# Performance Intelligence Configuration
# Adaptive performance patterns, auto-optimization, and resource management
# Enables intelligent performance monitoring and self-optimization

# Metadata
version: "1.0.0"
last_updated: "2025-01-06"
description: "Performance intelligence and auto-optimization patterns"

# Adaptive Performance Targets
adaptive_targets:
  baseline_management:
    # Dynamic baseline adjustment based on system performance
    adjustment_strategy: "rolling_average"
    adjustment_window: 50      # operations
    adjustment_sensitivity: 0.15  # 15% threshold for adjustment
    min_samples: 10           # minimum samples before adjustment
    
    baseline_metrics:
      response_time:
        initial_target: 500    # ms
        acceptable_variance: 0.3
        improvement_threshold: 0.1
        
      resource_usage:
        initial_target: 0.7    # 70%
        acceptable_variance: 0.2
        critical_threshold: 0.9
        
      error_rate:
        initial_target: 0.02   # 2%
        acceptable_variance: 0.01
        critical_threshold: 0.1
        
  target_adaptation:
    # How targets adapt to system capabilities
    adaptation_triggers:
      - condition: {performance_improvement: ">20%", duration: ">7_days"}
        action: "tighten_targets"
        adjustment: 0.15
        
      - condition: {performance_degradation: ">15%", duration: ">3_days"}
        action: "relax_targets"
        adjustment: 0.2
        
      - condition: {system_upgrade_detected: true}
        action: "recalibrate_baselines"
        reset_period: "24_hours"
        
    adaptation_limits:
      max_target_tightening: 0.5    # Don't make targets too aggressive
      max_target_relaxation: 2.0    # Don't make targets too loose
      adaptation_cooldown: 3600     # seconds between major adjustments

# Auto-Optimization Engine  
auto_optimization:
  optimization_triggers:
    # Automatic optimization triggers
    performance_triggers:
      - name: "response_time_degradation"
        condition: {avg_response_time: ">target*1.3", samples: ">10"}
        urgency: "high"
        actions: ["enable_aggressive_caching", "reduce_analysis_depth", "parallel_processing"]
        
      - name: "memory_pressure"
        condition: {memory_usage: ">0.85", duration: ">300_seconds"}
        urgency: "critical"
        actions: ["garbage_collection", "cache_cleanup", "reduce_context_size"]
        
      - name: "cpu_saturation"
        condition: {cpu_usage: ">0.9", duration: ">60_seconds"}
        urgency: "high"
        actions: ["reduce_concurrent_operations", "defer_non_critical", "enable_throttling"]
        
      - name: "error_rate_spike"
        condition: {error_rate: ">0.1", recent_window: "5_minutes"}
        urgency: "critical"
        actions: ["enable_fallback_mode", "increase_timeouts", "reduce_complexity"]
        
  optimization_strategies:
    # Available optimization strategies
    aggressive_caching:
      description: "Enable aggressive caching of results and computations"
      performance_impact: 0.3   # Expected improvement
      resource_cost: 0.1       # Memory cost
      duration: 1800           # seconds
      
    parallel_processing:
      description: "Increase parallelization where possible"
      performance_impact: 0.25
      resource_cost: 0.2
      duration: 3600
      
    reduce_analysis_depth:
      description: "Reduce depth of analysis to improve speed"
      performance_impact: 0.4
      quality_impact: -0.1     # Slight quality reduction
      duration: 1800
      
    intelligent_batching:
      description: "Batch similar operations for efficiency"
      performance_impact: 0.2
      resource_cost: -0.05     # Reduces resource usage
      duration: 3600

# Resource Management Intelligence
resource_management:
  resource_zones:
    # Performance zones with different strategies
    green_zone:
      threshold: 0.60         # Below 60% resource usage
      strategy: "optimal_performance"
      features_enabled: ["full_analysis", "comprehensive_caching", "background_optimization"]
      
    yellow_zone:
      threshold: 0.75         # 60-75% resource usage  
      strategy: "balanced_optimization"
      features_enabled: ["standard_analysis", "selective_caching", "reduced_background"]
      optimizations: ["defer_non_critical", "reduce_verbosity"]
      
    orange_zone:
      threshold: 0.85         # 75-85% resource usage
      strategy: "performance_preservation" 
      features_enabled: ["essential_analysis", "minimal_caching"]
      optimizations: ["aggressive_caching", "parallel_where_safe", "reduce_context"]
      
    red_zone:
      threshold: 0.95         # 85-95% resource usage
      strategy: "resource_conservation"
      features_enabled: ["critical_only"]
      optimizations: ["emergency_cleanup", "minimal_processing", "fail_fast"]
      
    critical_zone:
      threshold: 1.0          # Above 95% resource usage
      strategy: "emergency_mode"
      features_enabled: []
      optimizations: ["immediate_cleanup", "operation_rejection", "system_protection"]
      
  dynamic_allocation:
    # Intelligent resource allocation
    allocation_strategies:
      workload_based:
        description: "Allocate based on current workload patterns"
        factors: ["operation_complexity", "expected_duration", "priority"]
        
      predictive:
        description: "Allocate based on predicted resource needs"
        factors: ["historical_patterns", "operation_type", "context_size"]
        
      adaptive:
        description: "Adapt allocation based on real-time performance"
        factors: ["current_performance", "resource_availability", "optimization_goals"]

# Performance Regression Detection
regression_detection:
  detection_algorithms:
    # Algorithms for detecting performance regression
    statistical_analysis:
      algorithm: "t_test"
      confidence_level: 0.95
      minimum_samples: 20
      window_size: 100       # operations
      
    trend_analysis:
      algorithm: "linear_regression"
      trend_threshold: 0.1   # 10% degradation trend
      analysis_window: 168   # hours (1 week)
      
    anomaly_detection:
      algorithm: "isolation_forest"
      contamination: 0.1     # Expected anomaly rate
      sensitivity: 0.8
      
  regression_patterns:
    # Common regression patterns to detect
    gradual_degradation:
      pattern: {performance_trend: "decreasing", duration: ">5_days"}
      severity: "medium"
      investigation: "check_for_memory_leaks"
      
    sudden_degradation:
      pattern: {performance_drop: ">30%", timeframe: "<1_hour"}
      severity: "high"
      investigation: "check_recent_changes"
      
    periodic_degradation:
      pattern: {performance_cycles: "detected", frequency: "regular"}
      severity: "low"
      investigation: "analyze_periodic_patterns"

# Intelligent Resource Optimization
intelligent_optimization:
  predictive_optimization:
    # Predict and prevent performance issues
    prediction_models:
      resource_exhaustion:
        model_type: "time_series"
        prediction_horizon: 3600  # seconds
        accuracy_threshold: 0.8
        
      performance_degradation:
        model_type: "pattern_matching"
        pattern_library: "historical_degradations"
        confidence_threshold: 0.7
        
    proactive_actions:
      - prediction: "memory_exhaustion"
        lead_time: 1800      # seconds
        actions: ["preemptive_cleanup", "cache_optimization", "context_reduction"]
        
      - prediction: "cpu_saturation"
        lead_time: 600       # seconds
        actions: ["reduce_parallelism", "defer_background_tasks", "enable_throttling"]
        
  optimization_recommendations:
    # Generate optimization recommendations
    recommendation_engine:
      analysis_depth: "comprehensive"
      recommendation_confidence: 0.8
      implementation_difficulty: "user_friendly"
      
    recommendation_types:
      configuration_tuning:
        description: "Suggest configuration changes for better performance"
        impact_assessment: "quantified"
        
      resource_allocation:
        description: "Recommend better resource allocation strategies"
        cost_benefit_analysis: true
        
      workflow_optimization:
        description: "Suggest workflow improvements"
        user_experience_impact: "minimal"

# Performance Monitoring Intelligence
monitoring_intelligence:
  intelligent_metrics:
    # Smart metric collection and analysis
    adaptive_sampling:
      base_sampling_rate: 1.0    # Sample every operation
      high_load_rate: 0.5       # Reduce sampling under load
      critical_load_rate: 0.1   # Minimal sampling in critical situations
      
    contextual_metrics:
      # Collect different metrics based on context
      ui_operations:
        focus_metrics: ["response_time", "render_time", "user_interaction_delay"]
        
      analysis_operations:
        focus_metrics: ["processing_time", "memory_usage", "accuracy_score"]
        
      batch_operations:
        focus_metrics: ["throughput", "resource_efficiency", "completion_rate"]
        
  performance_insights:
    # Generate performance insights
    insight_generation:
      pattern_recognition: true
      correlation_analysis: true
      root_cause_analysis: true
      improvement_suggestions: true
      
    insight_types:
      bottleneck_identification:
        description: "Identify performance bottlenecks"
        priority: "high"
        
      optimization_opportunities:
        description: "Find optimization opportunities"  
        priority: "medium"
        
      capacity_planning:
        description: "Predict capacity requirements"
        priority: "low"

# Performance Validation
performance_validation:
  validation_framework:
    # Validate performance improvements
    a_b_testing:
      enable_automatic_testing: true
      test_duration: 3600     # seconds
      statistical_significance: 0.95
      
    performance_benchmarking:
      benchmark_frequency: "weekly"
      regression_threshold: 0.05  # 5% regression tolerance
      
  continuous_improvement:
    # Continuous performance improvement
    improvement_tracking:
      track_optimization_effectiveness: true
      measure_user_satisfaction: true
      monitor_system_health: true
      
    feedback_loops:
      performance_feedback: "real_time"
      user_feedback_integration: true
      system_learning_integration: true