--- name: capacity-planning-infrastructure description: Plan and manage IT infrastructure capacity including compute, storage, network, and cloud resources. Use when forecasting infrastructure needs, right-sizing resources, planning data center expansion, optimizing cloud capacity, conducting capacity reviews, or preventing resource exhaustion. Triggers on phrases like "capacity planning", "infrastructure capacity", "resource forecasting", "right-sizing", "capacity management", "growth planning", "infrastructure scaling", "resource optimization", "capacity threshold", "infrastructure roadmap". --- # IT Infrastructure Capacity Planning Forecast, plan, and optimize IT infrastructure capacity to support current and future business needs. ## Workflow 1. Establish capacity baselines: current utilization for compute, storage, network, and applications. 2. Collect historical data: 12-month utilization trends, growth rates, seasonal patterns. 3. Forecast demand: business-driven (user growth, transaction volume) and technology-driven (new initiatives). 4. Analyze capacity gaps: compare projected demand against current and planned capacity. 5. Develop capacity plan: procurement timeline, budget, implementation schedule, risk mitigation. 6. Implement right-sizing: optimize current resources before expanding (eliminate waste first). 7. Monitor capacity thresholds: automated alerts at 70%, 80%, 90% utilization. 8. Conduct quarterly capacity reviews: update forecasts, validate assumptions, adjust plans. 9. Report capacity posture: executive dashboard, procurement recommendations, risk assessment. 10. Execute capacity improvements: procurement, deployment, configuration, validation. ## Compute Capacity Planning ``` COMPUTE CAPACITY FRAMEWORK ============================ Current State Assessment: On-Premises Servers: Total physical servers: [X] Average utilization: CPU [Y]%, Memory [Z]% Server density: [X] VMs per physical host (average) Host capacity headroom: [X] additional VMs per host Servers at > 80% utilization: [X] (need attention) Idle/underutilized servers: [X] (candidates for consolidation) End-of-life within 12 months: [X] (need replacement budget) Virtual Machines: Total VMs: [X] production, [Y] non-production Average VM specs: [X] vCPU, [Y] GB RAM Over-provisioned VMs: [X] (CPU < 10% for 30 days) Under-provisioned VMs: [Y] (CPU > 80% for 30 days) Snapshot count: [X] (clean up old snapshots) Unused VMs: [X] (powered off > 30 days) Cloud Compute: Total instances: [X] (AWS EC2 + Azure VMs + GCP GCE) Average utilization: CPU [Y]%, Memory [Z]% Reserved instances: [X]% of total (target: > 70% for stable workloads) Spot instances: [X]% of total (for fault-tolerant workloads) Idle instances: [X] (no network traffic for 7+ days) Zombie instances: [X] (running but no attached EBS/network) Compute Growth Projection: Demand drivers: User growth: [X]% per quarter → [Y] additional VMs/instances Application growth: [X] new services planned → [Y] additional compute Batch/ETL growth: [X]% data growth → [Y] additional compute Peak factor: [X]x average (handle 95th percentile, not average) 12-month projection: Quarter Current VMs Projected Growth Headroom Action Required ──────── ─────────── ───────── ──────── ────────── ─────────────────── Q1 (now) 500 500 — 15% None Q2 500 550 +10% 10% Monitor Q3 550 610 +11% 4% ⚠️ Plan expansion Q4 610 670 +10% -2% 🔴 Execute expansion Action: Procure 3 new hosts (Q2 delivery) or increase cloud budget by 20% Right-Sizing Recommendations: Over-provisioned (downsize): VM-001: 8 vCPU, 32 GB RAM → 4 vCPU, 16 GB RAM (CPU avg: 8%, Mem avg: 15%) VM-002: 4 vCPU, 16 GB RAM → 2 vCPU, 8 GB RAM (CPU avg: 5%, Mem avg: 20%) VM-003: 4 vCPU, 8 GB RAM → 2 vCPU, 4 GB RAM (CPU avg: 12%, Mem avg: 30%) Estimated savings: 12 vCPU, 32 GB RAM → redeploy to other workloads Under-provisioned (upsize): VM-010: 2 vCPU, 4 GB RAM → 4 vCPU, 8 GB RAM (CPU avg: 85%, Mem avg: 90%) VM-011: 2 vCPU, 4 GB RAM → 4 vCPU, 8 GB RAM (CPU avg: 78%, Mem avg: 85%) Action: immediate resize to prevent performance degradation Consolidation opportunities: 10 small VMs (1 vCPU each, < 10% CPU) → 1 medium VM (4 vCPU) with containers Savings: 9 physical/VM hosts reclaimed; reduced management overhead ``` ## Storage Capacity Planning ``` STORAGE CAPACITY FRAMEWORK ============================ Current Storage Inventory: On-Premises Storage: SAN arrays: [X] arrays, [Y] TB total raw, [Z] TB usable NAS/file servers: [X] TB total Tape library: [X] TB (for backup archive) Average utilization: [X]% Growth rate: [Y] TB per month Cloud Storage: Block storage (EBS/Managed): [X] TB, $[Y]/month Object storage (S3/Blob): [X] TB, $[Y]/month File storage (EFS/DFS): [X] TB, $[Y]/month Database storage: [X] TB Backup storage: [X] TB Archive storage (Glacier): [X] TB Total cloud storage: [X] TB, $[Y]/month Storage by tier: Hot (frequent access): [X] TB — $[Y]/TB/month Warm (occasional access): [X] TB — $[Y]/TB/month Cool (rare access): [X] TB — $[Y]/TB/month Archive (annual access): [X] TB — $[Y]/TB/month Storage Growth Analysis: Historical growth (last 12 months): Month Total TB Growth TB Growth % Cost/Month Trend ────── ────────── ────────── ───────── ──────────── ───── Jan 500 +8 +1.6% $5,000 ↑ Feb 508 +10 +2.0% $5,080 ↑ Mar 518 +12 +2.3% $5,180 ↑ Apr 530 +15 +2.9% $5,300 ↑↑ ... Dec 650 +20 +3.2% $6,500 ↑↑ Growth rate: accelerating (+0.5% per month) Projected 12-month: 650 TB → 950 TB (+46% growth) Alert: 80% threshold reached in [X] months Storage Optimization: Data lifecycle policies: 0–90 days: Hot storage (SSD/NVMe) — active data 90–180 days: Warm storage (S3 Standard-IA) — reduced access 180–365 days: Cool storage (S3 Glacier Flexible) — archive 365+ days: Deep archive (S3 Glacier Deep Archive) — compliance Deduplication and compression: Backups: 3:1 to 10:1 dedup ratio typical VM images: 2:1 to 5:1 dedup ratio Logs: 5:1 to 10:1 compression ratio Estimated savings: 40–60% with proper lifecycle management Wasted storage: Unattached volumes: [X] TB ($[Y]/month wasted) Old snapshots: [X] TB ($[Y]/month wasted) Empty buckets/containers: [X] buckets Duplicate files: [X] TB (identify with dedup tools) Oversized disks: [X] disks > 80% free space Total reclaimable: [X] TB ($[Y]/month savings) Storage alerts: 70% utilization: WARNING — begin planning expansion 80% utilization: PLANNING — submit procurement request 90% utilization: CRITICAL — immediate action required 95% utilization: EMERGENCY — emergency procurement; may impact operations ``` ## Network Capacity Planning ``` NETWORK CAPACITY FRAMEWORK ============================ WAN Capacity: Current WAN links: Primary ISP: [X] Gbps, average [Y]%, peak [Z]% Secondary ISP: [X] Gbps, average [Y]%, peak [Z]% Backup (4G/5G): [X] Mbps (emergency only) Direct Connect: [X] Gbps (cloud interconnect) Utilization trend: Month Avg Util Peak (95th) Growth Status ────── ────────── ─────────── ───────── ──────── Jan 45% 65% baseline 🟢 Apr 52% 72% +2%/mo 🟡 Watch Jul 60% 80% +2.7%/mo 🟡 Plan Oct 68% 88% +2.7%/mo 🔴 Act Projected: 80% average utilization in [X] months Recommendation: upgrade to [Y] Gbps or add secondary [Z] Gbps link Budget: $[X]/month for upgraded link LAN/Datacenter Network: Core switch capacity: Switching capacity: [X] Tbps Port density: [X] ports (1G/10G/25G/40G/100G) Utilization: [X]% of switching capacity Available ports: [X] (for new servers) Top-of-rack (ToR) switches: Switches: [X] × 48-port 10G/25G switches Uplinks: [X] × 40G/100G to core Uplink utilization: [X]% (alert > 70%) Available ports: [X] per switch average Network growth drivers: New servers: [X] planned → [Y] additional switch ports needed Bandwidth growth: [X]% per quarter (video, backups, replication) Cloud traffic: [X] Gbps to cloud (growing [Y]% per quarter) Wireless Capacity: Access points: [X] APs covering [Y] sq ft Clients per AP: Average [Z] (target: < 30 for performance) Channel utilization: Average [X]% (target: < 70%) Growth: [X] new clients per quarter Upgrade plan: Wi-Fi 6/6E/7 upgrade in [X] months ``` ## Cloud Capacity Planning ``` CLOUD CAPACITY AND COST FORECASTING ===================================== Current Cloud Spend: Provider Monthly Annual YoY Growth % of Budget Trend ───────── ────────── ──────── ──────────── ───────────── ───── AWS $45,000 $540,000 +25% 52% ↑↑ Azure $25,000 $300,000 +15% 28% ↑ GCP $10,000 $120,000 +40% 11% ↑↑↑ Other $5,000 $60,000 +10% 9% → ──────────────────────────────────────────────────────────────────────── Total $85,000 $1,020,000 +22% 100% ↑ Cost by service category: Category Monthly % of Total Growth Rate Optimization ────────────────── ────────── ──────────── ──────────── ────────────── Compute (EC2/VMs) $30,000 35% +20% Right-size, RI Database (RDS/etc) $15,000 18% +25% Read replicas Storage (S3/Blob) $10,000 12% +30% Lifecycle policies Network (data xfer) $8,000 9% +35% VPC endpoints Container (EKS/etc) $7,000 8% +40% Auto-scaling Managed Services $8,000 9% +15% Evaluate build vs buy Other $7,000 9% +10% Review quarterly Capacity projection (12 months): Quarter Compute Storage Network Total Headroom Action ──────── ───────── ───────── ───────── ───────── ────────── ──────── Q1 $30K $10K $8K $48K 44% — Q2 $33K $11K $9K $53K 38% — Q3 $36K $12K $10K $58K 32% ⚠️ Q4 $40K $14K $12K $64K 24% 🔴 Budget cap: $65K/month (current) Gap: Q4 projected at $64K — near budget cap Action: implement cost optimization initiatives by Q2 Potential savings: $10K–$15K/month with RI + right-sizing + lifecycle Cloud capacity optimization: Reserved Instances / Savings Plans: Current coverage: [X]% of eligible spend Target coverage: > 75% for stable workloads Savings potential: $[X]/month (15–30% discount) Implementation: commit to 1-year or 3-year terms Auto-scaling: Current: [X] instances running 24/7 With auto-scaling: [Y] instances during off-hours Savings: [X-Y] instances × $[Z]/month Right-sizing: Over-provisioned: [X] instances (CPU < 20% for 30 days) Right-size candidates: [X] instances → smaller types Savings: $[Y]/month Spot instances: Eligible workloads: [X]% (batch, CI/CD, testing, stateless) Savings: 60–90% vs. on-demand Risk: instance interruption (handle gracefully) ``` ## Integration Points - **Datadog / New Relic**: Real-time infrastructure monitoring; utilization trends; forecasting; anomaly detection - **CloudHealth / Cloudability**: Cloud cost management; rightsizing recommendations; reserved instance optimization; anomaly detection - **ServiceNow Capacity Management**: Integrated with CMDB; trend analysis; threshold management; forecasting - **VMware vRealize Operations**: vSphere capacity planning; predictive analytics; right-sizing; what-if scenarios - **Azure Capacity Optimization / AWS Compute Optimizer**: Cloud-native right-sizing recommendations; utilization analysis - **SolarWinds Storage Resource Monitor**: Storage capacity tracking; growth forecasting; threshold alerting - **NetBox / Device42**: Infrastructure inventory; capacity tracking; documentation - **Custom dashboards** (Grafana, Power BI): Capacity visualization; trend analysis; executive reporting ## Edge Cases - **Seasonal capacity spikes** (e-commerce holidays, quarterly reporting, tax season): Plan for peak capacity (not average); implement auto-scaling for cloud; maintain standby capacity for on-prem; pre-provision 2–4 weeks before peak; decommission/release after peak; monitor and right-size during peak - E-commerce: Black Friday/Cyber Monday = 3–10x normal traffic; pre-scale 2 weeks before; maintain for 1 week after - Financial: month-end close = 5–10x batch processing; schedule batch jobs to distribute load - Tax season: 3–5x normal for financial systems; additional capacity February–April - **Rapid growth / scaling startup** (10x user growth in 12 months): Cloud-first architecture for elasticity; auto-scaling from day one; capacity planning monthly (not quarterly); budget with 50–100% growth buffer; implement FinOps practices early; hire DevOps/SRE for infrastructure automation - Architecture: microservices, containers, auto-scaling, serverless where possible - Monitoring: real-time capacity dashboards; automated scaling policies; cost anomaly detection - Budget: 3x projected spend as budget ceiling; monthly budget review - **Capacity constraints with budget freeze** (business won't approve new spending): Optimize existing resources aggressively: consolidate servers, implement deduplication, right-size all resources, eliminate waste, negotiate cloud discounts; prioritize critical workloads; defer non-essential projects; implement strict chargeback/showback - Quick wins: terminate idle resources (save 10–20% cloud spend), consolidate underutilized servers (save 20–30%), implement storage lifecycle (save 30–50% storage) - Communication: show business impact of capacity shortage (revenue at risk, SLA breach) - **Multi-cloud capacity** (workloads across AWS, Azure, GCP): Unified capacity dashboard across providers; consistent monitoring tool; cross-cloud load balancing; avoid vendor lock-in for capacity flexibility; negotiate enterprise agreements with each provider; consolidate billing and reporting - Tooling: CloudHealth, Cloudability, or custom Terraform-based dashboard - Strategy: primary cloud for core workloads; secondary for disaster recovery and cost optimization - Cost: multi-cloud adds 10–20% management overhead; ensure business justification - **Edge/IoT capacity** (thousands of edge devices): Lightweight monitoring agents; edge aggregation (reduce cloud data transfer); predictive maintenance (replace before failure); over-the-air update management; device lifecycle automation; battery life consideration for monitoring frequency - Architecture: edge gateway per 100 devices; gateway handles local processing and aggregation - Monitoring: heartbeat every 5–60 minutes; event-driven reporting for anomalies - Capacity: plan for 10–20% device replacement rate per year; maintain spare inventory - **Disaster recovery capacity** (standby capacity for failover): Pilot light (minimal capacity, scale on failover) vs. warm standby (partial capacity) vs. hot standby (full capacity); cost tradeoff: hot standby = 50–100% of production cost; pilot light = 10–20% - RTO/RPO alignment: shorter RTO requires more standby capacity - Testing: quarterly failover test (validate capacity and procedures) - Cloud advantage: cloud DR significantly cheaper than on-prem DR (pay for what you use)