Load Modeling
Purpose
Model application load patterns and plan capacity to handle current and projected traffic. Produces a load model with growth projections, scaling trigger definitions, a benchmark plan for critical paths, and cost-at-scale estimates.
Inputs
- •Current traffic data (requests/sec, concurrent users, peak vs average)
- •Application architecture (services, databases, queues, external dependencies)
- •Growth projections or business targets (user growth rate, launch events, seasonal spikes)
- •Current infrastructure (instance types, database tier, CDN, serverless limits)
- •SLA requirements (uptime target, max acceptable latency, error rate budget)
Process
Step 1: Estimate User Load Patterns
Model the traffic shape:
- •Average concurrent users and requests per second
- •Peak-to-average ratio (daily peaks, weekly patterns, seasonal spikes)
- •Growth trajectory — linear, exponential, or event-driven (launches, campaigns)
- •Geographic distribution and timezone effects on peak timing
- •Read vs write ratio across the application
Step 2: Model Request Distribution Across Endpoints
Map traffic to specific system paths:
- •Identify the top 10 endpoints by request volume
- •Classify endpoints: read-heavy, write-heavy, compute-heavy, I/O-heavy
- •Estimate per-endpoint resource cost (CPU time, memory, DB queries, external calls)
- •Identify fan-out patterns (one user request triggers N downstream calls)
- •Map dependency chains — which downstream services does each endpoint touch?
Step 3: Identify Resource Bottlenecks at Scale
For each resource type, model where saturation occurs:
- •Database — connection pool exhaustion, query throughput ceiling, storage growth
- •Application servers — CPU saturation, memory pressure, thread/process limits
- •Network — bandwidth limits, connection limits, DNS resolution latency
- •External services — rate limits, timeout thresholds, quota exhaustion
- •Queues/workers — queue depth growth, processing lag, dead letter accumulation
Step 4: Define Horizontal and Vertical Scaling Triggers
Set thresholds that trigger scaling actions:
- •CPU utilization threshold for auto-scaling (e.g., scale out at 70% sustained for 3 min)
- •Memory utilization threshold
- •Request queue depth or response latency threshold
- •Database connection pool utilization threshold
- •Define scale-up and scale-down policies (cooldown periods, min/max instances)
- •Document manual scaling triggers for known events (launches, sales)
Step 5: Design Benchmark Suite for Critical Paths
Define load tests that validate capacity:
- •Identify the 5-10 most critical user journeys to benchmark
- •Define load profiles: ramp-up, sustained, spike, soak (long-running)
- •Specify success criteria per benchmark (p95 latency, error rate, throughput)
- •Plan for realistic test data volume and variety
- •Define the baseline benchmark to run before and after changes
Step 6: Plan Capacity Thresholds and Alerts
Design the monitoring and alerting layer:
- •Define warning thresholds (approaching limits) vs critical thresholds (at limits)
- •Map each threshold to an alert channel and response playbook
- •Set up dashboard panels for real-time capacity visibility
- •Plan capacity review cadence (weekly, monthly, quarterly)
- •Define runbook actions for each alert scenario
Step 7: Model Cost-at-Scale Projections
Estimate infrastructure cost as traffic grows:
- •Current monthly infrastructure cost breakdown
- •Cost per additional 1,000 concurrent users (compute, database, bandwidth, CDN)
- •Identify cost cliffs (tier upgrades, reserved instance thresholds)
- •Model 3-month, 6-month, and 12-month cost projections
- •Identify cost optimization opportunities (reserved instances, spot/preemptible, caching ROI)
Output Format
markdown
# Load Model: [Application Name] ## Traffic Profile | Metric | Current | 3-Month | 6-Month | 12-Month | |--------|---------|---------|---------|----------| | Concurrent Users | ... | ... | ... | ... | | Requests/sec (avg) | ... | ... | ... | ... | | Requests/sec (peak) | ... | ... | ... | ... | | Peak:Average Ratio | ... | ... | ... | ... | | Daily Data Growth | ... | ... | ... | ... | ## Endpoint Heat Map | Endpoint | Req/sec | Type | Resource Cost | Bottleneck Risk | |----------|---------|------|--------------|----------------| | ... | ... | ... | ... | ... | ## Scaling Trigger Table | Resource | Warning Threshold | Critical Threshold | Scale Action | Cooldown | |----------|------------------|-------------------|-------------|----------| | CPU | ... | ... | ... | ... | | Memory | ... | ... | ... | ... | | DB Connections | ... | ... | ... | ... | | Queue Depth | ... | ... | ... | ... | ## Bottleneck Analysis | Resource | Current Utilization | Saturation Point | Time to Saturation | Mitigation | |----------|-------------------|-----------------|-------------------|-----------| | ... | ... | ... | ... | ... | ## Benchmark Plan | Journey | Load Profile | Success Criteria (p95) | Tool | |---------|-------------|----------------------|------| | ... | ... | ... | ... | ## Cost Projections | Component | Current/mo | +3mo | +6mo | +12mo | |-----------|-----------|------|------|-------| | Compute | ... | ... | ... | ... | | Database | ... | ... | ... | ... | | CDN/Bandwidth | ... | ... | ... | ... | | **Total** | ... | ... | ... | ... | ## Capacity Budget | Resource | Current Headroom | Scaling Ceiling | Action Required By | |----------|-----------------|----------------|-------------------| | ... | ... | ... | ... |
Quality Checks
- • Traffic model includes peak, average, and growth projections
- • All critical endpoints are mapped with resource cost estimates
- • Bottleneck analysis covers database, compute, network, and external dependencies
- • Scaling triggers have specific numeric thresholds, not vague descriptions
- • Benchmark plan covers the critical user journeys with defined success criteria
- • Cost projections include at least 3 time horizons
- • Alerts map to specific response playbooks
- • Model accounts for both organic growth and planned events (launches, campaigns)