EuConquisto Composer MCP

--- document: Resource Constraints Assessment Analysis version: 1.0.0 status: active author: Claude Desktop created: 2025-06-27 last_updated: 2025-06-27 related_task: TASK-003 phase: Phase 1 - Scope and Planning --- # Resource Constraints Assessment Analysis - Phase 1 ## 🎯 Task Scope Understanding ### Primary Objective Determine precise server resource requirements and deployment constraints for ECCComposerMCP browser automation to inform architectural decisions and scalability planning. ### Critical Success Factors - Define minimum and recommended server specifications - Establish browser automation resource patterns - Identify deployment architecture constraints - Create resource monitoring and alerting strategy ## 📊 Technical Analysis ### 1. Browser Automation Resource Requirements #### Chromium Instance Analysis **Base Resource Consumption**: - **Memory per instance**: 100-200MB typical, up to 500MB with complex content - **CPU overhead**: 10-15% per active instance (baseline) - **GPU acceleration**: Optional but recommended for video content - **Disk I/O**: Moderate for cache, high during initial startup **Resource Scaling Patterns**: ``` Single User Session: ├── Browser startup: ~150MB memory, 2-3 seconds CPU spike ├── Navigation: ~50MB additional per complex page ├── DOM manipulation: ~25MB for large compositions └── Cleanup: Full memory release (if properly handled) Concurrent Sessions (N users): ├── Memory: N × (150MB base + 75MB average content) ├── CPU: N × 10% + startup spikes ├── Network: N × bandwidth per session └── File handles: N × 50-100 open handles ``` #### Resource Bottlenecks Identified 1. **Memory Leaks**: Current implementation lacks cleanup strategies 2. **Startup Overhead**: Fresh browser instances for each operation 3. **No Instance Pooling**: Linear scaling without optimization 4. **Session Management**: No connection limits or queuing ### 2. Server Environment Constraints Analysis #### Minimum Specifications (Single User) ```yaml CPU: 2 cores (2.0GHz+) Memory: 4GB RAM Storage: 20GB SSD (browser cache + application) Network: 10Mbps stable connection OS: Linux/Ubuntu 20.04+ or containerized environment ``` #### Recommended Specifications (5-10 Concurrent Users) ```yaml CPU: 4-8 cores (2.5GHz+) Memory: 8-16GB RAM Storage: 50GB SSD NVMe Network: 50Mbps with low latency (<50ms to EuConquisto) OS: Ubuntu 22.04 LTS with Docker support Additional: Dedicated GPU for video processing (optional) ``` #### Production-Scale Specifications (25+ Concurrent Users) ```yaml CPU: 16+ cores (3.0GHz+) or multi-instance deployment Memory: 32GB+ RAM with monitoring Storage: 100GB+ SSD with backup Network: 100Mbps+ with redundancy Load Balancer: Required for distribution Monitoring: Comprehensive resource tracking ``` ### 3. Deployment Architecture Constraints #### Containerization Requirements **Docker Configuration**: ```dockerfile # Resource limits needed memory: 2GB per container instance cpu: 1.5 cores per container shared_memory: 2GB (for browser automation) ``` **Kubernetes Scaling**: - **Horizontal Pod Autoscaler**: CPU > 70% or Memory > 80% - **Resource Quotas**: Prevent resource exhaustion - **Node Affinity**: Browser automation on dedicated nodes #### Environment-Specific Constraints 1. **Cloud Providers**: - AWS: t3.large minimum, c5.xlarge recommended - GCP: n1-standard-2 minimum, n1-standard-4 recommended - Azure: Standard_D2s_v3 minimum, Standard_D4s_v3 recommended 2. **On-Premises**: - Physical hardware with dedicated resources - Network isolation for security - Backup power and cooling considerations ### 4. Performance Targets & Thresholds #### Response Time Targets ```yaml Composition Creation: <30 seconds (95th percentile) Browser Startup: <5 seconds DOM Navigation: <10 seconds Error Recovery: <15 seconds Session Cleanup: <3 seconds ``` #### Resource Usage Thresholds ```yaml Memory Utilization: <80% sustained CPU Utilization: <75% average, <90% peak Disk I/O: <70% sustained Network Bandwidth: <60% sustained Browser Instances: Max 10 per server without optimization ``` #### Monitoring Requirements - **Real-time**: CPU, memory, active browser instances - **Application**: Request rates, success rates, error rates - **Business**: Composition creation times, user satisfaction - **Alerting**: Threshold breaches, failed deployments, resource exhaustion ## 🚧 Current Implementation Gaps ### Critical Issues Identified 1. **No Resource Pooling**: Fresh browser instance per operation 2. **Memory Leak Risk**: Missing cleanup in error scenarios 3. **No Concurrency Limits**: Unlimited concurrent operations 4. **Missing Monitoring**: No resource usage tracking 5. **Single Point of Failure**: No load distribution ### Immediate Fixes Required ```javascript // Example resource pooling needed class BrowserPool { maxInstances: 5, idleTimeout: 300000, // 5 minutes cleanup: automatic, healthCheck: enabled } ``` ## 📋 Phase 2 Implementation Plan ### Deliverables Defined 1. **Resource Requirements Document** - Server specifications matrix (min/recommended/production) - Container resource allocation guidelines - Cloud provider sizing recommendations 2. **Browser Automation Architecture** - Instance pooling implementation - Resource cleanup strategies - Connection queuing system - Health monitoring integration 3. **Deployment Architecture** - Containerization strategy (Docker/Kubernetes) - Auto-scaling policies and triggers - Load balancing configuration - Monitoring and alerting setup 4. **Performance Optimization Plan** - Resource usage optimization - Caching strategies - Connection management - Error recovery mechanisms ### Implementation Priorities **Phase 2A (Week 1)**: Resource pooling and cleanup **Phase 2B (Week 2)**: Monitoring and alerting **Phase 2C (Week 3)**: Containerization and deployment **Phase 2D (Week 4)**: Performance optimization and testing ## 🎯 Success Criteria for Phase 2 ### Technical Deliverables - [ ] Complete resource requirements documentation - [ ] Browser automation architecture design - [ ] Deployment strategy with container specifications - [ ] Monitoring and alerting framework design ### Validation Criteria - [ ] Resource calculations validated against current usage - [ ] Architecture supports 10+ concurrent users - [ ] Deployment strategy tested in staging environment - [ ] Performance targets achievable with proposed architecture ## 🔗 Dependencies and Blocking Factors ### Upstream Dependencies - Current browser automation analysis (completed by Claude Code) - Server environment specifications (to be confirmed) - Business user volume projections (feeds into TASK-004) ### Downstream Impact - **TASK-004**: User volume projections depend on resource capacity - **TASK-005**: Testing strategy needs resource allocation - **TASK-006**: Production deployment needs resource architecture ## 📊 Risk Assessment ### High Risk Areas 1. **Resource Underestimation**: Browser automation memory usage variability 2. **Scalability Limits**: Chromium instance management complexity 3. **Cost Escalation**: Cloud resource pricing for sustained usage 4. **Performance Degradation**: Resource contention under load ### Mitigation Strategies - Conservative resource estimation with 25% buffer - Comprehensive testing under realistic load conditions - Cost monitoring and budget alerting - Performance testing with gradual load increase --- **Phase 1 Status**: ✅ COMPLETED **Next Action**: Proceed to Phase 2 Implementation **Estimated Phase 2 Duration**: 3-4 days **Ready for Phase 2**: Yes, pending approval