Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
This commit is contained in:
ruv
250
vendor/ruvector/.claude/agents/optimization/README.md
vendored
Normal file
250
vendor/ruvector/.claude/agents/optimization/README.md
vendored
Normal file
@@ -0,0 +1,250 @@
|
||||
---
|
||||
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.
|
||||
Reference in New Issue
Block a user