Infrastructure & Scalability

use.com's infrastructure is designed for global scale, high availability, and fault tolerance through cloud-native architecture, geographic distribution, and automated scaling.

Cloud-Native Architecture

Multi-Cloud Strategy: Primary deployment on AWS with failover capability to Azure/GCP

Benefits:

• Avoid vendor lock-in

• Leverage best-of-breed services

• Geographic coverage

• Disaster recovery

Kubernetes Orchestration: All services containerized and orchestrated via Kubernetes for:

• Automated scaling

• Self-healing

• Rolling updates

• Resource optimization

Geographic Distribution

Edge Points of Presence (POPs)

Global Coverage: 20+ edge locations across 6 continents

Regions:

• North America: US East, US West, Canada

• Europe: UK, Germany, France, Netherlands

• Asia: Singapore, Tokyo, Hong Kong, Mumbai

• LATAM: Brazil, Mexico

• MENA: UAE, Turkey

• Oceania: Australia

Anycast Routing: Users automatically routed to nearest POP

Latency Reduction: $Latency\_Improvement = RTT_{direct} - RTT_{edge}$

Example: User in Brazil:

• Direct to US: ~150ms

• Via Brazil POP: ~20ms

• Improvement: 130ms (87% reduction)

Data Residency

Compliance Requirement: Some jurisdictions require data to remain in-country

Implementation:

• EU data stored in EU data centers (GDPR compliance)

• User data replicated to local region

• Cross-border transfers minimized

Horizontal Scaling

Service-Level Scaling

Scaling Formula: $Instances\_Required = \frac{Expected\_Load}{Capacity\_Per\_Instance} \times Safety\_Factor$

Where Safety_Factor = 1.5 (50% headroom)

Auto-Scaling Triggers:

• CPU utilization > 70%

• Memory utilization > 80%

• Request queue depth > 1000

• Response time > 2× target

Example (API Service):

• Current load: 50,000 requests/second

• Capacity per instance: 5,000 requests/second

• Safety factor: 1.5

• Instances required: (50,000 / 5,000) × 1.5 = 15 instances

Database Scaling

Read Replicas: 5-10 read replicas per primary database

Sharding Strategy:

• User data: Sharded by user_id

• Trading data: Sharded by symbol

• Historical data: Sharded by time range

Capacity Planning: $Storage\_Required = Daily\_Growth \times Retention\_Days \times Replication\_Factor$

Example:

• Daily growth: 100 GB

• Retention: 2,555 days (7 years)

• Replication factor: 3

• Storage required: 100 × 2,555 × 3 = 766 TB

High Availability

Redundancy Model

N+2 Redundancy: Every critical service runs N+2 instances (can lose 2 and remain operational)

Availability Calculation: $Availability = 1 - (1 - Component\_Availability)^{N}$

Example (3 instances, 99.9% each): $Availability = 1 - (1 - 0.999)^3 = 1 - 0.000000001 = 99.9999999\%$

Failure Domains

Isolation Levels:

• Availability Zone: Separate data centers within region

• Region: Separate geographic regions

• Cloud Provider: Separate cloud providers

Deployment Strategy: Services distributed across 3 availability zones minimum.

Load Balancing

Multi-Layer Load Balancing:

1. DNS: GeoDNS routes to nearest region

2. Global: Anycast routes to nearest POP

3. Regional: Load balancer distributes across availability zones

4. Service: Kubernetes distributes across pods

Health Checks: Every 10 seconds, remove unhealthy instances within 30 seconds.

Disaster Recovery

Backup Strategy

Frequency:

• Hot data: Continuous replication

• Warm data: Hourly snapshots

• Cold data: Daily snapshots

Retention:

• Hourly: 7 days

• Daily: 30 days

• Weekly: 90 days

• Monthly: 7 years

Geographic Distribution: Backups stored in 3 separate regions.

Recovery Objectives

Recovery Time Objective (RTO): Maximum acceptable downtime

Service	RTO	Strategy
Trading	5 min	Hot standby
API	15 min	Automated failover
Deposits/Withdrawals	1 hour	Manual failover
Reporting	24 hours	Restore from backup

Recovery Point Objective (RPO): Maximum acceptable data loss

Data Type	RPO	Strategy
Trades	0	Synchronous replication
Balances	0	Synchronous replication
User data	1 hour	Asynchronous replication
Analytics	24 hours	Daily backups

Failover Procedures

Automated Failover (for critical services):

1. Health check failure detected

2. Traffic rerouted to standby

3. Alerts sent to operations team

4. Post-mortem scheduled

Manual Failover (for non-critical services):

1. Issue identified

2. Operations team notified

3. Failover decision made

4. Procedure executed

5. Verification performed

Performance Optimization

Caching Strategy

Multi-Layer Caching:

1. CDN: Static assets (images, CSS, JS)

2. Edge: API responses (market data)

3. Application: Database queries

4. Database: Query results

Cache Hit Ratio Target: > 90%

Example Impact:

• Cache miss: 50ms database query

• Cache hit: 1ms memory lookup

• Improvement: 98% faster

Database Optimization

Indexing Strategy:

• Primary keys: All tables

• Foreign keys: All relationships

• Query patterns: Analyzed monthly, indexes added

Query Optimization:

• Slow query log: Queries > 100ms logged

• Monthly review: Top 10 slow queries optimized

• Target: 95% of queries < 10ms

Network Optimization

Protocol Selection:

• WebSocket: Real-time market data (persistent connection)

• HTTP/2: API requests (multiplexing)

• gRPC: Internal service communication (efficient binary protocol)

Compression: Gzip/Brotli compression for all text data (70-90% size reduction).

Monitoring & Observability

Metrics Collection

Infrastructure Metrics:

• CPU, memory, disk, network utilization

• Request rates, error rates, latency

• Queue depths, cache hit rates

Business Metrics:

• Orders per second

• Trades per second

• Active users

• Trading volume

Collection Frequency: Every 10 seconds

Alerting

Alert Levels:

• Critical: Service down, SLO breach

• Warning: Approaching limits, degraded performance

• Info: Unusual patterns, capacity planning

Escalation:

• Critical: Immediate page to on-call engineer

• Warning: Slack notification

• Info: Email digest

Dashboards

Public Dashboards:

• System status

• Performance metrics (latency, uptime)

• Trading volume

Internal Dashboards:

• Infrastructure health

• Service dependencies

• Cost optimization

Capacity Planning

Growth Projections: $Capacity_{t+1} = Capacity_t \times (1 + Growth\_Rate) \times Safety\_Factor$

Planning Horizon: 12 months ahead

Review Frequency: Quarterly

Example:

• Current capacity: 100,000 orders/second

• Growth rate: 50% annually

• Safety factor: 1.5

• Required capacity (Year 1): 100k × 1.5 × 1.5 = 225,000 orders/second

Cost Optimization

Reserved Instances: 70% of baseline capacity on 1-3 year reservations (40-60% cost savings)

Spot Instances: 20% of capacity on spot instances for non-critical workloads (70-90% cost savings)

Auto-Scaling: 10% on-demand capacity for burst traffic

Cost Monitoring: Weekly reviews, monthly optimization initiatives.

Conclusion

use.com's infrastructure provides global scale, high availability, and fault tolerance through cloud-native architecture, geographic distribution, and automated scaling. By maintaining N+2 redundancy, sub-5-minute RTO for critical services, and 99.95%+ uptime, use.com delivers the reliability required for institutional-grade cryptocurrency trading.

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Related Sections:

• Product Architecture Overview

• Matching Engine & Order Book Design