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This commit is contained in:
Zhongwei Li
677
commands/optimize/infrastructure.md
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677
commands/optimize/infrastructure.md
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# Infrastructure Optimization Operation
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You are executing the **infrastructure** operation to optimize infrastructure scaling, CDN configuration, resource allocation, deployment, and cost efficiency.
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## Parameters
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**Received**: `$ARGUMENTS` (after removing 'infrastructure' operation name)
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Expected format: `target:"scaling|cdn|resources|deployment|costs|all" [environment:"prod|staging|dev"] [provider:"aws|azure|gcp|vercel"] [budget_constraint:"true|false"]`
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**Parameter definitions**:
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- `target` (required): What to optimize - `scaling`, `cdn`, `resources`, `deployment`, `costs`, or `all`
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- `environment` (optional): Target environment (default: production)
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- `provider` (optional): Cloud provider (auto-detected if not specified)
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- `budget_constraint` (optional): Prioritize cost reduction (default: false)
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## Workflow
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### 1. Detect Infrastructure Provider
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```bash
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# Check for cloud provider configuration
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ls -la .aws/ .azure/ .gcp/ vercel.json netlify.toml 2>/dev/null
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# Check for container orchestration
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kubectl config current-context 2>/dev/null
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docker-compose version 2>/dev/null
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# Check for IaC tools
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ls -la terraform/ *.tf serverless.yml cloudformation/ 2>/dev/null
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```
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### 2. Analyze Current Infrastructure
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**Resource Utilization (Kubernetes)**:
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```bash
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# Node resource usage
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kubectl top nodes
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# Pod resource usage
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kubectl top pods --all-namespaces
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# Check resource requests vs limits
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kubectl get pods -o=jsonpath='{range .items[*]}{.metadata.name}{"\t"}{.spec.containers[*].resources}{"\n"}{end}'
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```
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**Resource Utilization (AWS EC2)**:
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```bash
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# CloudWatch metrics
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aws cloudwatch get-metric-statistics \
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--namespace AWS/EC2 \
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--metric-name CPUUtilization \
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--dimensions Name=InstanceId,Value=i-1234567890abcdef0 \
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--start-time 2025-10-07T00:00:00Z \
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--end-time 2025-10-14T00:00:00Z \
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--period 3600 \
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--statistics Average
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```
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### 3. Scaling Optimization
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#### 3.1. Horizontal Pod Autoscaling (Kubernetes)
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```yaml
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# BEFORE (fixed 3 replicas)
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: api-server
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spec:
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replicas: 3 # Fixed count, wastes resources at low traffic
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template:
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spec:
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containers:
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- name: api
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image: api:v1.0.0
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resources:
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requests:
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memory: "512Mi"
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cpu: "500m"
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# AFTER (horizontal pod autoscaler)
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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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name: api-server-hpa
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spec:
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scaleTargetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: api-server
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minReplicas: 2 # Minimum for high availability
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maxReplicas: 10 # Scale up under load
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metrics:
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- type: Resource
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resource:
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name: cpu
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target:
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type: Utilization
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averageUtilization: 70 # Target 70% CPU
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- type: Resource
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resource:
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name: memory
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target:
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type: Utilization
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averageUtilization: 80
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behavior:
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scaleDown:
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stabilizationWindowSeconds: 300 # Wait 5 min before scaling down
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scaleUp:
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stabilizationWindowSeconds: 0 # Scale up immediately
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policies:
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- type: Percent
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value: 100 # Double pods at a time
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periodSeconds: 15
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# Result:
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# - Off-peak: 2 pods (save 33% resources)
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# - Peak: Up to 10 pods (handle 5x traffic)
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# - Cost savings: ~40% while maintaining performance
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```
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#### 3.2. Vertical Pod Autoscaling
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```yaml
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# Automatically adjust resource requests/limits
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apiVersion: autoscaling.k8s.io/v1
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kind: VerticalPodAutoscaler
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metadata:
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name: api-server-vpa
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spec:
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targetRef:
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apiVersion: apps/v1
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kind: Deployment
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name: api-server
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updatePolicy:
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updateMode: "Auto" # Automatically apply recommendations
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resourcePolicy:
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containerPolicies:
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- containerName: api
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minAllowed:
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memory: "256Mi"
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cpu: "100m"
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maxAllowed:
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memory: "2Gi"
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cpu: "2000m"
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controlledResources: ["cpu", "memory"]
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```
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#### 3.3. AWS Auto Scaling Groups
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```json
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{
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"AutoScalingGroupName": "api-server-asg",
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"MinSize": 2,
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"MaxSize": 10,
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"DesiredCapacity": 2,
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"DefaultCooldown": 300,
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"HealthCheckType": "ELB",
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"HealthCheckGracePeriod": 180,
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"TargetGroupARNs": ["arn:aws:elasticloadbalancing:..."],
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"TargetTrackingScalingPolicies": [
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{
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"PolicyName": "target-tracking-cpu",
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"TargetValue": 70.0,
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"PredefinedMetricSpecification": {
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"PredefinedMetricType": "ASGAverageCPUUtilization"
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}
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}
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]
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}
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```
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### 4. CDN Optimization
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#### 4.1. CloudFront Configuration (AWS)
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```json
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{
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"DistributionConfig": {
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"CallerReference": "api-cdn-2025",
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"Comment": "Optimized CDN for static assets",
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"Enabled": true,
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"PriceClass": "PriceClass_100",
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"Origins": [
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{
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"Id": "S3-static-assets",
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"DomainName": "static-assets.s3.amazonaws.com",
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"S3OriginConfig": {
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"OriginAccessIdentity": "origin-access-identity/cloudfront/..."
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}
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}
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],
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"DefaultCacheBehavior": {
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"TargetOriginId": "S3-static-assets",
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"ViewerProtocolPolicy": "redirect-to-https",
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"Compress": true,
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"MinTTL": 0,
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"DefaultTTL": 86400,
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"MaxTTL": 31536000,
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"ForwardedValues": {
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"QueryString": false,
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"Cookies": { "Forward": "none" }
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}
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},
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"CacheBehaviors": [
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{
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"PathPattern": "*.js",
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"TargetOriginId": "S3-static-assets",
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"Compress": true,
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"MinTTL": 31536000,
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"CachePolicyId": "immutable-assets"
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},
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{
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"PathPattern": "*.css",
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"TargetOriginId": "S3-static-assets",
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"Compress": true,
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"MinTTL": 31536000
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}
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]
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}
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}
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```
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**Cache Headers**:
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```javascript
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// Express server - set appropriate cache headers
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app.use('/static', express.static('public', {
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maxAge: '1y', // Immutable assets with hash in filename
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immutable: true
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}));
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app.use('/api', (req, res, next) => {
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res.set('Cache-Control', 'no-cache'); // API responses
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next();
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});
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// HTML pages - short cache with revalidation
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app.get('/', (req, res) => {
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res.set('Cache-Control', 'public, max-age=300, must-revalidate');
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res.sendFile('index.html');
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});
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```
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#### 4.2. Image Optimization with CDN
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```nginx
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# Nginx configuration for image optimization
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location ~* \.(jpg|jpeg|png|gif|webp)$ {
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expires 1y;
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add_header Cache-Control "public, immutable";
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# Enable compression
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gzip on;
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gzip_comp_level 6;
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# Serve WebP if browser supports it
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set $webp_suffix "";
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if ($http_accept ~* "webp") {
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set $webp_suffix ".webp";
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}
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try_files $uri$webp_suffix $uri =404;
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}
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```
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### 5. Resource Right-Sizing
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#### 5.1. Analyze Resource Usage Patterns
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```bash
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# Kubernetes - Resource usage over time
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kubectl top pods --containers --namespace production | awk '{
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if (NR>1) {
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split($3, cpu, "m"); split($4, mem, "Mi");
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print $1, $2, cpu[1], mem[1]
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}
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}' > resource-usage.txt
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# Analyze patterns
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# If CPU consistently <30% → reduce CPU request
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# If memory consistently <50% → reduce memory request
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```
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**Optimization Example**:
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```yaml
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# BEFORE (over-provisioned)
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resources:
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requests:
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memory: "2Gi" # Usage: 600Mi (30%)
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cpu: "1000m" # Usage: 200m (20%)
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limits:
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memory: "4Gi"
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cpu: "2000m"
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# AFTER (right-sized)
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resources:
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requests:
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memory: "768Mi" # 600Mi + 28% headroom
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cpu: "300m" # 200m + 50% headroom
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limits:
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memory: "1.5Gi" # 2x request
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cpu: "600m" # 2x request
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# Savings: 62% CPU, 61% memory
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# Cost impact: ~60% reduction per pod
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```
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#### 5.2. Reserved Instances / Savings Plans
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**AWS Reserved Instances**:
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```bash
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# Analyze instance usage patterns
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aws ce get-reservation-utilization \
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--time-period Start=2024-10-01,End=2025-10-01 \
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--granularity MONTHLY
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# Recommendation: Convert frequently-used instances to Reserved Instances
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# Example savings:
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# - On-Demand t3.large: $0.0832/hour = $612/month
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# - Reserved t3.large (1 year): $0.0520/hour = $383/month
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# - Savings: 37% ($229/month per instance)
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```
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### 6. Deployment Optimization
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#### 6.1. Container Image Optimization
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```dockerfile
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# BEFORE (large image: 1.2GB)
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FROM node:18
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WORKDIR /app
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COPY . .
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RUN npm install
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CMD ["npm", "start"]
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# AFTER (optimized image: 180MB)
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# Multi-stage build
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FROM node:18-alpine AS builder
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WORKDIR /app
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COPY package*.json ./
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RUN npm ci --only=production
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COPY . .
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RUN npm run build
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FROM node:18-alpine
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WORKDIR /app
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COPY --from=builder /app/dist ./dist
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COPY --from=builder /app/node_modules ./node_modules
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COPY package*.json ./
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# Create non-root user
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RUN addgroup -g 1001 -S nodejs && adduser -S nodejs -u 1001
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USER nodejs
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EXPOSE 3000
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CMD ["node", "dist/main.js"]
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# Image size: 1.2GB → 180MB (85% smaller)
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# Security: Non-root user, minimal attack surface
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```
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#### 6.2. Blue-Green Deployment
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```yaml
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# Kubernetes Blue-Green deployment
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# Green (new version)
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: api-green
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spec:
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replicas: 3
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selector:
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matchLabels:
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app: api
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version: green
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template:
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metadata:
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labels:
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app: api
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version: green
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spec:
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containers:
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- name: api
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image: api:v2.0.0
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---
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# Service - switch traffic by changing selector
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apiVersion: v1
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kind: Service
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metadata:
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name: api-service
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spec:
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selector:
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app: api
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version: green # Change from 'blue' to 'green' to switch traffic
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ports:
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- port: 80
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targetPort: 3000
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# Zero-downtime deployment
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# Instant rollback by changing selector back to 'blue'
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```
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### 7. Cost Optimization
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#### 7.1. Spot Instances for Non-Critical Workloads
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```yaml
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# Kubernetes - Use spot instances for batch jobs
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apiVersion: batch/v1
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kind: Job
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metadata:
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name: data-processing
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spec:
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template:
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spec:
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nodeSelector:
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node.kubernetes.io/instance-type: spot # Use spot instances
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tolerations:
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- key: "spot"
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operator: "Equal"
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value: "true"
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effect: "NoSchedule"
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containers:
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- name: processor
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image: data-processor:v1.0.0
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# Savings: 70-90% cost reduction for spot vs on-demand
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# Trade-off: May be interrupted (acceptable for batch jobs)
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```
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#### 7.2. Storage Optimization
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```bash
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# S3 Lifecycle Policy
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aws s3api put-bucket-lifecycle-configuration \
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--bucket static-assets \
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--lifecycle-configuration '{
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"Rules": [
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{
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"Id": "archive-old-logs",
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"Status": "Enabled",
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"Filter": { "Prefix": "logs/" },
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"Transitions": [
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{
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"Days": 30,
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"StorageClass": "STANDARD_IA"
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},
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{
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"Days": 90,
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"StorageClass": "GLACIER"
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}
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],
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"Expiration": { "Days": 365 }
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}
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]
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}'
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# Cost impact:
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# - Standard: $0.023/GB/month
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# - Standard-IA: $0.0125/GB/month (46% cheaper)
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# - Glacier: $0.004/GB/month (83% cheaper)
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```
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#### 7.3. Database Instance Right-Sizing
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```sql
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-- Analyze actual database usage
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SELECT
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datname,
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pg_size_pretty(pg_database_size(datname)) AS size
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FROM pg_database
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ORDER BY pg_database_size(datname) DESC;
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-- Check connection usage
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SELECT count(*) AS connections,
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max_conn,
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max_conn - count(*) AS available
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FROM pg_stat_activity,
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(SELECT setting::int AS max_conn FROM pg_settings WHERE name='max_connections') mc
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GROUP BY max_conn;
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-- Recommendation: If consistently using <30% connections and <50% storage
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-- Consider downsizing from db.r5.xlarge to db.r5.large
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-- Savings: ~50% cost reduction
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```
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### 8. Monitoring and Alerting
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||||
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**CloudWatch Alarms (AWS)**:
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```json
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{
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"AlarmName": "high-cpu-utilization",
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"ComparisonOperator": "GreaterThanThreshold",
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"EvaluationPeriods": 2,
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"MetricName": "CPUUtilization",
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"Namespace": "AWS/EC2",
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"Period": 300,
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"Statistic": "Average",
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"Threshold": 80.0,
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"ActionsEnabled": true,
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"AlarmActions": ["arn:aws:sns:us-east-1:123456789012:ops-team"]
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}
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```
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**Prometheus Alerts (Kubernetes)**:
|
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```yaml
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groups:
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- name: infrastructure
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rules:
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- alert: HighMemoryUsage
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expr: (node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) / node_memory_MemTotal_bytes > 0.85
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for: 5m
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labels:
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severity: warning
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annotations:
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summary: "High memory usage on {{ $labels.instance }}"
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- alert: HighCPUUsage
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expr: 100 - (avg by (instance) (irate(node_cpu_seconds_total{mode="idle"}[5m])) * 100) > 80
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for: 5m
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labels:
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severity: warning
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```
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||||
## Output Format
|
||||
|
||||
```markdown
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||||
# Infrastructure Optimization Report: [Environment]
|
||||
|
||||
**Optimization Date**: [Date]
|
||||
**Provider**: [AWS/Azure/GCP/Hybrid]
|
||||
**Environment**: [production/staging]
|
||||
**Target**: [scaling/cdn/resources/costs/all]
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||||
|
||||
## Executive Summary
|
||||
|
||||
[Summary of infrastructure state and optimizations]
|
||||
|
||||
## Baseline Metrics
|
||||
|
||||
### Resource Utilization
|
||||
- **CPU**: 68% average across nodes
|
||||
- **Memory**: 72% average
|
||||
- **Network**: 45% utilization
|
||||
- **Storage**: 60% utilization
|
||||
|
||||
### Cost Breakdown (Monthly)
|
||||
- **Compute**: $4,500 (EC2 instances)
|
||||
- **Database**: $1,200 (RDS)
|
||||
- **Storage**: $800 (S3, EBS)
|
||||
- **Network**: $600 (Data transfer, CloudFront)
|
||||
- **Total**: $7,100/month
|
||||
|
||||
### Scaling Configuration
|
||||
- **Auto Scaling**: Fixed 5 instances (no scaling)
|
||||
- **Pod Count**: Fixed 15 pods
|
||||
- **Resource Allocation**: Static (no HPA/VPA)
|
||||
|
||||
## Optimizations Implemented
|
||||
|
||||
### 1. Horizontal Pod Autoscaling
|
||||
|
||||
**Before**: Fixed 15 pods
|
||||
**After**: 8-25 pods based on load
|
||||
|
||||
**Impact**:
|
||||
- Off-peak: 8 pods (47% reduction)
|
||||
- Peak: 25 pods (67% increase capacity)
|
||||
- Cost savings: $1,350/month (30%)
|
||||
|
||||
### 2. Resource Right-Sizing
|
||||
|
||||
**Optimized 12 deployments**:
|
||||
- Average CPU reduction: 55%
|
||||
- Average memory reduction: 48%
|
||||
- Cost impact: $945/month savings
|
||||
|
||||
### 3. CDN Configuration
|
||||
|
||||
**Implemented**:
|
||||
- CloudFront for static assets
|
||||
- Cache-Control headers optimized
|
||||
- Compression enabled
|
||||
|
||||
**Impact**:
|
||||
- Origin requests: 85% reduction
|
||||
- TTFB: 750ms → 120ms (84% faster)
|
||||
- Bandwidth costs: $240/month savings
|
||||
|
||||
### 4. Reserved Instances
|
||||
|
||||
**Converted**:
|
||||
- 3 x t3.large on-demand → Reserved
|
||||
- Commitment: 1 year, no upfront
|
||||
|
||||
**Savings**: $687/month (37% per instance)
|
||||
|
||||
### 5. Storage Lifecycle Policies
|
||||
|
||||
**Implemented**:
|
||||
- Logs: Standard → Standard-IA (30d) → Glacier (90d)
|
||||
- Backups: Glacier after 30 days
|
||||
- Old assets: Glacier after 180 days
|
||||
|
||||
**Savings**: $285/month
|
||||
|
||||
## Results Summary
|
||||
|
||||
### Cost Optimization
|
||||
|
||||
| Category | Before | After | Savings |
|
||||
|----------|--------|-------|---------|
|
||||
| Compute | $4,500 | $2,518 | $1,982 (44%) |
|
||||
| Database | $1,200 | $720 | $480 (40%) |
|
||||
| Storage | $800 | $515 | $285 (36%) |
|
||||
| Network | $600 | $360 | $240 (40%) |
|
||||
| **Total** | **$7,100** | **$4,113** | **$2,987 (42%)** |
|
||||
|
||||
**Annual Savings**: $35,844
|
||||
|
||||
### Performance Improvements
|
||||
|
||||
| Metric | Before | After | Improvement |
|
||||
|--------|--------|-------|-------------|
|
||||
| Average Response Time | 285ms | 125ms | 56% faster |
|
||||
| TTFB (with CDN) | 750ms | 120ms | 84% faster |
|
||||
| Resource Utilization | 68% | 75% | Better efficiency |
|
||||
| Auto-scaling Response | N/A | 30s | Handles traffic spikes |
|
||||
|
||||
### Scalability Improvements
|
||||
|
||||
- **Traffic Capacity**: 2x increase (25 pods vs 15 fixed)
|
||||
- **Scaling Response Time**: 30 seconds to scale up
|
||||
- **Cost Efficiency**: Pay for what you use
|
||||
|
||||
## Trade-offs and Considerations
|
||||
|
||||
**Auto-scaling**:
|
||||
- **Benefit**: 42% cost reduction, 2x capacity
|
||||
- **Trade-off**: 30s delay for cold starts
|
||||
- **Mitigation**: Min 8 pods for baseline capacity
|
||||
|
||||
**Reserved Instances**:
|
||||
- **Benefit**: 37% savings per instance
|
||||
- **Trade-off**: 1-year commitment
|
||||
- **Risk**: Low (steady baseline load confirmed)
|
||||
|
||||
**CDN Caching**:
|
||||
- **Benefit**: 84% faster TTFB, 85% fewer origin requests
|
||||
- **Trade-off**: Cache invalidation complexity
|
||||
- **Mitigation**: Short TTL for dynamic content
|
||||
|
||||
## Monitoring Recommendations
|
||||
|
||||
1. **Cost Tracking**:
|
||||
- Daily cost reports
|
||||
- Budget alerts at 80%, 100%
|
||||
- Tag-based cost allocation
|
||||
|
||||
2. **Performance Monitoring**:
|
||||
- CloudWatch dashboards
|
||||
- Prometheus + Grafana
|
||||
- APM for application metrics
|
||||
|
||||
3. **Auto-scaling Health**:
|
||||
- HPA metrics (scale events)
|
||||
- Resource utilization trends
|
||||
- Alert on frequent scaling
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. Evaluate spot instances for batch workloads (potential 70% savings)
|
||||
2. Implement multi-region deployment for better global performance
|
||||
3. Consider serverless for low-traffic endpoints
|
||||
4. Review database read replicas for read-heavy workloads
|
||||
Reference in New Issue
Block a user