Komodo MCP Server

--- name: Performance Optimization type: documentation category: optimization description: Comprehensive suite of performance optimization agents for swarm efficiency and scalability --- # Performance Optimization Agents This directory contains a comprehensive suite of performance optimization agents designed to maximize swarm efficiency, scalability, and reliability. ## Agent Overview ### 1. Load Balancing Coordinator (`load-balancer.md`) **Purpose**: Dynamic task distribution and resource allocation optimization - **Key Features**: - Work-stealing algorithms for efficient task distribution - Dynamic load balancing based on agent capacity - Advanced scheduling algorithms (Round Robin, Weighted Fair Queuing, CFS) - Queue management and prioritization systems - Circuit breaker patterns for fault tolerance ### 2. Performance Monitor (`performance-monitor.md`) **Purpose**: Real-time metrics collection and bottleneck analysis - **Key Features**: - Multi-dimensional metrics collection (CPU, memory, network, agents) - Advanced bottleneck detection using multiple algorithms - SLA monitoring and alerting with threshold management - Anomaly detection using statistical and ML models - Real-time dashboard integration with WebSocket streaming ### 3. Topology Optimizer (`topology-optimizer.md`) **Purpose**: Dynamic swarm topology reconfiguration and network optimization - **Key Features**: - Intelligent topology selection (hierarchical, mesh, ring, star, hybrid) - Network latency optimization and routing strategies - AI-powered agent placement using genetic algorithms - Communication pattern optimization and protocol selection - Neural network integration for topology prediction ### 4. Resource Allocator (`resource-allocator.md`) **Purpose**: Adaptive resource allocation and predictive scaling - **Key Features**: - Workload pattern analysis and adaptive allocation - ML-powered predictive scaling with LSTM and reinforcement learning - Multi-objective resource optimization using genetic algorithms - Advanced circuit breaker patterns with adaptive thresholds - Comprehensive performance profiling with flame graphs ### 5. Benchmark Suite (`benchmark-suite.md`) **Purpose**: Comprehensive performance benchmarking and validation - **Key Features**: - Automated performance testing (load, stress, volume, endurance) - Performance regression detection using multiple algorithms - SLA validation and quality assessment frameworks - Continuous integration with CI/CD pipelines - Error pattern analysis and trend detection ## Architecture Overview ``` ┌─────────────────────────────────────────────────────┐ │ MCP Integration Layer │ ├─────────────────────────────────────────────────────┤ │ Performance │ Load │ Topology │ Resource │ │ Monitor │ Balancer │ Optimizer │ Allocator│ ├─────────────────────────────────────────────────────┤ │ Benchmark Suite & Validation │ ├─────────────────────────────────────────────────────┤ │ Swarm Infrastructure Integration │ └─────────────────────────────────────────────────────┘ ``` ## Key Performance Features ### Advanced Algorithms - **Genetic Algorithms**: For topology optimization and resource allocation - **Simulated Annealing**: For topology reconfiguration optimization - **Reinforcement Learning**: For adaptive scaling decisions - **Machine Learning**: For anomaly detection and predictive analytics - **Work-Stealing**: For efficient task distribution ### Monitoring & Analytics - **Real-time Metrics**: CPU, memory, network, agent performance - **Bottleneck Detection**: Multi-algorithm approach for identifying performance issues - **Trend Analysis**: Historical performance pattern recognition - **Predictive Analytics**: ML-based forecasting for resource needs - **Cost Optimization**: Resource efficiency and cost analysis ### Fault Tolerance - **Circuit Breaker Patterns**: Adaptive thresholds for system protection - **Bulkhead Isolation**: Resource pool separation for failure containment - **Graceful Degradation**: Fallback mechanisms for service continuity - **Recovery Strategies**: Automated system recovery and healing ### Integration Capabilities - **MCP Tools**: Extensive use of claude-flow MCP performance tools - **Real-time Dashboards**: WebSocket-based live performance monitoring - **CI/CD Integration**: Automated performance validation in deployment pipelines - **Alert Systems**: Multi-channel notification for performance issues ## Usage Examples ### Basic Optimization Workflow ```bash # 1. Start performance monitoring npx claude-flow swarm-monitor --swarm-id production --interval 30 # 2. Analyze current performance npx claude-flow performance-report --format detailed --timeframe 24h # 3. Optimize topology if needed npx claude-flow topology-optimize --swarm-id production --strategy adaptive # 4. Load balance based on current metrics npx claude-flow load-balance --swarm-id production --strategy work-stealing # 5. Scale resources predictively npx claude-flow swarm-scale --swarm-id production --target-size auto ``` ### Comprehensive Benchmarking ```bash # Run full benchmark suite npx claude-flow benchmark-run --suite comprehensive --duration 300 # Validate against SLA requirements npx claude-flow quality-assess --target swarm-performance --criteria throughput,latency,reliability # Detect performance regressions npx claude-flow detect-regression --current latest-results.json --historical baseline.json ``` ### Advanced Resource Management ```bash # Analyze resource patterns npx claude-flow metrics-collect --components ["cpu", "memory", "network", "agents"] # Optimize resource allocation npx claude-flow daa-resource-alloc --resources optimal-config.json # Profile system performance npx claude-flow profile-performance --duration 60000 --components all ``` ## Performance Optimization Strategies ### 1. Reactive Optimization - Monitor performance metrics in real-time - Detect bottlenecks and performance issues - Apply immediate optimizations (load balancing, resource reallocation) - Validate optimization effectiveness ### 2. Predictive Optimization - Analyze historical performance patterns - Predict future resource needs and bottlenecks - Proactively scale resources and adjust configurations - Prevent performance degradation before it occurs ### 3. Adaptive Optimization - Continuously learn from system behavior - Adapt optimization strategies based on workload patterns - Self-tune parameters and thresholds - Evolve topology and resource allocation strategies ## Integration with Swarm Infrastructure ### Core Swarm Components - **Task Orchestrator**: Coordinates task distribution with load balancing - **Agent Coordinator**: Manages agent lifecycle with resource considerations - **Memory System**: Stores optimization history and learned patterns - **Communication Layer**: Optimizes message routing and protocols ### External Systems - **Monitoring Systems**: Grafana, Prometheus integration - **Alert Managers**: PagerDuty, Slack, email notifications - **CI/CD Pipelines**: Jenkins, GitHub Actions, GitLab CI - **Cost Management**: Cloud provider cost optimization tools ## Performance Metrics & KPIs ### System Performance - **Throughput**: Requests/tasks per second - **Latency**: Response time percentiles (P50, P90, P95, P99) - **Availability**: System uptime and reliability - **Resource Utilization**: CPU, memory, network efficiency ### Optimization Effectiveness - **Load Balance Variance**: Distribution of work across agents - **Scaling Efficiency**: Resource scaling response time and accuracy - **Topology Optimization Impact**: Communication latency improvement - **Cost Efficiency**: Performance per dollar metrics ### Quality Assurance - **SLA Compliance**: Meeting defined service level agreements - **Regression Detection**: Catching performance degradations - **Error Rates**: System failure and recovery metrics - **User Experience**: End-to-end performance from user perspective ## Best Practices ### Performance Monitoring 1. Establish baseline performance metrics 2. Set up automated alerting for critical thresholds 3. Monitor trends, not just point-in-time metrics 4. Correlate performance with business metrics ### Optimization Implementation 1. Test optimizations in staging environments first 2. Implement gradual rollouts for major changes 3. Maintain rollback capabilities for all optimizations 4. Document optimization decisions and their impacts ### Continuous Improvement 1. Regular performance reviews and optimization cycles 2. Automated regression testing in CI/CD pipelines 3. Capacity planning based on growth projections 4. Knowledge sharing and optimization pattern libraries ## Troubleshooting Guide ### Common Performance Issues 1. **High CPU Usage**: Check for inefficient algorithms, infinite loops 2. **Memory Leaks**: Monitor memory growth patterns, object retention 3. **Network Bottlenecks**: Analyze communication patterns, optimize protocols 4. **Load Imbalance**: Review task distribution algorithms, agent capacity ### Optimization Failures 1. **Topology Changes Not Effective**: Verify network constraints, communication patterns 2. **Scaling Not Responsive**: Check predictive model accuracy, threshold tuning 3. **Circuit Breakers Triggering**: Analyze failure patterns, adjust thresholds 4. **Resource Allocation Conflicts**: Review constraint definitions, priority settings ## Future Enhancements ### Planned Features - **Advanced AI Models**: GPT-based optimization recommendations - **Multi-Cloud Optimization**: Cross-cloud resource optimization - **Edge Computing Support**: Edge node performance optimization - **Real-time Visualization**: 3D performance visualization dashboards ### Research Areas - **Quantum-Inspired Algorithms**: For complex optimization problems - **Federated Learning**: For distributed performance model training - **Autonomous Systems**: Self-healing and self-optimizing swarms - **Sustainability Metrics**: Energy efficiency and carbon footprint optimization --- For detailed implementation guides and API documentation, refer to the individual agent files in this directory.