oxirs_embed/

cross_module_performance.rs

//! Cross-Module Performance Coordinator
//!
//! Advanced performance optimization system that coordinates performance across
//! all OxiRS modules, providing intelligent resource allocation, predictive scaling,
//! and cross-system optimization strategies.

use anyhow::{anyhow, Result};
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::atomic::{AtomicU64, AtomicUsize, Ordering};
use std::sync::{Arc, RwLock};
use std::time::Duration;
use tokio::time;
use tracing::{debug, info, warn};

/// Cross-module performance coordinator
#[derive(Debug)]
pub struct CrossModulePerformanceCoordinator {
    /// Configuration for cross-module optimization
    config: CoordinatorConfig,
    /// Module performance monitors
    module_monitors: Arc<RwLock<HashMap<String, ModulePerformanceMonitor>>>,
    /// Resource allocator
    resource_allocator: ResourceAllocator,
    /// Predictive performance engine
    predictive_engine: PredictivePerformanceEngine,
    /// Performance optimization cache
    optimization_cache: Arc<RwLock<OptimizationCache>>,
    /// Global performance metrics
    global_metrics: Arc<RwLock<GlobalPerformanceMetrics>>,
}

/// Configuration for cross-module performance coordination
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoordinatorConfig {
    /// Enable predictive scaling
    pub enable_predictive_scaling: bool,
    /// Enable intelligent prefetching
    pub enable_intelligent_prefetching: bool,
    /// Enable dynamic resource allocation
    pub enable_dynamic_allocation: bool,
    /// Enable cross-module caching
    pub enable_cross_module_caching: bool,
    /// Performance monitoring interval
    pub monitoring_interval_ms: u64,
    /// Resource reallocation threshold
    pub reallocation_threshold: f64,
    /// Prefetch prediction window
    pub prefetch_window_seconds: u64,
    /// Maximum concurrent optimizations
    pub max_concurrent_optimizations: usize,
    /// Enable performance learning
    pub enable_performance_learning: bool,
}

impl Default for CoordinatorConfig {
    fn default() -> Self {
        Self {
            enable_predictive_scaling: true,
            enable_intelligent_prefetching: true,
            enable_dynamic_allocation: true,
            enable_cross_module_caching: true,
            monitoring_interval_ms: 1000,
            reallocation_threshold: 0.8,
            prefetch_window_seconds: 30,
            max_concurrent_optimizations: 4,
            enable_performance_learning: true,
        }
    }
}

/// Module performance monitor
#[derive(Debug, Clone)]
pub struct ModulePerformanceMonitor {
    /// Module name
    module_name: String,
    /// Performance metrics
    metrics: Arc<RwLock<ModuleMetrics>>,
    /// Resource usage tracker
    resource_tracker: ResourceTracker,
    /// Performance history
    history: Arc<RwLock<VecDeque<PerformanceSnapshot>>>,
    /// Prediction model
    prediction_model: PredictionModel,
}

/// Module performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModuleMetrics {
    /// CPU usage percentage
    pub cpu_usage: f64,
    /// Memory usage in bytes
    pub memory_usage: u64,
    /// GPU memory usage in bytes
    pub gpu_memory_usage: Option<u64>,
    /// Network I/O bytes per second
    pub network_io_bps: u64,
    /// Disk I/O bytes per second
    pub disk_io_bps: u64,
    /// Request rate per second
    pub request_rate: f64,
    /// Average response time
    pub avg_response_time: Duration,
    /// Error rate percentage
    pub error_rate: f64,
    /// Cache hit rate percentage
    pub cache_hit_rate: f64,
    /// Active connections
    pub active_connections: usize,
    /// Queue depth
    pub queue_depth: usize,
}

/// Resource allocator for dynamic resource management
#[derive(Debug)]
pub struct ResourceAllocator {
    /// Available CPU cores
    available_cores: AtomicUsize,
    /// Available memory in bytes
    available_memory: AtomicU64,
    /// Available GPU memory in bytes
    available_gpu_memory: AtomicU64,
    /// Resource allocation history
    allocation_history: Arc<RwLock<VecDeque<AllocationEvent>>>,
    /// Current allocations by module
    current_allocations: Arc<RwLock<HashMap<String, ResourceAllocation>>>,
    /// Allocation optimization strategies
    optimization_strategies: Vec<AllocationStrategy>,
}

/// Resource allocation for a module
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceAllocation {
    /// Allocated CPU cores
    pub cpu_cores: usize,
    /// Allocated memory in bytes
    pub memory_bytes: u64,
    /// Allocated GPU memory in bytes
    pub gpu_memory_bytes: Option<u64>,
    /// Priority level (0-100)
    pub priority: u8,
    /// Allocation timestamp
    pub allocated_at: DateTime<Utc>,
    /// Expected duration
    pub expected_duration: Option<Duration>,
}

/// Resource allocation event
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AllocationEvent {
    /// Module name
    pub module_name: String,
    /// Event type
    pub event_type: AllocationType,
    /// Resource allocation
    pub allocation: ResourceAllocation,
    /// Performance impact
    pub performance_impact: Option<PerformanceImpact>,
    /// Timestamp
    pub timestamp: DateTime<Utc>,
}

/// Allocation type
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AllocationType {
    Initial,
    Increase,
    Decrease,
    Rebalance,
    Emergency,
}

/// Performance impact of allocation changes
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceImpact {
    /// Latency change percentage
    pub latency_change_pct: f64,
    /// Throughput change percentage
    pub throughput_change_pct: f64,
    /// Resource efficiency change
    pub efficiency_change_pct: f64,
    /// Overall score (0-100)
    pub overall_score: f64,
}

/// Predictive performance engine
#[derive(Debug)]
pub struct PredictivePerformanceEngine {
    /// Performance models by module
    models: Arc<RwLock<HashMap<String, PerformanceModel>>>,
    /// Prediction cache
    prediction_cache: Arc<RwLock<HashMap<String, PredictionCache>>>,
    /// Learning engine
    learning_engine: LearningEngine,
    /// Anomaly detector
    anomaly_detector: AnomalyDetector,
}

/// Performance prediction model
#[derive(Debug, Clone)]
pub struct PerformanceModel {
    /// Model type
    pub model_type: ModelType,
    /// Model parameters
    pub parameters: HashMap<String, f64>,
    /// Training data window
    pub training_window: Duration,
    /// Prediction accuracy
    pub accuracy: f64,
    /// Last training time
    pub last_trained: DateTime<Utc>,
}

/// Model types for performance prediction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelType {
    LinearRegression,
    TimeSeriesARIMA,
    NeuralNetwork,
    EnsembleModel,
    AdaptiveFilter,
}

/// Performance prediction cache
#[derive(Debug)]
pub struct PredictionCache {
    /// Cached predictions
    predictions: HashMap<String, CachedPrediction>,
    /// Cache hit count
    hit_count: AtomicU64,
    /// Cache miss count
    miss_count: AtomicU64,
    /// Last cache cleanup
    last_cleanup: DateTime<Utc>,
}

/// Cached prediction
#[derive(Debug, Clone)]
pub struct CachedPrediction {
    /// Predicted value
    pub value: f64,
    /// Confidence interval
    pub confidence_interval: (f64, f64),
    /// Prediction timestamp
    pub predicted_at: DateTime<Utc>,
    /// TTL
    pub expires_at: DateTime<Utc>,
    /// Hit count
    pub hit_count: u64,
}

/// Learning engine for continuous improvement
#[derive(Debug)]
pub struct LearningEngine {
    /// Learning rate
    learning_rate: f64,
    /// Training samples
    training_samples: Arc<RwLock<VecDeque<TrainingSample>>>,
    /// Model update frequency
    update_frequency: Duration,
    /// Performance baselines
    baselines: Arc<RwLock<HashMap<String, PerformanceBaseline>>>,
}

/// Training sample for learning
#[derive(Debug, Clone)]
pub struct TrainingSample {
    /// Feature vector
    pub features: Vec<f64>,
    /// Target value
    pub target: f64,
    /// Context information
    pub context: HashMap<String, String>,
    /// Sample weight
    pub weight: f64,
    /// Timestamp
    pub timestamp: DateTime<Utc>,
}

/// Performance baseline
#[derive(Debug, Clone)]
pub struct PerformanceBaseline {
    /// Baseline metrics
    pub metrics: ModuleMetrics,
    /// Established date
    pub established_at: DateTime<Utc>,
    /// Confidence level
    pub confidence: f64,
    /// Update count
    pub update_count: u64,
}

/// Anomaly detector for performance issues
#[derive(Debug)]
pub struct AnomalyDetector {
    /// Detection algorithms
    algorithms: Vec<AnomalyAlgorithm>,
    /// Detection thresholds
    thresholds: HashMap<String, f64>,
    /// Historical anomalies
    anomaly_history: Arc<RwLock<VecDeque<AnomalyEvent>>>,
    /// False positive rate
    false_positive_rate: f64,
}

/// Anomaly detection algorithm
#[derive(Debug, Clone)]
pub enum AnomalyAlgorithm {
    StatisticalOutlier { z_threshold: f64 },
    IsolationForest { contamination: f64 },
    OneClassSVM { nu: f64 },
    LocalOutlierFactor { n_neighbors: usize },
    EllipticEnvelope { contamination: f64 },
}

/// Anomaly event
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnomalyEvent {
    /// Module name
    pub module_name: String,
    /// Anomaly type
    pub anomaly_type: AnomalyType,
    /// Severity level
    pub severity: SeverityLevel,
    /// Anomaly score
    pub score: f64,
    /// Affected metrics
    pub affected_metrics: Vec<String>,
    /// Recommended actions
    pub recommended_actions: Vec<String>,
    /// Detection timestamp
    pub detected_at: DateTime<Utc>,
    /// Resolution timestamp
    pub resolved_at: Option<DateTime<Utc>>,
}

/// Anomaly types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum AnomalyType {
    PerformanceDegradation,
    ResourceSpike,
    MemoryLeak,
    ThroughputDrop,
    LatencyIncrease,
    ErrorRateSpike,
    CacheEfficiencyDrop,
    ConnectionPoolExhaustion,
}

/// Severity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SeverityLevel {
    Low,
    Medium,
    High,
    Critical,
}

/// Implementation of CrossModulePerformanceCoordinator
impl CrossModulePerformanceCoordinator {
    /// Create a new cross-module performance coordinator
    pub fn new(config: CoordinatorConfig) -> Self {
        Self {
            config,
            module_monitors: Arc::new(RwLock::new(HashMap::new())),
            resource_allocator: ResourceAllocator::new(),
            predictive_engine: PredictivePerformanceEngine::new(),
            optimization_cache: Arc::new(RwLock::new(OptimizationCache::new())),
            global_metrics: Arc::new(RwLock::new(GlobalPerformanceMetrics::new())),
        }
    }

    /// Register a module for performance monitoring
    pub async fn register_module(&self, module_name: String) -> Result<()> {
        let monitor = ModulePerformanceMonitor::new(module_name.clone());

        {
            let mut monitors = self.module_monitors.write().expect("lock poisoned");
            monitors.insert(module_name.clone(), monitor);
        }

        info!(
            "Registered module '{}' for performance monitoring",
            module_name
        );
        Ok(())
    }

    /// Update module metrics
    pub async fn update_module_metrics(
        &self,
        module_name: &str,
        metrics: ModuleMetrics,
    ) -> Result<()> {
        let monitor = {
            let monitors = self.module_monitors.read().expect("lock poisoned");
            monitors.get(module_name).cloned()
        };
        if let Some(monitor) = monitor {
            monitor.update_metrics(metrics).await?;
        } else {
            return Err(anyhow!("Module '{}' not registered", module_name));
        }
        Ok(())
    }

    /// Optimize performance across all modules
    pub async fn optimize_performance(&self) -> Result<OptimizationResults> {
        info!("Starting cross-module performance optimization");

        let mut results = OptimizationResults::new();

        // Collect current performance data
        let performance_data = self.collect_performance_data().await?;

        // Detect anomalies
        let anomalies = self
            .predictive_engine
            .detect_anomalies(&performance_data)
            .await?;
        results.anomalies_detected = anomalies.len();

        // Generate optimization recommendations
        let recommendations = self
            .generate_optimization_recommendations(&performance_data, &anomalies)
            .await?;
        results.recommendations = recommendations.clone();

        // Apply optimizations
        for recommendation in recommendations {
            match self.apply_optimization(recommendation).await {
                Ok(impact) => {
                    results.optimizations_applied += 1;
                    results.total_performance_gain += impact.overall_score;
                }
                Err(e) => {
                    warn!("Failed to apply optimization: {}", e);
                    results.optimization_failures += 1;
                }
            }
        }

        // Update global metrics
        self.update_global_metrics(&results).await?;

        info!("Performance optimization completed: {:?}", results);
        Ok(results)
    }

    /// Collect performance data from all modules
    async fn collect_performance_data(&self) -> Result<HashMap<String, ModuleMetrics>> {
        let monitor_list = {
            let monitors = self.module_monitors.read().expect("lock poisoned");
            monitors
                .iter()
                .map(|(name, monitor)| (name.clone(), monitor.clone()))
                .collect::<Vec<_>>()
        };
        let mut data = HashMap::new();

        for (module_name, monitor) in monitor_list {
            let metrics = monitor.get_current_metrics().await?;
            data.insert(module_name, metrics);
        }

        Ok(data)
    }

    /// Generate optimization recommendations
    async fn generate_optimization_recommendations(
        &self,
        performance_data: &HashMap<String, ModuleMetrics>,
        anomalies: &[AnomalyEvent],
    ) -> Result<Vec<OptimizationRecommendation>> {
        let mut recommendations = Vec::new();

        // Analyze resource usage patterns
        for (module_name, metrics) in performance_data {
            // CPU optimization
            if metrics.cpu_usage > 80.0 {
                recommendations.push(OptimizationRecommendation {
                    module_name: module_name.clone(),
                    optimization_type: OptimizationType::ResourceReallocation,
                    priority: Priority::High,
                    description: "High CPU usage detected - recommend resource reallocation"
                        .to_string(),
                    estimated_impact: PerformanceImpact {
                        latency_change_pct: -15.0,
                        throughput_change_pct: 20.0,
                        efficiency_change_pct: 10.0,
                        overall_score: 75.0,
                    },
                    implementation_steps: vec![
                        "Increase CPU allocation".to_string(),
                        "Enable parallel processing".to_string(),
                        "Optimize critical paths".to_string(),
                    ],
                });
            }

            // Memory optimization
            if metrics.memory_usage > 8_000_000_000 {
                // 8GB
                recommendations.push(OptimizationRecommendation {
                    module_name: module_name.clone(),
                    optimization_type: OptimizationType::MemoryOptimization,
                    priority: Priority::Medium,
                    description: "High memory usage - recommend memory optimization".to_string(),
                    estimated_impact: PerformanceImpact {
                        latency_change_pct: -10.0,
                        throughput_change_pct: 15.0,
                        efficiency_change_pct: 25.0,
                        overall_score: 70.0,
                    },
                    implementation_steps: vec![
                        "Enable memory pooling".to_string(),
                        "Optimize data structures".to_string(),
                        "Implement garbage collection tuning".to_string(),
                    ],
                });
            }

            // Cache optimization
            if metrics.cache_hit_rate < 80.0 {
                recommendations.push(OptimizationRecommendation {
                    module_name: module_name.clone(),
                    optimization_type: OptimizationType::CacheOptimization,
                    priority: Priority::Medium,
                    description: "Low cache hit rate - recommend cache optimization".to_string(),
                    estimated_impact: PerformanceImpact {
                        latency_change_pct: -20.0,
                        throughput_change_pct: 25.0,
                        efficiency_change_pct: 15.0,
                        overall_score: 80.0,
                    },
                    implementation_steps: vec![
                        "Increase cache size".to_string(),
                        "Implement intelligent prefetching".to_string(),
                        "Optimize cache eviction policy".to_string(),
                    ],
                });
            }
        }

        // Add anomaly-based recommendations
        for anomaly in anomalies {
            recommendations.extend(self.generate_anomaly_recommendations(anomaly).await?);
        }

        // Sort by priority and estimated impact
        recommendations.sort_by(|a, b| {
            b.priority.cmp(&a.priority).then_with(|| {
                b.estimated_impact
                    .overall_score
                    .partial_cmp(&a.estimated_impact.overall_score)
                    .unwrap_or(std::cmp::Ordering::Equal)
            })
        });

        Ok(recommendations)
    }

    /// Generate recommendations based on anomalies
    async fn generate_anomaly_recommendations(
        &self,
        anomaly: &AnomalyEvent,
    ) -> Result<Vec<OptimizationRecommendation>> {
        let mut recommendations = Vec::new();

        match anomaly.anomaly_type {
            AnomalyType::PerformanceDegradation => {
                recommendations.push(OptimizationRecommendation {
                    module_name: anomaly.module_name.clone(),
                    optimization_type: OptimizationType::PerformanceTuning,
                    priority: Priority::High,
                    description: "Performance degradation detected - immediate optimization needed"
                        .to_string(),
                    estimated_impact: PerformanceImpact {
                        latency_change_pct: -30.0,
                        throughput_change_pct: 40.0,
                        efficiency_change_pct: 20.0,
                        overall_score: 85.0,
                    },
                    implementation_steps: anomaly.recommended_actions.clone(),
                });
            }
            AnomalyType::MemoryLeak => {
                recommendations.push(OptimizationRecommendation {
                    module_name: anomaly.module_name.clone(),
                    optimization_type: OptimizationType::MemoryOptimization,
                    priority: Priority::Critical,
                    description: "Memory leak detected - immediate action required".to_string(),
                    estimated_impact: PerformanceImpact {
                        latency_change_pct: -50.0,
                        throughput_change_pct: 60.0,
                        efficiency_change_pct: 80.0,
                        overall_score: 95.0,
                    },
                    implementation_steps: vec![
                        "Identify memory leak source".to_string(),
                        "Implement automatic memory cleanup".to_string(),
                        "Add memory monitoring alerts".to_string(),
                    ],
                });
            }
            _ => {
                // Generate generic recommendation based on anomaly type
                recommendations.push(OptimizationRecommendation {
                    module_name: anomaly.module_name.clone(),
                    optimization_type: OptimizationType::GeneralOptimization,
                    priority: match anomaly.severity {
                        SeverityLevel::Critical => Priority::Critical,
                        SeverityLevel::High => Priority::High,
                        SeverityLevel::Medium => Priority::Medium,
                        SeverityLevel::Low => Priority::Low,
                    },
                    description: format!("Anomaly detected: {:?}", anomaly.anomaly_type),
                    estimated_impact: PerformanceImpact {
                        latency_change_pct: -10.0,
                        throughput_change_pct: 15.0,
                        efficiency_change_pct: 10.0,
                        overall_score: 60.0,
                    },
                    implementation_steps: anomaly.recommended_actions.clone(),
                });
            }
        }

        Ok(recommendations)
    }

    /// Apply an optimization recommendation
    async fn apply_optimization(
        &self,
        recommendation: OptimizationRecommendation,
    ) -> Result<PerformanceImpact> {
        info!("Applying optimization: {}", recommendation.description);

        match recommendation.optimization_type {
            OptimizationType::ResourceReallocation => {
                self.resource_allocator
                    .reallocate_resources(&recommendation.module_name, &recommendation)
                    .await?;
            }
            OptimizationType::MemoryOptimization => {
                self.apply_memory_optimization(&recommendation.module_name, &recommendation)
                    .await?;
            }
            OptimizationType::CacheOptimization => {
                self.apply_cache_optimization(&recommendation.module_name, &recommendation)
                    .await?;
            }
            OptimizationType::PerformanceTuning => {
                self.apply_performance_tuning(&recommendation.module_name, &recommendation)
                    .await?;
            }
            OptimizationType::GeneralOptimization => {
                self.apply_general_optimization(&recommendation.module_name, &recommendation)
                    .await?;
            }
        }

        // Measure actual impact
        time::sleep(Duration::from_secs(5)).await; // Allow time for changes to take effect
        let actual_impact = self
            .measure_optimization_impact(&recommendation.module_name)
            .await?;

        // Update prediction models with actual results
        self.predictive_engine
            .update_models(&recommendation, &actual_impact)
            .await?;

        Ok(actual_impact)
    }

    /// Apply memory optimization
    async fn apply_memory_optimization(
        &self,
        module_name: &str,
        recommendation: &OptimizationRecommendation,
    ) -> Result<()> {
        debug!("Applying memory optimization for module: {}", module_name);

        // Implement memory optimization strategies
        for step in &recommendation.implementation_steps {
            if step.contains("memory pooling") {
                self.enable_memory_pooling(module_name).await?;
            } else if step.contains("garbage collection") {
                self.optimize_garbage_collection(module_name).await?;
            } else if step.contains("data structures") {
                self.optimize_data_structures(module_name).await?;
            }
        }

        Ok(())
    }

    /// Apply cache optimization
    async fn apply_cache_optimization(
        &self,
        module_name: &str,
        recommendation: &OptimizationRecommendation,
    ) -> Result<()> {
        debug!("Applying cache optimization for module: {}", module_name);

        for step in &recommendation.implementation_steps {
            if step.contains("cache size") {
                self.increase_cache_size(module_name).await?;
            } else if step.contains("prefetching") {
                self.enable_intelligent_prefetching(module_name).await?;
            } else if step.contains("eviction policy") {
                self.optimize_cache_eviction(module_name).await?;
            }
        }

        Ok(())
    }

    /// Apply performance tuning
    async fn apply_performance_tuning(
        &self,
        module_name: &str,
        recommendation: &OptimizationRecommendation,
    ) -> Result<()> {
        debug!("Applying performance tuning for module: {}", module_name);

        // Implement performance tuning strategies
        for step in &recommendation.implementation_steps {
            if step.contains("parallel processing") {
                self.enable_parallel_processing(module_name).await?;
            } else if step.contains("critical paths") {
                self.optimize_critical_paths(module_name).await?;
            } else if step.contains("algorithms") {
                self.optimize_algorithms(module_name).await?;
            }
        }

        Ok(())
    }

    /// Apply general optimization
    async fn apply_general_optimization(
        &self,
        module_name: &str,
        _recommendation: &OptimizationRecommendation,
    ) -> Result<()> {
        debug!("Applying general optimization for module: {}", module_name);

        // Apply generic optimization strategies
        self.tune_module_parameters(module_name).await?;
        self.optimize_resource_usage(module_name).await?;

        Ok(())
    }

    /// Measure optimization impact
    async fn measure_optimization_impact(&self, module_name: &str) -> Result<PerformanceImpact> {
        // Get baseline metrics
        let baseline = self.get_baseline_metrics(module_name).await?;

        // Get current metrics
        let current = self.get_current_module_metrics(module_name).await?;

        // Calculate impact
        let latency_change = calculate_percentage_change(
            baseline.avg_response_time.as_millis() as f64,
            current.avg_response_time.as_millis() as f64,
        );

        let throughput_change =
            calculate_percentage_change(baseline.request_rate, current.request_rate);

        let efficiency_change = calculate_percentage_change(baseline.cpu_usage, current.cpu_usage);

        let overall_score =
            (latency_change.abs() + throughput_change + efficiency_change.abs()) / 3.0;

        Ok(PerformanceImpact {
            latency_change_pct: latency_change,
            throughput_change_pct: throughput_change,
            efficiency_change_pct: efficiency_change,
            overall_score,
        })
    }

    /// Update global metrics
    async fn update_global_metrics(&self, results: &OptimizationResults) -> Result<()> {
        let mut global_metrics = self.global_metrics.write().expect("lock poisoned");
        global_metrics.update(results);
        Ok(())
    }

    // Helper methods for specific optimizations
    async fn enable_memory_pooling(&self, _module_name: &str) -> Result<()> {
        // Implementation would interact with the specific module's memory management
        debug!("Enabling memory pooling");
        Ok(())
    }

    async fn optimize_garbage_collection(&self, _module_name: &str) -> Result<()> {
        debug!("Optimizing garbage collection");
        Ok(())
    }

    async fn optimize_data_structures(&self, _module_name: &str) -> Result<()> {
        debug!("Optimizing data structures");
        Ok(())
    }

    async fn increase_cache_size(&self, _module_name: &str) -> Result<()> {
        debug!("Increasing cache size");
        Ok(())
    }

    async fn enable_intelligent_prefetching(&self, _module_name: &str) -> Result<()> {
        debug!("Enabling intelligent prefetching");
        Ok(())
    }

    async fn optimize_cache_eviction(&self, _module_name: &str) -> Result<()> {
        debug!("Optimizing cache eviction policy");
        Ok(())
    }

    async fn enable_parallel_processing(&self, _module_name: &str) -> Result<()> {
        debug!("Enabling parallel processing");
        Ok(())
    }

    async fn optimize_critical_paths(&self, _module_name: &str) -> Result<()> {
        debug!("Optimizing critical paths");
        Ok(())
    }

    async fn optimize_algorithms(&self, _module_name: &str) -> Result<()> {
        debug!("Optimizing algorithms");
        Ok(())
    }

    async fn tune_module_parameters(&self, _module_name: &str) -> Result<()> {
        debug!("Tuning module parameters");
        Ok(())
    }

    async fn optimize_resource_usage(&self, _module_name: &str) -> Result<()> {
        debug!("Optimizing resource usage");
        Ok(())
    }

    async fn get_baseline_metrics(&self, _module_name: &str) -> Result<ModuleMetrics> {
        // Implementation would retrieve baseline metrics for the module
        Ok(ModuleMetrics {
            cpu_usage: 50.0,
            memory_usage: 4_000_000_000,
            gpu_memory_usage: Some(2_000_000_000),
            network_io_bps: 1_000_000,
            disk_io_bps: 500_000,
            request_rate: 100.0,
            avg_response_time: Duration::from_millis(100),
            error_rate: 1.0,
            cache_hit_rate: 85.0,
            active_connections: 50,
            queue_depth: 10,
        })
    }

    async fn get_current_module_metrics(&self, module_name: &str) -> Result<ModuleMetrics> {
        let monitor = {
            let monitors = self.module_monitors.read().expect("lock poisoned");
            monitors.get(module_name).cloned()
        };
        if let Some(monitor) = monitor {
            monitor.get_current_metrics().await
        } else {
            Err(anyhow!("Module '{}' not found", module_name))
        }
    }
}

/// Additional supporting structures and implementations...
/// Optimization recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    /// Target module name
    pub module_name: String,
    /// Type of optimization
    pub optimization_type: OptimizationType,
    /// Priority level
    pub priority: Priority,
    /// Description of the optimization
    pub description: String,
    /// Estimated performance impact
    pub estimated_impact: PerformanceImpact,
    /// Implementation steps
    pub implementation_steps: Vec<String>,
}

/// Optimization types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationType {
    ResourceReallocation,
    MemoryOptimization,
    CacheOptimization,
    PerformanceTuning,
    GeneralOptimization,
}

/// Priority levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub enum Priority {
    Low,
    Medium,
    High,
    Critical,
}

/// Optimization results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationResults {
    /// Number of anomalies detected
    pub anomalies_detected: usize,
    /// Number of optimizations applied
    pub optimizations_applied: usize,
    /// Number of optimization failures
    pub optimization_failures: usize,
    /// Total performance gain
    pub total_performance_gain: f64,
    /// Optimization recommendations
    pub recommendations: Vec<OptimizationRecommendation>,
    /// Execution time
    pub execution_time: Duration,
}

impl OptimizationResults {
    fn new() -> Self {
        Self {
            anomalies_detected: 0,
            optimizations_applied: 0,
            optimization_failures: 0,
            total_performance_gain: 0.0,
            recommendations: Vec::new(),
            execution_time: Duration::from_secs(0),
        }
    }
}

/// Global performance metrics
#[derive(Debug)]
pub struct GlobalPerformanceMetrics {
    /// Total optimizations performed
    total_optimizations: AtomicU64,
    /// Average performance gain
    avg_performance_gain: Arc<RwLock<f64>>,
    /// Success rate
    success_rate: Arc<RwLock<f64>>,
    /// Last optimization time
    last_optimization: Arc<RwLock<Option<DateTime<Utc>>>>,
}

impl GlobalPerformanceMetrics {
    fn new() -> Self {
        Self {
            total_optimizations: AtomicU64::new(0),
            avg_performance_gain: Arc::new(RwLock::new(0.0)),
            success_rate: Arc::new(RwLock::new(0.0)),
            last_optimization: Arc::new(RwLock::new(None)),
        }
    }

    fn update(&mut self, results: &OptimizationResults) {
        self.total_optimizations.fetch_add(1, Ordering::SeqCst);

        {
            let mut gain = self.avg_performance_gain.write().expect("lock poisoned");
            *gain = (*gain + results.total_performance_gain) / 2.0;
        }

        {
            let mut rate = self.success_rate.write().expect("lock poisoned");
            let success = results.optimizations_applied as f64
                / (results.optimizations_applied + results.optimization_failures).max(1) as f64;
            *rate = (*rate + success) / 2.0;
        }

        {
            let mut last = self.last_optimization.write().expect("lock poisoned");
            *last = Some(Utc::now());
        }
    }
}

/// Optimization cache
#[derive(Debug)]
pub struct OptimizationCache {
    /// Cached optimization results
    cache: HashMap<String, CachedOptimization>,
    /// Cache statistics
    stats: CacheStats,
}

/// Cached optimization
#[derive(Debug, Clone)]
pub struct CachedOptimization {
    /// Optimization recommendation
    pub recommendation: OptimizationRecommendation,
    /// Actual impact achieved
    pub actual_impact: PerformanceImpact,
    /// Cache timestamp
    pub cached_at: DateTime<Utc>,
    /// Hit count
    pub hit_count: u64,
}

/// Cache statistics
#[derive(Debug)]
pub struct CacheStats {
    /// Cache hits
    pub hits: AtomicU64,
    /// Cache misses
    pub misses: AtomicU64,
    /// Cache size
    pub size: AtomicUsize,
}

impl OptimizationCache {
    fn new() -> Self {
        Self {
            cache: HashMap::new(),
            stats: CacheStats {
                hits: AtomicU64::new(0),
                misses: AtomicU64::new(0),
                size: AtomicUsize::new(0),
            },
        }
    }
}

/// Helper implementations for other types...
impl ResourceAllocator {
    fn new() -> Self {
        Self {
            available_cores: AtomicUsize::new(8), // Default to 8 cores
            available_memory: AtomicU64::new(16_000_000_000), // 16GB
            available_gpu_memory: AtomicU64::new(8_000_000_000), // 8GB
            allocation_history: Arc::new(RwLock::new(VecDeque::new())),
            current_allocations: Arc::new(RwLock::new(HashMap::new())),
            optimization_strategies: Vec::new(),
        }
    }

    async fn reallocate_resources(
        &self,
        module_name: &str,
        recommendation: &OptimizationRecommendation,
    ) -> Result<()> {
        debug!("Reallocating resources for module: {}", module_name);

        // Calculate new allocation based on recommendation
        let current_allocation = self.get_current_allocation(module_name).await?;
        let new_allocation = self
            .calculate_new_allocation(&current_allocation, recommendation)
            .await?;

        // Apply new allocation
        self.apply_allocation(module_name, new_allocation).await?;

        Ok(())
    }

    async fn get_current_allocation(&self, module_name: &str) -> Result<ResourceAllocation> {
        let allocations = self.current_allocations.read().expect("lock poisoned");
        if let Some(allocation) = allocations.get(module_name) {
            Ok(allocation.clone())
        } else {
            // Return default allocation if none exists
            Ok(ResourceAllocation {
                cpu_cores: 2,
                memory_bytes: 2_000_000_000,           // 2GB
                gpu_memory_bytes: Some(1_000_000_000), // 1GB
                priority: 50,
                allocated_at: Utc::now(),
                expected_duration: None,
            })
        }
    }

    async fn calculate_new_allocation(
        &self,
        current: &ResourceAllocation,
        recommendation: &OptimizationRecommendation,
    ) -> Result<ResourceAllocation> {
        let mut new_allocation = current.clone();

        // Adjust allocation based on optimization type
        match recommendation.optimization_type {
            OptimizationType::ResourceReallocation => {
                // Increase resources based on priority
                match recommendation.priority {
                    Priority::Critical => {
                        new_allocation.cpu_cores = (current.cpu_cores * 2).min(8);
                        new_allocation.memory_bytes = (current.memory_bytes * 2).min(8_000_000_000);
                    }
                    Priority::High => {
                        new_allocation.cpu_cores = (current.cpu_cores + 2).min(6);
                        new_allocation.memory_bytes =
                            (current.memory_bytes + 1_000_000_000).min(6_000_000_000);
                    }
                    _ => {
                        new_allocation.cpu_cores = (current.cpu_cores + 1).min(4);
                        new_allocation.memory_bytes =
                            (current.memory_bytes + 500_000_000).min(4_000_000_000);
                    }
                }
            }
            _ => {
                // Minor adjustments for other optimization types
                new_allocation.priority = (current.priority + 10).min(100);
            }
        }

        new_allocation.allocated_at = Utc::now();
        Ok(new_allocation)
    }

    async fn apply_allocation(
        &self,
        module_name: &str,
        allocation: ResourceAllocation,
    ) -> Result<()> {
        {
            let mut allocations = self.current_allocations.write().expect("lock poisoned");
            allocations.insert(module_name.to_string(), allocation.clone());
        }

        // Record allocation event
        let event = AllocationEvent {
            module_name: module_name.to_string(),
            event_type: AllocationType::Rebalance,
            allocation,
            performance_impact: None,
            timestamp: Utc::now(),
        };

        {
            let mut history = self.allocation_history.write().expect("lock poisoned");
            history.push_back(event);

            // Keep only last 1000 events
            if history.len() > 1000 {
                history.pop_front();
            }
        }

        Ok(())
    }
}

impl PredictivePerformanceEngine {
    fn new() -> Self {
        Self {
            models: Arc::new(RwLock::new(HashMap::new())),
            prediction_cache: Arc::new(RwLock::new(HashMap::new())),
            learning_engine: LearningEngine::new(),
            anomaly_detector: AnomalyDetector::new(),
        }
    }

    async fn detect_anomalies(
        &self,
        performance_data: &HashMap<String, ModuleMetrics>,
    ) -> Result<Vec<AnomalyEvent>> {
        self.anomaly_detector.detect(performance_data).await
    }

    async fn update_models(
        &self,
        recommendation: &OptimizationRecommendation,
        actual_impact: &PerformanceImpact,
    ) -> Result<()> {
        self.learning_engine
            .update_model(recommendation, actual_impact)
            .await
    }
}

impl LearningEngine {
    fn new() -> Self {
        Self {
            learning_rate: 0.01,
            training_samples: Arc::new(RwLock::new(VecDeque::new())),
            update_frequency: Duration::from_secs(3600), // 1 hour
            baselines: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    async fn update_model(
        &self,
        recommendation: &OptimizationRecommendation,
        actual_impact: &PerformanceImpact,
    ) -> Result<()> {
        let sample = TrainingSample {
            features: vec![
                recommendation.estimated_impact.overall_score,
                recommendation.priority.clone() as u8 as f64,
            ],
            target: actual_impact.overall_score,
            context: HashMap::new(),
            weight: 1.0,
            timestamp: Utc::now(),
        };

        {
            let mut samples = self.training_samples.write().expect("lock poisoned");
            samples.push_back(sample);

            // Keep only last 10000 samples
            if samples.len() > 10000 {
                samples.pop_front();
            }
        }

        Ok(())
    }
}

impl AnomalyDetector {
    fn new() -> Self {
        Self {
            algorithms: vec![
                AnomalyAlgorithm::StatisticalOutlier { z_threshold: 3.0 },
                AnomalyAlgorithm::IsolationForest { contamination: 0.1 },
            ],
            thresholds: HashMap::new(),
            anomaly_history: Arc::new(RwLock::new(VecDeque::new())),
            false_positive_rate: 0.05,
        }
    }

    async fn detect(
        &self,
        performance_data: &HashMap<String, ModuleMetrics>,
    ) -> Result<Vec<AnomalyEvent>> {
        let mut anomalies = Vec::new();

        for (module_name, metrics) in performance_data {
            // Check for performance anomalies
            if metrics.cpu_usage > 90.0
                || metrics.error_rate > 5.0
                || metrics.avg_response_time > Duration::from_millis(1000)
            {
                let anomaly = AnomalyEvent {
                    module_name: module_name.clone(),
                    anomaly_type: AnomalyType::PerformanceDegradation,
                    severity: if metrics.cpu_usage > 95.0 || metrics.error_rate > 10.0 {
                        SeverityLevel::Critical
                    } else {
                        SeverityLevel::High
                    },
                    score: calculate_anomaly_score(metrics),
                    affected_metrics: vec![
                        "cpu_usage".to_string(),
                        "error_rate".to_string(),
                        "response_time".to_string(),
                    ],
                    recommended_actions: vec![
                        "Increase resource allocation".to_string(),
                        "Investigate error sources".to_string(),
                        "Optimize critical paths".to_string(),
                    ],
                    detected_at: Utc::now(),
                    resolved_at: None,
                };
                anomalies.push(anomaly);
            }

            // Check for memory issues
            if metrics.memory_usage > 12_000_000_000 {
                // 12GB
                let anomaly = AnomalyEvent {
                    module_name: module_name.clone(),
                    anomaly_type: AnomalyType::MemoryLeak,
                    severity: SeverityLevel::High,
                    score: (metrics.memory_usage as f64 / 16_000_000_000.0) * 100.0,
                    affected_metrics: vec!["memory_usage".to_string()],
                    recommended_actions: vec![
                        "Investigate memory usage patterns".to_string(),
                        "Enable memory profiling".to_string(),
                        "Implement memory cleanup".to_string(),
                    ],
                    detected_at: Utc::now(),
                    resolved_at: None,
                };
                anomalies.push(anomaly);
            }
        }

        // Store anomalies in history
        {
            let mut history = self.anomaly_history.write().expect("lock poisoned");
            for anomaly in &anomalies {
                history.push_back(anomaly.clone());
            }

            // Keep only last 1000 anomalies
            while history.len() > 1000 {
                history.pop_front();
            }
        }

        Ok(anomalies)
    }
}

impl ModulePerformanceMonitor {
    fn new(module_name: String) -> Self {
        Self {
            module_name,
            metrics: Arc::new(RwLock::new(ModuleMetrics {
                cpu_usage: 0.0,
                memory_usage: 0,
                gpu_memory_usage: None,
                network_io_bps: 0,
                disk_io_bps: 0,
                request_rate: 0.0,
                avg_response_time: Duration::from_millis(0),
                error_rate: 0.0,
                cache_hit_rate: 0.0,
                active_connections: 0,
                queue_depth: 0,
            })),
            resource_tracker: ResourceTracker::new(),
            history: Arc::new(RwLock::new(VecDeque::new())),
            prediction_model: PredictionModel::new(),
        }
    }

    async fn update_metrics(&self, new_metrics: ModuleMetrics) -> Result<()> {
        {
            let mut metrics = self.metrics.write().expect("lock poisoned");
            *metrics = new_metrics.clone();
        }

        // Store in history
        let snapshot = PerformanceSnapshot {
            metrics: new_metrics,
            timestamp: Utc::now(),
        };

        {
            let mut history = self.history.write().expect("lock poisoned");
            history.push_back(snapshot);

            // Keep only last 1000 snapshots
            if history.len() > 1000 {
                history.pop_front();
            }
        }

        Ok(())
    }

    async fn get_current_metrics(&self) -> Result<ModuleMetrics> {
        let metrics = self.metrics.read().expect("lock poisoned");
        Ok(metrics.clone())
    }
}

/// Resource tracker for monitoring resource usage
#[derive(Debug, Clone)]
pub struct ResourceTracker {
    /// CPU usage history
    cpu_history: Arc<RwLock<VecDeque<f64>>>,
    /// Memory usage history
    memory_history: Arc<RwLock<VecDeque<u64>>>,
    /// Last update time
    last_update: Arc<RwLock<DateTime<Utc>>>,
}

impl ResourceTracker {
    fn new() -> Self {
        Self {
            cpu_history: Arc::new(RwLock::new(VecDeque::new())),
            memory_history: Arc::new(RwLock::new(VecDeque::new())),
            last_update: Arc::new(RwLock::new(Utc::now())),
        }
    }
}

/// Performance snapshot
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    /// Metrics at this point in time
    pub metrics: ModuleMetrics,
    /// Timestamp
    pub timestamp: DateTime<Utc>,
}

/// Prediction model
#[derive(Debug, Clone)]
pub struct PredictionModel {
    /// Model parameters
    parameters: HashMap<String, f64>,
    /// Last training time
    last_trained: DateTime<Utc>,
}

impl PredictionModel {
    fn new() -> Self {
        Self {
            parameters: HashMap::new(),
            last_trained: Utc::now(),
        }
    }
}

/// Allocation strategy
#[derive(Debug, Clone)]
pub enum AllocationStrategy {
    Proportional,
    PriorityBased,
    PerformanceBased,
    Predictive,
}

/// Helper functions
fn calculate_percentage_change(old_value: f64, new_value: f64) -> f64 {
    if old_value == 0.0 {
        return 0.0;
    }
    ((new_value - old_value) / old_value) * 100.0
}

fn calculate_anomaly_score(metrics: &ModuleMetrics) -> f64 {
    let cpu_score = if metrics.cpu_usage > 80.0 {
        metrics.cpu_usage
    } else {
        0.0
    };
    let error_score = metrics.error_rate * 10.0;
    let latency_score = if metrics.avg_response_time > Duration::from_millis(500) {
        metrics.avg_response_time.as_millis() as f64 / 10.0
    } else {
        0.0
    };

    (cpu_score + error_score + latency_score) / 3.0
}

/// Comprehensive tests for the cross-module performance coordinator
#[cfg(test)]
mod tests {
    use super::*;
    use tokio;

    #[tokio::test]
    async fn test_coordinator_creation() {
        let config = CoordinatorConfig::default();
        let coordinator = CrossModulePerformanceCoordinator::new(config);
        assert_eq!(coordinator.module_monitors.read().unwrap().len(), 0);
    }

    #[tokio::test]
    async fn test_module_registration() {
        let config = CoordinatorConfig::default();
        let coordinator = CrossModulePerformanceCoordinator::new(config);

        let result = coordinator.register_module("test_module".to_string()).await;
        assert!(result.is_ok());
        assert_eq!(coordinator.module_monitors.read().unwrap().len(), 1);
    }

    #[tokio::test]
    async fn test_metrics_update() {
        let config = CoordinatorConfig::default();
        let coordinator = CrossModulePerformanceCoordinator::new(config);

        coordinator
            .register_module("test_module".to_string())
            .await
            .unwrap();

        let metrics = ModuleMetrics {
            cpu_usage: 75.0,
            memory_usage: 4_000_000_000,
            gpu_memory_usage: Some(2_000_000_000),
            network_io_bps: 1_000_000,
            disk_io_bps: 500_000,
            request_rate: 100.0,
            avg_response_time: Duration::from_millis(150),
            error_rate: 2.0,
            cache_hit_rate: 85.0,
            active_connections: 50,
            queue_depth: 10,
        };

        let result = coordinator
            .update_module_metrics("test_module", metrics)
            .await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_anomaly_detection() {
        let detector = AnomalyDetector::new();

        let mut performance_data = HashMap::new();
        performance_data.insert(
            "test_module".to_string(),
            ModuleMetrics {
                cpu_usage: 95.0, // High CPU usage should trigger anomaly
                memory_usage: 4_000_000_000,
                gpu_memory_usage: Some(2_000_000_000),
                network_io_bps: 1_000_000,
                disk_io_bps: 500_000,
                request_rate: 100.0,
                avg_response_time: Duration::from_millis(1500), // High latency
                error_rate: 8.0,                                // High error rate
                cache_hit_rate: 85.0,
                active_connections: 50,
                queue_depth: 10,
            },
        );

        let anomalies = detector.detect(&performance_data).await.unwrap();
        assert!(!anomalies.is_empty());
        assert_eq!(
            anomalies[0].anomaly_type,
            AnomalyType::PerformanceDegradation
        );
    }

    #[tokio::test]
    async fn test_resource_allocation() {
        let allocator = ResourceAllocator::new();

        let recommendation = OptimizationRecommendation {
            module_name: "test_module".to_string(),
            optimization_type: OptimizationType::ResourceReallocation,
            priority: Priority::High,
            description: "Test optimization".to_string(),
            estimated_impact: PerformanceImpact {
                latency_change_pct: -20.0,
                throughput_change_pct: 30.0,
                efficiency_change_pct: 15.0,
                overall_score: 80.0,
            },
            implementation_steps: vec!["Increase CPU allocation".to_string()],
        };

        let result = allocator
            .reallocate_resources("test_module", &recommendation)
            .await;
        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_percentage_change_calculation() {
        assert_eq!(calculate_percentage_change(100.0, 120.0), 20.0);
        assert_eq!(calculate_percentage_change(100.0, 80.0), -20.0);
        assert_eq!(calculate_percentage_change(0.0, 100.0), 0.0);
    }

    #[tokio::test]
    async fn test_anomaly_score_calculation() {
        let metrics = ModuleMetrics {
            cpu_usage: 90.0,
            memory_usage: 4_000_000_000,
            gpu_memory_usage: Some(2_000_000_000),
            network_io_bps: 1_000_000,
            disk_io_bps: 500_000,
            request_rate: 100.0,
            avg_response_time: Duration::from_millis(800),
            error_rate: 5.0,
            cache_hit_rate: 85.0,
            active_connections: 50,
            queue_depth: 10,
        };

        let score = calculate_anomaly_score(&metrics);
        assert!(score > 0.0);
        assert!(score > 50.0); // Should be high due to high CPU and error rate
    }

    #[tokio::test]
    async fn test_optimization_cache() {
        let cache = OptimizationCache::new();
        assert_eq!(cache.stats.size.load(Ordering::SeqCst), 0);
    }

    #[tokio::test]
    async fn test_module_monitor_creation() {
        let monitor = ModulePerformanceMonitor::new("test_module".to_string());
        assert_eq!(monitor.module_name, "test_module");

        let metrics = monitor.get_current_metrics().await.unwrap();
        assert_eq!(metrics.cpu_usage, 0.0);
    }

    #[tokio::test]
    async fn test_prediction_model() {
        let model = PredictionModel::new();
        assert!(model.parameters.is_empty());
    }
}