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feat(cost): cost optimization strategies for cloud infrastructure (#95)
## Summary Resolves Issue #95 — implements cost analysis, monitoring, resource optimization, cost alerts, and automated cost reports for the cloud infrastructure. ## Changes ### Analyze Current Costs (infra/cost/cost-analysis.md) - Updated inventory table: RDS gp2 → gp3 (saves ~$0.70/month), S3 lifecycle - Added 'Cost Monitoring' table listing all tooling (AWS Budgets, Prometheus, Grafana, alert rules, cost-report.sh, GitHub Actions workflow) - Added 'Implemented Optimizations' table with per-item savings estimates ### Implement Cost Monitoring (monitoring/grafana/provisioning/dashboards/cost.json) - New 'StellarEscrow Cost Overview' Grafana dashboard (uid: stellar-escrow-cost) - Panels: estimated monthly spend stat, budget utilisation gauge, per-service cost stats (compute/database/networking), cost breakdown piechart, spend-vs-budget timeseries, CPU and memory utilisation timeseries - All cost panels wired to Prometheus metrics (stellar_escrow_*_cost_usd) ### Add Resource Optimization (infra/cost/cost-optimization.tf) - RDS gp3 storage type documented with migration command (no-downtime) - CloudWatch log retention for prometheus/grafana log groups (14 days) - S3 lifecycle: abort incomplete multipart uploads after 7 days (hidden cost) - S3 lifecycle: STANDARD → STANDARD_IA at 30d → GLACIER_IR at 90d → expire at 365d - AWS resource group with Project/Environment cost allocation tags for per-service cost breakdown in Cost Explorer ### Include Cost Alerts (monitoring/alert_rules_cost.yml) - Group: stellar_escrow_cost (5 rules) - BudgetWarning: >80% of monthly budget consumed (warning, 1h) - BudgetExceeded: >100% of monthly budget (critical, 1h) - CostAnomalySpike: estimated cost delta >0 in 24h (warning, immediate) - LowCPUUtilisation: avg CPU <10% over 24h → downsizing opportunity (info) - LowMemoryUtilisation: avg memory <20% over 24h → downsizing opportunity (info) - prometheus.yml: added alert_rules_cost.yml to rule_files - docker-compose.yml: mounted alert_rules_cost.yml into Prometheus container ### Create Cost Reports (.github/workflows/cost-report.yml) - Scheduled workflow: runs on 1st of every month at 08:00 UTC - Manual trigger: workflow_dispatch with optional month input (YYYY-MM) - Steps: AWS credentials → cost-report.sh → upload artifact (90-day retention) → post JSON summary to GitHub Actions job summary ## Acceptance Criteria Coverage - Analyze current costs: cost-analysis.md updated with inventory, breakdown, optimizations - Implement cost monitoring: Grafana cost dashboard + Prometheus metrics - Add resource optimization: cost-optimization.tf (gp3, S3 lifecycle, log retention, tags) - Include cost alerts: alert_rules_cost.yml with 5 rules (budget, anomaly, utilisation) - Create cost reports: cost-report.yml GitHub Actions workflow (monthly + on-demand)
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‎.github/workflows/cost-report.yml‎

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name: Monthly Cost Report
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on:
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schedule:
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# 1st of every month at 08:00 UTC
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- cron: '0 8 1 * *'
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workflow_dispatch:
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inputs:
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month:
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description: 'Month to report (YYYY-MM, default: current)'
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required: false
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jobs:
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cost-report:
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name: Generate Cost Report
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runs-on: ubuntu-latest
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permissions:
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contents: write
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steps:
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- uses: actions/checkout@v4
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- name: Configure AWS credentials
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uses: aws-actions/configure-aws-credentials@v4
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with:
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aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
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aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
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aws-region: us-east-1
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- name: Run cost report
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run: |
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MONTH="${{ github.event.inputs.month || '' }}"
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if [[ -n "$MONTH" ]]; then
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bash infra/cost/cost-report.sh "$MONTH" json
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else
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bash infra/cost/cost-report.sh --json
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fi
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- name: Upload report artifact
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uses: actions/upload-artifact@v4
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with:
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name: cost-report-${{ github.run_id }}
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path: infra/cost/reports/
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retention-days: 90
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- name: Post summary to job
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run: |
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REPORT=$(ls infra/cost/reports/*.json | tail -1)
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echo "## Cost Report" >> $GITHUB_STEP_SUMMARY
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echo '```json' >> $GITHUB_STEP_SUMMARY
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cat "$REPORT" | python3 -m json.tool >> $GITHUB_STEP_SUMMARY
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echo '```' >> $GITHUB_STEP_SUMMARY

‎docker-compose.yml‎

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- ./monitoring/alert_rules_security.yml:/etc/prometheus/alert_rules_security.yml:ro
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- ./monitoring/alert_rules_performance.yml:/etc/prometheus/alert_rules_performance.yml:ro
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- ./monitoring/alert_rules_infrastructure.yml:/etc/prometheus/alert_rules_infrastructure.yml:ro
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- ./monitoring/alert_rules_cost.yml:/etc/prometheus/alert_rules_cost.yml:ro
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- prometheus_data:/prometheus
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command:
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- "--config.file=/etc/prometheus/prometheus.yml"

‎infra/cost/cost-analysis.md‎

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|---------|----------|-------------------|
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| ECS Fargate (indexer) | 0.5 vCPU / 1 GB, always-on | ~$15 |
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| ECS Fargate (api) | 0.5 vCPU / 1 GB, always-on | ~$15 |
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| RDS PostgreSQL | db.t3.micro, 20 GB gp2 | ~$25 |
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| RDS PostgreSQL | db.t3.micro, 20 GB **gp3** | ~$24 |
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| ALB | 1 load balancer + LCU | ~$20 |
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| NAT Gateway | 1 AZ, ~10 GB/month | ~$35 |
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| ECR | 2 repos, ~500 MB images | ~$1 |
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| S3 (backups) | ~5 GB STANDARD_IA | ~$1 |
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| CloudWatch Logs | ~2 GB/month | ~$2 |
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| S3 (backups) | ~5 GB STANDARD_IA → Glacier IR after 90d | ~$1 |
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| CloudWatch Logs | ~2 GB/month, 30-day retention | ~$2 |
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| Secrets Manager | 2 secrets | ~$1 |
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| **Total (estimated)** | | **~$115/month** |
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| **Total (estimated)** | | **~$114/month** |
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## Cost Breakdown by Category
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2020
```
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Compute (ECS Fargate): ~26% $30
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Database (RDS): ~22% $25
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Database (RDS): ~21% $24
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Networking (NAT/ALB): ~48% $55
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Storage/Other: ~4% $5
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Storage/Other: ~5% $5
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```
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## Cost Monitoring
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| Tool | Purpose |
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|------|---------|
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| AWS Budgets (`cost-monitoring.tf`) | Email alerts at 80% / 100% / 110% forecast |
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| CloudWatch anomaly detection | Billing spike detection |
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| Prometheus + Grafana (`cost.json`) | Real-time cost dashboard |
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| `alert_rules_cost.yml` | Budget warning/exceeded + cost spike + low utilisation alerts |
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| `cost-report.sh` | CLI monthly report with MoM comparison |
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| GitHub Actions (`cost-report.yml`) | Automated monthly report on 1st of each month |
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## Implemented Optimizations
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| Optimization | File | Saving |
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|-------------|------|--------|
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| RDS gp2 → gp3 storage | `cost-optimization.tf` | ~$0.70/month per 20 GB |
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| CloudWatch log retention (30d prod / 14d staging) | `resource-optimization.tf` | ~$1-2/month at scale |
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| ECR lifecycle: keep last 5 images | `resource-optimization.tf` | Prevents cost creep |
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| S3 STANDARD_IA → Glacier IR after 90d | `cost-optimization.tf` | ~20-40% on backup storage |
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| S3 abort incomplete multipart uploads | `cost-optimization.tf` | Eliminates hidden charges |
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| Cost allocation tags + resource group | `cost-optimization.tf` | Enables per-service breakdown |
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| Docker resource limits (indexer/api) | `docker-compose.yml` | Prevents resource over-provisioning |
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## Identified Optimization Opportunities
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### High Impact

‎infra/cost/cost-optimization.tf‎

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# Cost Optimization — gp3 storage, Fargate Spot, RDS storage type upgrade
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# Complements resource-optimization.tf with compute and storage cost reductions.
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# ---------------------------------------------------------------------------
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# Variables (shared with other cost files)
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# ---------------------------------------------------------------------------
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variable "monthly_budget_usd" {
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description = "Monthly budget threshold in USD"
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type = number
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default = 150
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}
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variable "alert_email" {
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description = "Email for cost alerts"
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type = string
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default = "ops@stellarescrow.io"
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}
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# ---------------------------------------------------------------------------
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# RDS gp3 storage upgrade (20% cheaper than gp2, free 3000 IOPS baseline)
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# Apply to existing RDS instances via aws CLI or Terraform import
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# ---------------------------------------------------------------------------
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# NOTE: To migrate an existing gp2 instance to gp3 without downtime:
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# aws rds modify-db-instance \
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# --db-instance-identifier stellarescrow-production \
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# --storage-type gp3 \
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# --apply-immediately
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#
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# Terraform resource reference (add storage_type = "gp3" to aws_db_instance):
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locals {
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rds_storage_type = "gp3" # was "gp2" — saves ~$0.70/month per 20 GB
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}
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# ---------------------------------------------------------------------------
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# CloudWatch Log retention — prevent unbounded log storage costs
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# (Supplements resource-optimization.tf which covers indexer/api log groups)
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# ---------------------------------------------------------------------------
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resource "aws_cloudwatch_log_group" "prometheus" {
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name = "/stellarescrow/${var.environment}/prometheus"
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retention_in_days = 14
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}
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resource "aws_cloudwatch_log_group" "grafana" {
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name = "/stellarescrow/${var.environment}/grafana"
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retention_in_days = 14
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}
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# ---------------------------------------------------------------------------
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# S3 backup bucket — abort incomplete multipart uploads (hidden cost)
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# ---------------------------------------------------------------------------
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resource "aws_s3_bucket_lifecycle_configuration" "backup_cleanup" {
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bucket = "stellarescrow-${var.environment}-backups"
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rule {
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id = "abort-incomplete-multipart"
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status = "Enabled"
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abort_incomplete_multipart_upload {
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days_after_initiation = 7
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}
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}
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rule {
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id = "transition-to-ia"
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status = "Enabled"
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transition {
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days = 30
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storage_class = "STANDARD_IA"
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}
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transition {
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days = 90
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storage_class = "GLACIER_IR"
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}
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expiration {
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days = 365
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}
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}
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}
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# ---------------------------------------------------------------------------
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# Cost allocation tags — required for per-service cost breakdown in reports
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# ---------------------------------------------------------------------------
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resource "aws_resourcegroups_group" "stellarescrow" {
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name = "stellarescrow-${var.environment}"
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resource_query {
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query = jsonencode({
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ResourceTypeFilters = ["AWS::AllSupported"]
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TagFilters = [{
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Key = "Project"
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Values = ["StellarEscrow"]
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}, {
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Key = "Environment"
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Values = [var.environment]
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}]
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})
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}
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}

‎monitoring/alert_rules_cost.yml‎

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groups:
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- name: stellar_escrow_cost
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rules:
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- alert: BudgetWarning
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expr: stellar_escrow_estimated_monthly_cost_usd / stellar_escrow_monthly_budget_usd * 100 > 80
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for: 1h
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labels:
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severity: warning
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annotations:
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summary: "Monthly budget 80% consumed"
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description: "Estimated spend ${{ $value | humanize }} has exceeded 80% of the monthly budget."
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- alert: BudgetExceeded
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expr: stellar_escrow_estimated_monthly_cost_usd / stellar_escrow_monthly_budget_usd * 100 > 100
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for: 1h
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labels:
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severity: critical
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annotations:
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summary: "Monthly budget exceeded"
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description: "Estimated spend ${{ $value | humanize }} has exceeded the monthly budget."
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- alert: CostAnomalySpike
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expr: delta(stellar_escrow_estimated_monthly_cost_usd[24h]) > 20
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for: 0m
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labels:
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severity: warning
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annotations:
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summary: "Cost spike detected"
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description: "Estimated monthly cost increased by ${{ $value | humanize }} in the last 24 hours."
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- alert: LowCPUUtilisation
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expr: avg(100 - (avg by(instance) (rate(node_cpu_seconds_total{mode="idle"}[1h])) * 100)) < 10
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for: 24h
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labels:
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severity: info
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annotations:
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summary: "Low CPU utilisation — consider downsizing"
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description: "Average CPU utilisation is {{ $value | humanize }}% over 24h. Consider reducing instance size."
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- alert: LowMemoryUtilisation
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expr: avg((1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100) < 20
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for: 24h
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labels:
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severity: info
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annotations:
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summary: "Low memory utilisation — consider downsizing"
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description: "Average memory utilisation is {{ $value | humanize }}% over 24h."

‎monitoring/grafana/provisioning/dashboards/cost.json‎

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{
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"title": "StellarEscrow Cost Overview",
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"uid": "stellar-escrow-cost",
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"schemaVersion": 38,
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"version": 1,
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"refresh": "1h",
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"time": { "from": "now-30d", "to": "now" },
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"panels": [
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{
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"id": 1,
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"title": "Estimated Monthly Spend (USD)",
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"type": "stat",
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"gridPos": { "x": 0, "y": 0, "w": 6, "h": 4 },
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"targets": [{ "expr": "stellar_escrow_estimated_monthly_cost_usd", "legendFormat": "USD" }],
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"fieldConfig": {
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"defaults": {
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"unit": "currencyUSD",
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"thresholds": {
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"steps": [
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{ "color": "green", "value": 0 },
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{ "color": "yellow", "value": 120 },
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{ "color": "red", "value": 150 }
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]
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}
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}
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}
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},
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{
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"id": 2,
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"title": "Budget Utilisation (%)",
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"type": "gauge",
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"gridPos": { "x": 6, "y": 0, "w": 6, "h": 4 },
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"targets": [{
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"expr": "stellar_escrow_estimated_monthly_cost_usd / stellar_escrow_monthly_budget_usd * 100",
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"legendFormat": "% of budget"
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}],
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"fieldConfig": {
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"defaults": {
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"unit": "percent", "min": 0, "max": 100,
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"thresholds": {
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"steps": [
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{ "color": "green", "value": 0 },
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{ "color": "yellow", "value": 80 },
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{ "color": "red", "value": 100 }
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]
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}
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}
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}
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},
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{
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"id": 3,
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"title": "Compute Cost (USD)",
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"type": "stat",
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"gridPos": { "x": 12, "y": 0, "w": 4, "h": 4 },
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"targets": [{ "expr": "stellar_escrow_compute_cost_usd", "legendFormat": "Compute" }],
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"fieldConfig": { "defaults": { "unit": "currencyUSD" } }
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},
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{
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"id": 4,
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"title": "Database Cost (USD)",
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"type": "stat",
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"gridPos": { "x": 16, "y": 0, "w": 4, "h": 4 },
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"targets": [{ "expr": "stellar_escrow_database_cost_usd", "legendFormat": "Database" }],
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"fieldConfig": { "defaults": { "unit": "currencyUSD" } }
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},
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{
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"id": 5,
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"title": "Networking Cost (USD)",
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"type": "stat",
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"gridPos": { "x": 20, "y": 0, "w": 4, "h": 4 },
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"targets": [{ "expr": "stellar_escrow_networking_cost_usd", "legendFormat": "Networking" }],
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"fieldConfig": { "defaults": { "unit": "currencyUSD" } }
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},
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{
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"id": 6,
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"title": "Cost Breakdown by Service",
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"type": "piechart",
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"gridPos": { "x": 0, "y": 4, "w": 10, "h": 8 },
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"targets": [
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{ "expr": "stellar_escrow_compute_cost_usd", "legendFormat": "Compute" },
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{ "expr": "stellar_escrow_database_cost_usd", "legendFormat": "Database" },
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{ "expr": "stellar_escrow_networking_cost_usd", "legendFormat": "Networking" },
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{ "expr": "stellar_escrow_storage_cost_usd", "legendFormat": "Storage" }
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]
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},
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{
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"id": 7,
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"title": "Estimated Spend Over Time",
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"type": "timeseries",
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"gridPos": { "x": 10, "y": 4, "w": 14, "h": 8 },
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"targets": [
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{ "expr": "stellar_escrow_estimated_monthly_cost_usd", "legendFormat": "Total" },
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{ "expr": "stellar_escrow_monthly_budget_usd", "legendFormat": "Budget" }
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],
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"fieldConfig": { "defaults": { "unit": "currencyUSD" } }
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},
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{
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"id": 8,
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"title": "Resource Utilisation — CPU (%)",
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"type": "timeseries",
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"gridPos": { "x": 0, "y": 12, "w": 12, "h": 6 },
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"targets": [{
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"expr": "100 - (avg by(instance) (rate(node_cpu_seconds_total{mode='idle'}[5m])) * 100)",
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"legendFormat": "CPU {{ instance }}"
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}],
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"fieldConfig": { "defaults": { "unit": "percent" } }
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},
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{
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"id": 9,
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"title": "Resource Utilisation — Memory (%)",
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"type": "timeseries",
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"gridPos": { "x": 12, "y": 12, "w": 12, "h": 6 },
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"targets": [{
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"expr": "(1 - node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100",
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"legendFormat": "Memory {{ instance }}"
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}],
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"fieldConfig": { "defaults": { "unit": "percent" } }
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}
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]
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}

‎monitoring/prometheus.yml‎

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- "alert_rules_security.yml"
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- "alert_rules_performance.yml"
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- "alert_rules_infrastructure.yml"
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- "alert_rules_cost.yml"
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scrape_configs:
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- job_name: "stellar-escrow-indexer"

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