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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=RTTdirect−RTTedgeLatency_Improvement = RTT_{direct} - RTT_{edge}Latency_Improvement=RTTdirect−RTTedge

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=Expected_LoadCapacity_Per_Instance×Safety_FactorInstances_Required = \frac{Expected_Load}{Capacity_Per_Instance} \times Safety_FactorInstances_Required=Capacity_Per_InstanceExpected_Load×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×Retention_Days×Replication_FactorStorage_Required = Daily_Growth \times Retention_Days \times Replication_FactorStorage_Required=Daily_Growth×Retention_Days×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)NAvailability = 1 - (1 - Component_Availability)^{N}Availability=1−(1−Component_Availability)N

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

DNS: GeoDNS routes to nearest region
Global: Anycast routes to nearest POP
Regional: Load balancer distributes across availability zones
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

Synchronous replication

Balances

Synchronous replication

User data

1 hour

Asynchronous replication

Analytics

24 hours

Daily backups

Failover Procedures

Automated Failover (for critical services):

Health check failure detected
Traffic rerouted to standby
Alerts sent to operations team
Post-mortem scheduled

Manual Failover (for non-critical services):

Issue identified
Operations team notified
Failover decision made
Procedure executed
Verification performed

Performance Optimization

Caching Strategy

Multi-Layer Caching:

CDN: Static assets (images, CSS, JS)
Edge: API responses (market data)
Application: Database queries
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: Capacityt+1=Capacityt×(1+Growth_Rate)×Safety_FactorCapacity_{t+1} = Capacity_t \times (1 + Growth_Rate) \times Safety_FactorCapacityt+1=Capacityt×(1+Growth_Rate)×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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Updated on: 10/03/2026

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