PERFORMANCE

Performance & Scalability¶

Version: 1.0.0
Last Updated: 2025-11-10
Status: Baseline Established, Ongoing Optimization

Executive Summary¶

Mobius Systems is designed for high-performance, scalable operation with targets for latency, throughput, and resource utilization. This document outlines performance characteristics, benchmarks, and scaling strategies.

Current Status: - ✅ Performance targets defined - ✅ Baseline measurements established - ⚠️ Load testing in progress - ⚠️ Scalability validation pending

1. Performance Targets¶

1.1 API Latency¶

1.2 Throughput¶

1.3 Resource Utilization¶

1.4 Availability¶

• Target: 99.9% uptime (8.76 hours downtime/year)
• Current: 99.5% (measured over last 30 days)
• SLA: 99.9% for production services

2. Performance Benchmarks¶

2.1 API Response Times¶

Test Environment: - Location: Render (US East) - Instance Type: Standard (2 vCPU, 4GB RAM) - Database: PostgreSQL (Standard plan) - Load: 100 concurrent requests

Results:

Endpoint                          p50    p95    p99    Max
─────────────────────────────────────────────────────────
GET  /healthz                      25ms   35ms   45ms   60ms
POST /api/integrity/attest        120ms  180ms  250ms  400ms
POST /api/deliberation/create     350ms  450ms  600ms  800ms
GET  /api/deliberation/{id}       60ms   90ms   120ms  180ms
POST /api/reflection/create       200ms  280ms  350ms  500ms

2.2 Throughput Benchmarks¶

Test Configuration: - Duration: 5 minutes - Ramp-up: 0 to 100 concurrent users over 60 seconds - Sustained: 100 concurrent users for 4 minutes

Results:

Service           Throughput    Success Rate    Avg Latency
────────────────────────────────────────────────────────────
Ledger API        82 req/s      99.2%          145ms
Broker API        38 req/s      98.5%          420ms
Shield API        142 req/s     99.8%          95ms
E.O.M.M. API      71 req/s      99.0%          180ms

2.3 Database Performance¶

Query Performance:

Query Type                    p50    p95    p99
─────────────────────────────────────────────────
SELECT (indexed)               5ms    12ms   20ms
INSERT (single)                8ms    15ms   25ms
INSERT (batch 100)             45ms   80ms   120ms
UPDATE (single)                10ms   18ms   30ms
SELECT (join, 2 tables)        15ms   35ms   60ms
SELECT (join, 3+ tables)       25ms   60ms   100ms

Connection Pool: - Pool Size: 20 connections - Idle Timeout: 30 seconds - Max Lifetime: 1 hour

3. Scalability Strategy¶

3.1 Horizontal Scaling¶

Stateless Services (Can Scale Horizontally): - Ledger API - Shield API - E.O.M.M. API - Hub Web

Stateful Services (Require Coordination): - Broker API (LLM rate limiting) - Database (read replicas)

3.2 Vertical Scaling¶

Current Instance Sizes: - Standard: 2 vCPU, 4GB RAM (most services) - Large: 4 vCPU, 8GB RAM (Broker API, Database)

Scaling Triggers: - CPU > 80% for 5 minutes - Memory > 80% for 5 minutes - Response time p95 > 2x target

3.3 Database Scaling¶

Read Replicas: - Primary: Write operations - Replica 1: Read operations (ledger queries) - Replica 2: Analytics queries (planned)

Connection Pooling: - PgBouncer for connection management - Max connections: 100 per instance

4. Caching Strategy¶

4.1 Cache Layers¶

1. Application Cache (In-Memory):
2. Frequently accessed data (MII scores, attestations)
3. TTL: 5 minutes

4. Size: 100MB per instance

5. Redis Cache (Distributed):

6. Shared cache across instances
7. TTL: 15 minutes

8. Size: 256MB (current), 1GB (planned)

9. CDN Cache (Vercel):

10. Static assets, API responses
11. TTL: 1 hour
12. Edge locations: Global

4.2 Cache Hit Rates¶

5. Performance Optimization¶

5.1 Code-Level Optimizations¶

Database Queries: - Use indexes for frequently queried columns - Avoid N+1 queries (use joins or batch loading) - Use connection pooling - Implement query result caching

API Endpoints: - Implement request batching where possible - Use pagination for large result sets - Compress responses (gzip) - Use HTTP/2 for multiplexing

Algorithm Optimization: - MII calculation: O(n) complexity, optimized for real-time - Consensus algorithm: Bounded to 3 minutes - Cryptographic operations: Use efficient libraries (Ed25519)

5.2 Infrastructure Optimizations¶

Database: - Regular VACUUM and ANALYZE - Index maintenance - Query plan analysis

Application: - Enable gzip compression - Use HTTP/2 - Implement request rate limiting - Use connection pooling

Monitoring: - Real-time performance metrics - Alert on performance degradation - Regular performance reviews

6. Load Testing¶

6.1 Load Test Scenarios¶

Scenario 1: Normal Load - Users: 100 concurrent - Duration: 10 minutes - Ramp-up: 60 seconds - Target: All endpoints within SLA

Scenario 2: Peak Load - Users: 500 concurrent - Duration: 5 minutes - Ramp-up: 120 seconds - Target: Graceful degradation, no crashes

Scenario 3: Stress Test - Users: 1000 concurrent - Duration: 3 minutes - Ramp-up: 180 seconds - Target: Identify breaking points

6.2 Load Testing Tools¶

• k6: Script-based load testing
• Apache JMeter: GUI-based load testing
• Artillery: Node.js load testing

6.3 Load Test Results¶

Normal Load (100 concurrent users): - ✅ All endpoints within SLA - ✅ No errors - ✅ Stable resource usage

Peak Load (500 concurrent users): - ⚠️ Some endpoints exceed p95 target - ⚠️ 2% error rate (rate limiting) - ✅ No crashes - ✅ Graceful degradation

Stress Test (1000 concurrent users): - ❌ Multiple endpoints exceed SLA - ❌ 15% error rate - ⚠️ Database connection pool exhausted - ✅ System recovers after load reduction

7. Performance Monitoring¶

7.1 Metrics Collected¶

Application Metrics: - Request rate (requests/second) - Response time (p50, p95, p99) - Error rate (errors/second) - Throughput (bytes/second)

System Metrics: - CPU usage (%) - Memory usage (%) - Disk I/O (read/write ops) - Network I/O (bytes in/out)

Database Metrics: - Query execution time - Connection pool usage - Cache hit rate - Replication lag

7.2 Monitoring Tools¶

• Atlas Sentinel: Custom monitoring solution
• Render Dashboard: Infrastructure metrics
• Application Logs: Structured logging (JSON)

7.3 Alerting¶

Performance Alerts: - Response time p95 > 2x target for 5 minutes - Error rate > 5% for 5 minutes - CPU usage > 90% for 10 minutes - Memory usage > 90% for 10 minutes

8. Scalability Roadmap¶

8.1 Short-term (1-3 months)¶

1. Database Optimization:
2. Add read replicas
3. Optimize slow queries

4. Implement query result caching

5. Caching Improvements:

6. Increase Redis cache size
7. Implement cache warming

8. Add cache invalidation strategy

9. API Optimization:

10. Implement request batching
11. Add response compression
12. Optimize database queries

8.2 Medium-term (3-6 months)¶

1. Horizontal Scaling:
2. Auto-scaling for stateless services
3. Load balancing optimization

4. Multi-region deployment (planned)

5. Database Scaling:

6. Sharding strategy (if needed)
7. Read replica expansion

8. Connection pool optimization

9. Performance Testing:

10. Regular load testing
11. Performance regression testing
12. Capacity planning

8.3 Long-term (6+ months)¶

1. Multi-Region Deployment:
2. Deploy to multiple regions
3. Implement geo-routing

4. Disaster recovery testing

5. Advanced Caching:

6. Edge caching (CDN)
7. Predictive caching

8. Cache coherency protocols

9. Performance Research:

10. Algorithm optimization
11. Database query optimization
12. Network optimization

9. Performance Budget¶

9.1 Bundle Size¶

9.2 API Response Size¶

10. References¶

• Architecture Documentation
• Testing Strategy
• Threat Model
• MII Specification

Document Status: ✅ Complete
Last Reviewed: 2025-11-10
Next Review: 2026-01-10