Scalability Planning

Purpose

Design the system to handle growth in users, data volume, and traffic while maintaining performance, availability, and cost efficiency.

When to Use

• Initial architecture design
• Anticipating growth
• Performance issues
• Capacity planning

Prerequisites

• NFR scalability targets
• Growth projections
• Current/expected load patterns
• Budget constraints

Process

Step 1: Define Scalability Requirements

From NFRs:
- Peak concurrent users
- Requests per second
- Data growth rate
- Response time SLAs
- Geographic distribution

Step 2: Identify Scaling Dimensions

Dimensions to scale:
- Compute: More/larger instances
- Database: Read replicas, sharding
- Storage: Tiered storage, CDN
- Network: Load balancing, CDN
- Background: Worker scaling

Step 3: Choose Scaling Strategies

Strategies:
- Vertical: Bigger machines (limit exists)
- Horizontal: More machines (preferred)
- Auto-scaling: Demand-based
- Caching: Reduce backend load
- CDN: Geographic distribution
- Async: Queue-based processing

Step 4: Design for Scale

Design patterns:
- Stateless services (enable horizontal)
- Connection pooling
- Caching layers
- Database read replicas
- Message queues for async
- Event-driven architecture

Step 5: Plan Scaling Triggers

Auto-scaling rules:
- CPU > 70% → add instance
- Response time > 2s → add instance
- Queue depth > 100 → add worker
- Min/max bounds defined

Inputs

Input	Type	Required	Description
nfr_matrix	Markdown	Yes	Scale targets
growth_projections	JSON	Optional	Expected growth
current_metrics	JSON	Optional	Baseline

Outputs

Output	Type	Description
scalability_plan.md	Markdown	Scaling strategy
auto_scaling_config	YAML	Scaling rules
capacity_model.md	Markdown	Capacity calculations

StudyAbroad-Specific Considerations

• Peak load during application deadlines
• Seasonal patterns (enrollment periods)
• Document uploads spike with deadlines
• University API rate limits constrain scaling

Integration Points

• DevOps Agent: Auto-scaling implementation
• Operations Agent: Capacity monitoring

Examples

Scalability Plan - StudyAbroad-v1

TARGETS:
- Normal: 5,000 concurrent users
- Peak: 25,000 concurrent users (deadline days)
- API: 500 req/sec normal, 2,000 peak
- Data: 100GB growth/month

SCALING STRATEGY:

Application Layer:
- Container-based (ECS Fargate)
- Horizontal scaling 2-10 instances
- Auto-scale on CPU > 70% or response > 2s
- Stateless design (session in Redis)

Database Layer:
- RDS PostgreSQL with read replica
- Connection pooling (PgBouncer)
- Read replica for search queries
- Consider read replicas in EU-WEST-2

Caching Layer:
- Redis for sessions
- Redis for university/program cache (1hr TTL)
- CDN for static assets

Storage Layer:
- S3 for documents
- CloudFront CDN
- Lifecycle policies for old documents

Background Processing:
- SQS queues for async tasks
- Email queue
- Document processing queue
- Auto-scaling workers 1-5

AUTO-SCALING RULES:

API Service:
- Min: 2 instances
- Max: 10 instances
- Scale up: CPU > 70% for 3 min
- Scale up: Response p95 > 2s
- Scale down: CPU < 30% for 10 min

Workers:
- Min: 1 instance
- Max: 5 instances
- Scale up: Queue depth > 100
- Scale down: Queue empty for 10 min

PEAK PREPARATION:
Before application deadlines:
1. Pre-scale to 5 API instances
2. Pre-scale to 3 workers
3. Warm database connection pools
4. Clear non-essential caches
5. Enable enhanced monitoring

Validation

• Meets NFR scale targets
• Auto-scaling rules defined
• Peak handling planned
• Cost implications understood
• Bottlenecks identified