanId,
timeGrain: TimeSpan.FromMinutes(1),
statistic: MetricStatisticType.Average,
timeWindow: TimeSpan.FromMinutes(5),
timeAggregation: TimeAggregationType.Average,
@operator: ComparisonOperationType.GreaterThan,
threshold: 70
),
scaleAction: new ScaleAction(
direction: ScaleDirection.Increase,
scaleType: ScaleType.ChangeCount,
value: "1",
cooldown: TimeSpan.FromMinutes(5)
)
),
// Scale in
new ScaleRule(
metricTrigger: new MetricTrigger(
metricName: "CpuPercentage",
metricResourceUri: appServicePlanId,
timeGrain: TimeSpan.FromMinutes(1),
statistic: MetricStatisticType.Average,
timeWindow: TimeSpan.FromMinutes(10),
timeAggregation: TimeAggregationType.Average,
@operator: ComparisonOperationType.LessThan,
threshold: 30
),
scaleAction: new ScaleAction(
direction: ScaleDirection.Decrease,
scaleType: ScaleType.ChangeCount,
value: "1",
cooldown: TimeSpan.FromMinutes(10)
)
)
}
)
},
isEnabled: true,
targetResourceUri: appServicePlanId
);
await _monitorClient.AutoscaleSettings.CreateOrUpdateAsync(
resourceGroupName,
$"{planName}-autoscale",
autoscaleSettings
);
}
Best Practices
autoscaling_best_practices = {
"scale_out_quickly": {
"description": "Scale out faster than you scale in",
"scale_out_window": "5 minutes",
"scale_in_window": "10-15 minutes",
"reason": "User experience matters more than cost during spikes"
},
"use_multiple_metrics": {
"description": "Don't rely on single metric",
"example": "CPU AND request count AND response time",
"reason": "Different workloads stress different resources"
},
"set_appropriate_cooldowns": {
"description": "Prevent scaling thrashing",
"scale_out_cooldown": "5 minutes minimum",
"scale_in_cooldown": "10 minutes minimum",
"reason": "New instances need time to warm up and stabilize"
},
"test_scaling_behavior": {
"description": "Load test your auto-scaling",
"tools": ["Azure Load Testing", "k6", "Locust"],
"verify": ["Scaling triggers correctly", "Application handles scaling", "No data loss"]
},
"monitor_and_alert": {
"description": "Track scaling events",
"alerts": ["Max capacity reached", "Scaling failures", "Unusual patterns"]
}
}
Common Mistakes
scaling_mistakes:
too_aggressive_scale_in:
problem: Instances removed too quickly
symptom: Repeated scale-out/scale-in cycles
fix: Increase scale-in cooldown and stabilization window
wrong_metric:
problem: Scaling on metric that doesn't reflect load
symptom: Over/under provisioned during actual load
fix: Use request-based or custom application metrics
no_pre_warming:
problem: Cold instances can't handle traffic
symptom: Errors during scale-out
fix: Pre-warm instances, use health checks with delay
ignoring_startup_time:
problem: Instances need time to start
symptom: Scale-out doesn't help immediately
fix: Scale earlier based on trends, use predictive scaling
Conclusion
Auto-scaling is powerful but requires careful tuning. Start with conservative settings, monitor behavior, and adjust based on real data. Combine metric-based scaling with schedule-based profiles for predictable patterns. Always test your scaling behavior under load before relying on it in production.