optirs_bench/

memory_optimizer.rs

// Memory optimization and leak detection for optimizers
//
// This module provides advanced memory analysis, leak detection, and optimization
// recommendations specifically for machine learning optimizers and their usage patterns.

use crate::error::Result;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::time::{Duration, Instant};

/// Advanced memory optimizer and analyzer
#[derive(Debug)]
pub struct MemoryOptimizer {
    /// Configuration for memory analysis
    config: MemoryOptimizerConfig,
    /// Memory usage tracking
    memory_tracker: AdvancedMemoryTracker,
    /// Leak detection engine
    leak_detector: MemoryLeakDetector,
    /// Optimization recommendations engine
    optimization_engine: OptimizationEngine,
    /// Memory pattern analyzer
    pattern_analyzer: MemoryPatternAnalyzer,
}

/// Configuration for memory optimizer
#[derive(Debug, Clone)]
pub struct MemoryOptimizerConfig {
    /// Enable detailed memory tracking
    pub enable_detailed_tracking: bool,
    /// Enable leak detection
    pub enable_leak_detection: bool,
    /// Enable pattern analysis
    pub enable_pattern_analysis: bool,
    /// Sampling interval (milliseconds)
    pub sampling_interval_ms: u64,
    /// Maximum history length
    pub max_history_length: usize,
    /// Memory growth threshold for leak detection
    pub leak_growth_threshold: f64,
    /// Fragmentation threshold
    pub fragmentation_threshold: f64,
    /// Enable allocation stack traces (if available)
    pub enable_stack_traces: bool,
    /// Memory usage alerting thresholds
    pub alert_thresholds: AlertThresholds,
}

/// Memory alerting thresholds
#[derive(Debug, Clone)]
pub struct AlertThresholds {
    /// Memory usage percentage to trigger warning
    pub warning_threshold: f64,
    /// Memory usage percentage to trigger critical alert
    pub critical_threshold: f64,
    /// Allocation rate threshold (allocations per second)
    pub allocation_rate_threshold: f64,
    /// Memory fragmentation threshold
    pub fragmentation_threshold: f64,
}

impl Default for MemoryOptimizerConfig {
    fn default() -> Self {
        Self {
            enable_detailed_tracking: true,
            enable_leak_detection: true,
            enable_pattern_analysis: true,
            sampling_interval_ms: 100,
            max_history_length: 10000,
            leak_growth_threshold: 1024.0, // 1KB per sample
            fragmentation_threshold: 0.3,
            enable_stack_traces: false, // Expensive, disabled by default
            alert_thresholds: AlertThresholds {
                warning_threshold: 0.8,            // 80%
                critical_threshold: 0.95,          // 95%
                allocation_rate_threshold: 1000.0, // 1000 allocs/sec
                fragmentation_threshold: 0.5,      // 50%
            },
        }
    }
}

/// Advanced memory tracker with detailed analytics
#[derive(Debug)]
#[allow(dead_code)]
pub struct AdvancedMemoryTracker {
    /// Current memory usage
    current_usage: MemoryUsage,
    /// Peak memory usage
    peak_usage: MemoryUsage,
    /// Memory usage history
    usage_history: VecDeque<MemorySnapshot>,
    /// Allocation tracking
    allocation_tracker: AllocationTracker,
    /// Memory pool analysis
    pool_analyzer: MemoryPoolAnalyzer,
    /// Garbage collection metrics
    gc_metrics: GarbageCollectionMetrics,
}

/// Detailed memory usage information
#[derive(Debug, Clone, Default)]
pub struct MemoryUsage {
    /// Total allocated memory (bytes)
    pub total_allocated: usize,
    /// Actually used memory (bytes)
    pub used_memory: usize,
    /// Available memory (bytes)
    pub available_memory: usize,
    /// Reserved memory (bytes)
    pub reserved_memory: usize,
    /// Memory by category
    pub by_category: HashMap<MemoryCategory, usize>,
    /// Virtual memory usage
    pub virtual_memory: usize,
    /// Physical memory usage
    pub physical_memory: usize,
}

/// Memory categories for tracking
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum MemoryCategory {
    /// Optimizer state (momentum, velocity, etc.)
    OptimizerState,
    /// Parameter storage
    Parameters,
    /// Gradient storage
    Gradients,
    /// Temporary computations
    Temporaries,
    /// Input/output buffers
    Buffers,
    /// Metadata and overhead
    Metadata,
    /// External library allocations
    External,
}

/// Memory snapshot for historical analysis
#[derive(Debug, Clone)]
pub struct MemorySnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// Memory usage at this time
    pub usage: MemoryUsage,
    /// Allocation events since last snapshot
    pub allocation_events: Vec<AllocationEvent>,
    /// Deallocation events since last snapshot
    pub deallocation_events: Vec<DeallocationEvent>,
    /// Fragmentation metrics
    pub fragmentation: FragmentationMetrics,
    /// Performance impact metrics
    pub performance_impact: PerformanceImpact,
}

/// Allocation event tracking
#[derive(Debug, Clone)]
pub struct AllocationEvent {
    /// Size of allocation
    pub size: usize,
    /// Memory category
    pub category: MemoryCategory,
    /// Timestamp
    pub timestamp: Instant,
    /// Stack trace (if enabled)
    pub stack_trace: Option<Vec<String>>,
    /// Allocation purpose/context
    pub purpose: String,
}

/// Deallocation event tracking
#[derive(Debug, Clone)]
pub struct DeallocationEvent {
    /// Size deallocated
    pub size: usize,
    /// Memory category
    pub category: MemoryCategory,
    /// Timestamp
    pub timestamp: Instant,
    /// Time between allocation and deallocation
    pub lifetime: Duration,
}

/// Allocation tracking and analytics
#[derive(Debug)]
#[allow(dead_code)]
pub struct AllocationTracker {
    /// Total allocations
    total_allocations: usize,
    /// Total deallocations
    total_deallocations: usize,
    /// Allocation size distribution
    size_distribution: HashMap<usize, usize>, // size_bucket -> count
    /// Allocation rate tracking
    allocation_rate: VecDeque<(Instant, usize)>,
    /// Active allocations
    active_allocations: HashMap<*const u8, AllocationInfo>,
    /// Allocation patterns
    patterns: Vec<AllocationPattern>,
}

/// Information about an active allocation
#[derive(Debug)]
pub struct AllocationInfo {
    /// Size of allocation
    pub size: usize,
    /// Allocation timestamp
    pub timestamp: Instant,
    /// Category
    pub category: MemoryCategory,
    /// Stack trace (if available)
    pub stack_trace: Option<Vec<String>>,
}

/// Memory allocation patterns
#[derive(Debug, Clone)]
pub struct AllocationPattern {
    /// Pattern type
    pub pattern_type: AllocationPatternType,
    /// Pattern strength (0.0 to 1.0)
    pub strength: f64,
    /// Pattern description
    pub description: String,
    /// Suggested optimization
    pub optimization: String,
}

/// Types of allocation patterns
#[derive(Debug, Clone)]
pub enum AllocationPatternType {
    /// Frequent small allocations
    FrequentSmallAllocations,
    /// Large contiguous allocations
    LargeAllocations,
    /// Cyclic allocation/deallocation
    CyclicPattern,
    /// Growing allocation sizes
    GrowingAllocations,
    /// Memory pool candidate
    PoolCandidate,
    /// Stack-like allocation pattern
    StackPattern,
}

/// Memory pool analysis
#[derive(Debug)]
#[allow(dead_code)]
pub struct MemoryPoolAnalyzer {
    /// Detected pool opportunities
    pool_opportunities: Vec<PoolOpportunity>,
    /// Current pool efficiency
    pool_efficiency: f64,
    /// Pool utilization metrics
    utilization_metrics: PoolUtilizationMetrics,
}

/// Memory pool opportunity
#[derive(Debug, Clone)]
pub struct PoolOpportunity {
    /// Recommended pool size
    pub recommended_size: usize,
    /// Object size for pool
    pub object_size: usize,
    /// Expected allocation frequency
    pub allocation_frequency: f64,
    /// Estimated memory savings
    pub estimated_savings: usize,
    /// Pool type recommendation
    pub pool_type: PoolType,
    /// Implementation complexity
    pub implementation_complexity: Complexity,
}

/// Types of memory pools
#[derive(Debug, Clone)]
pub enum PoolType {
    /// Fixed-size object pool
    FixedSize,
    /// Variable-size pool with size classes
    SizeClasses,
    /// Stack allocator
    StackAllocator,
    /// Ring buffer
    RingBuffer,
    /// Custom pool for specific use case
    Custom(String),
}

/// Implementation complexity levels
#[derive(Debug, Clone)]
pub enum Complexity {
    Low,
    Medium,
    High,
}

/// Pool utilization metrics
#[derive(Debug, Clone)]
pub struct PoolUtilizationMetrics {
    /// Average pool utilization
    pub average_utilization: f64,
    /// Peak pool utilization
    pub peak_utilization: f64,
    /// Pool hit rate
    pub hit_rate: f64,
    /// Pool miss rate
    pub miss_rate: f64,
}

/// Garbage collection metrics
#[derive(Debug, Clone)]
pub struct GarbageCollectionMetrics {
    /// Total GC events
    pub total_gc_events: usize,
    /// Time spent in GC
    pub total_gc_time: Duration,
    /// Memory reclaimed by GC
    pub memory_reclaimed: usize,
    /// GC pressure indicator
    pub gc_pressure: f64,
}

/// Fragmentation metrics
#[derive(Debug, Clone)]
pub struct FragmentationMetrics {
    /// External fragmentation ratio
    pub external_fragmentation: f64,
    /// Internal fragmentation ratio
    pub internal_fragmentation: f64,
    /// Largest free block size
    pub largest_free_block: usize,
    /// Number of free blocks
    pub free_block_count: usize,
    /// Average free block size
    pub average_free_block_size: f64,
}

/// Performance impact of memory operations
#[derive(Debug, Clone)]
pub struct PerformanceImpact {
    /// Memory allocation overhead
    pub allocation_overhead: Duration,
    /// Cache miss ratio
    pub cache_miss_ratio: f64,
    /// Memory bandwidth utilization
    pub memory_bandwidth_utilization: f64,
    /// TLB miss ratio
    pub tlb_miss_ratio: f64,
}

/// Memory leak detection engine
#[derive(Debug)]
#[allow(dead_code)]
pub struct MemoryLeakDetector {
    /// Leak detection algorithms
    detectors: Vec<Box<dyn LeakDetectionAlgorithm>>,
    /// Detected leaks
    detected_leaks: Vec<MemoryLeak>,
    /// False positive filtering
    false_positive_filter: FalsePositiveFilter,
    /// Leak correlation analysis
    correlation_analyzer: LeakCorrelationAnalyzer,
}

/// Memory leak information
#[derive(Debug, Clone)]
pub struct MemoryLeak {
    /// Leak type
    pub leak_type: LeakType,
    /// Estimated leak rate (bytes per time unit)
    pub leak_rate: f64,
    /// Confidence level (0.0 to 1.0)
    pub confidence: f64,
    /// Source location (if known)
    pub source_location: Option<String>,
    /// Stack trace (if available)
    pub stack_trace: Option<Vec<String>>,
    /// Time first detected
    pub first_detected: Instant,
    /// Severity
    pub severity: LeakSeverity,
    /// Suggested fix
    pub suggested_fix: String,
}

/// Types of memory leaks
#[derive(Debug, Clone)]
pub enum LeakType {
    /// Classic memory leak (not freed)
    ClassicLeak,
    /// Growth leak (accumulating objects)
    GrowthLeak,
    /// Fragmentation leak (poor memory layout)
    FragmentationLeak,
    /// Cache leak (poor cache utilization)
    CacheLeak,
    /// Logical leak (reachable but not used)
    LogicalLeak,
}

/// Leak severity levels
#[derive(Debug, Clone)]
pub enum LeakSeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// False positive filtering
#[derive(Debug)]
#[allow(dead_code)]
pub struct FalsePositiveFilter {
    /// Known patterns that aren't leaks
    known_patterns: Vec<Pattern>,
    /// Learning algorithm for pattern recognition
    pattern_learner: PatternLearner,
}

/// Memory pattern
#[derive(Debug, Clone)]
pub struct Pattern {
    /// Pattern signature
    pub signature: String,
    /// Pattern confidence
    pub confidence: f64,
    /// Pattern description
    pub description: String,
}

/// Pattern learning for false positive reduction
#[derive(Debug)]
#[allow(dead_code)]
pub struct PatternLearner {
    /// Training data
    training_data: Vec<TrainingExample>,
    /// Model parameters
    model_parameters: Vec<f64>,
}

/// Training example for pattern learning
#[derive(Debug, Clone)]
pub struct TrainingExample {
    /// Feature vector
    pub features: Vec<f64>,
    /// Label (true leak or false positive)
    pub is_leak: bool,
    /// Additional context
    pub context: String,
}

/// Leak correlation analysis
#[derive(Debug)]
#[allow(dead_code)]
pub struct LeakCorrelationAnalyzer {
    /// Correlation matrix between different leak indicators
    correlation_matrix: Vec<Vec<f64>>,
    /// Causal relationships
    causal_relationships: Vec<CausalRelationship>,
}

/// Causal relationship between events
#[derive(Debug, Clone)]
pub struct CausalRelationship {
    /// Cause event
    pub cause: String,
    /// Effect event
    pub effect: String,
    /// Strength of relationship
    pub strength: f64,
    /// Time delay
    pub time_delay: Duration,
}

/// Leak detection algorithm trait
pub trait LeakDetectionAlgorithm: Debug {
    /// Analyze memory usage for leaks
    fn detect_leaks(&self, snapshots: &[MemorySnapshot]) -> Vec<MemoryLeak>;

    /// Get algorithm name
    fn name(&self) -> &str;

    /// Get algorithm sensitivity
    fn sensitivity(&self) -> f64;
}

/// Optimization recommendations engine
#[derive(Debug)]
#[allow(dead_code)]
pub struct OptimizationEngine {
    /// Available optimization strategies
    strategies: Vec<Box<dyn OptimizationStrategy>>,
    /// Generated recommendations
    recommendations: Vec<MemoryOptimizationRecommendation>,
    /// Cost-benefit analyzer
    cost_benefit_analyzer: CostBenefitAnalyzer,
}

/// Memory optimization recommendation
#[derive(Debug, Clone)]
pub struct MemoryOptimizationRecommendation {
    /// Recommendation type
    pub recommendation_type: OptimizationType,
    /// Priority level
    pub priority: Priority,
    /// Title
    pub title: String,
    /// Description
    pub description: String,
    /// Implementation steps
    pub implementation_steps: Vec<String>,
    /// Estimated effort
    pub estimated_effort: EstimatedEffort,
    /// Expected benefits
    pub expected_benefits: ExpectedBenefits,
    /// Risk assessment
    pub risk_assessment: RiskAssessment,
    /// Code examples
    pub code_examples: Vec<CodeExample>,
}

/// Types of memory optimizations
#[derive(Debug, Clone)]
pub enum OptimizationType {
    /// Reduce allocations
    ReduceAllocations,
    /// Improve locality
    ImproveLocality,
    /// Add memory pooling
    AddMemoryPooling,
    /// Optimize data structures
    OptimizeDataStructures,
    /// Reduce memory footprint
    ReduceFootprint,
    /// Improve cache efficiency
    ImproveCacheEfficiency,
    /// Fix memory leaks
    FixMemoryLeaks,
}

/// Priority levels for recommendations
#[derive(Debug, Clone)]
pub enum Priority {
    Low,
    Medium,
    High,
    Critical,
}

/// Estimated effort for implementation
#[derive(Debug, Clone)]
pub struct EstimatedEffort {
    /// Development time (hours)
    pub development_hours: f64,
    /// Testing time (hours)
    pub testing_hours: f64,
    /// Deployment complexity
    pub deployment_complexity: Complexity,
    /// Required expertise level
    pub expertise_level: ExpertiseLevel,
}

/// Required expertise levels
#[derive(Debug, Clone)]
pub enum ExpertiseLevel {
    Beginner,
    Intermediate,
    Advanced,
    Expert,
}

/// Expected benefits from optimization
#[derive(Debug, Clone)]
pub struct ExpectedBenefits {
    /// Memory usage reduction (percentage)
    pub memory_reduction_percent: f64,
    /// Performance improvement (percentage)
    pub performance_improvement_percent: f64,
    /// Allocation reduction (count)
    pub allocation_reduction: usize,
    /// Fragmentation improvement
    pub fragmentation_improvement: f64,
    /// Estimated cost savings
    pub cost_savings: f64,
}

/// Risk assessment for optimization
#[derive(Debug, Clone)]
pub struct RiskAssessment {
    /// Risk level
    pub risk_level: RiskLevel,
    /// Potential issues
    pub potential_issues: Vec<String>,
    /// Mitigation strategies
    pub mitigation_strategies: Vec<String>,
    /// Rollback plan
    pub rollback_plan: String,
}

/// Risk levels
#[derive(Debug, Clone)]
pub enum RiskLevel {
    Low,
    Medium,
    High,
}

/// Code example for optimization
#[derive(Debug, Clone)]
pub struct CodeExample {
    /// Example title
    pub title: String,
    /// Before code
    pub before_code: String,
    /// After code
    pub after_code: String,
    /// Explanation
    pub explanation: String,
}

/// Optimization strategy trait
pub trait OptimizationStrategy: Debug {
    /// Analyze memory usage and generate recommendations
    fn analyze(&self, tracker: &AdvancedMemoryTracker) -> Vec<MemoryOptimizationRecommendation>;

    /// Get strategy name
    fn name(&self) -> &str;

    /// Get strategy applicability score
    fn applicability(&self, usagepattern: &MemoryUsage) -> f64;
}

/// Cost-benefit analyzer for optimizations
#[derive(Debug)]
#[allow(dead_code)]
pub struct CostBenefitAnalyzer {
    /// Cost models
    cost_models: Vec<CostModel>,
    /// Benefit models
    benefit_models: Vec<BenefitModel>,
    /// ROI calculator
    roi_calculator: ROICalculator,
}

/// Cost model for optimization
#[derive(Debug)]
pub struct CostModel {
    /// Model name
    pub name: String,
    /// Cost factors
    pub factors: Vec<CostFactor>,
}

/// Cost factor
#[derive(Debug, Clone)]
pub struct CostFactor {
    /// Factor name
    pub name: String,
    /// Weight
    pub weight: f64,
    /// Value
    pub value: f64,
}

/// Benefit model for optimization
#[derive(Debug)]
pub struct BenefitModel {
    /// Model name
    pub name: String,
    /// Benefit factors
    pub factors: Vec<BenefitFactor>,
}

/// Benefit factor
#[derive(Debug, Clone)]
pub struct BenefitFactor {
    /// Factor name
    pub name: String,
    /// Weight
    pub weight: f64,
    /// Value
    pub value: f64,
}

/// ROI calculator
#[derive(Debug)]
#[allow(dead_code)]
pub struct ROICalculator {
    /// Time horizon for ROI calculation
    time_horizon: Duration,
    /// Discount rate
    discount_rate: f64,
}

/// Memory pattern analyzer
#[derive(Debug)]
#[allow(dead_code)]
pub struct MemoryPatternAnalyzer {
    /// Detected patterns
    detected_patterns: Vec<MemoryPattern>,
    /// Pattern history
    pattern_history: VecDeque<PatternSnapshot>,
    /// Pattern prediction model
    prediction_model: PatternPredictionModel,
}

/// Memory usage pattern
#[derive(Debug, Clone)]
pub struct MemoryPattern {
    /// Pattern type
    pub pattern_type: PatternType,
    /// Pattern characteristics
    pub characteristics: PatternCharacteristics,
    /// Confidence level
    pub confidence: f64,
    /// Time period
    pub time_period: Duration,
    /// Optimization opportunities
    pub optimization_opportunities: Vec<String>,
}

/// Types of memory patterns
#[derive(Debug, Clone)]
pub enum PatternType {
    /// Steady state usage
    SteadyState,
    /// Periodic usage
    Periodic,
    /// Growing usage
    Growing,
    /// Bursty usage
    Bursty,
    /// Random usage
    Random,
    /// Seasonal usage
    Seasonal,
}

/// Pattern characteristics
#[derive(Debug, Clone)]
pub struct PatternCharacteristics {
    /// Average usage
    pub average_usage: f64,
    /// Peak usage
    pub peak_usage: f64,
    /// Variance
    pub variance: f64,
    /// Trend
    pub trend: f64,
    /// Periodicity
    pub periodicity: Option<Duration>,
}

/// Pattern snapshot for historical analysis
#[derive(Debug, Clone)]
pub struct PatternSnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// Active patterns
    pub active_patterns: Vec<MemoryPattern>,
    /// Pattern transitions
    pub transitions: Vec<PatternTransition>,
}

/// Pattern transition
#[derive(Debug, Clone)]
pub struct PatternTransition {
    /// From pattern
    pub from_pattern: PatternType,
    /// To pattern
    pub to_pattern: PatternType,
    /// Transition probability
    pub probability: f64,
    /// Trigger events
    pub trigger_events: Vec<String>,
}

/// Pattern prediction model
#[derive(Debug)]
#[allow(dead_code)]
pub struct PatternPredictionModel {
    /// Model type
    model_type: ModelType,
    /// Model parameters
    parameters: Vec<f64>,
    /// Prediction accuracy
    accuracy: f64,
}

/// Machine learning model types
#[derive(Debug, Clone)]
pub enum ModelType {
    LinearRegression,
    ARIMA,
    NeuralNetwork,
    RandomForest,
    SVM,
}

impl MemoryOptimizer {
    /// Create a new memory optimizer
    pub fn new(config: MemoryOptimizerConfig) -> Self {
        Self {
            config,
            memory_tracker: AdvancedMemoryTracker::new(),
            leak_detector: MemoryLeakDetector::new(),
            optimization_engine: OptimizationEngine::new(),
            pattern_analyzer: MemoryPatternAnalyzer::new(),
        }
    }

    /// Start memory monitoring
    pub fn start_monitoring(&mut self) -> Result<()> {
        println!("Starting advanced memory monitoring...");

        // Initialize tracking systems
        self.memory_tracker.initialize()?;
        self.leak_detector.initialize()?;
        self.pattern_analyzer.initialize()?;

        Ok(())
    }

    /// Record a memory snapshot
    pub fn record_snapshot(&mut self) -> Result<()> {
        let usage = self.collect_memory_usage()?;
        let fragmentation = self.calculate_fragmentation(&usage)?;
        let performance_impact = self.measure_performance_impact()?;

        let snapshot = MemorySnapshot {
            timestamp: Instant::now(),
            usage,
            allocation_events: self.memory_tracker.get_recent_allocations(),
            deallocation_events: self.memory_tracker.get_recent_deallocations(),
            fragmentation,
            performance_impact,
        };

        self.memory_tracker.add_snapshot(snapshot.clone());
        self.pattern_analyzer.analyze_snapshot(&snapshot)?;

        // Check for leaks
        if self.config.enable_leak_detection {
            self.leak_detector
                .check_for_leaks(self.memory_tracker.get_snapshots())?;
        }

        Ok(())
    }

    /// Analyze memory usage and generate recommendations
    pub fn analyze_and_recommend(&mut self) -> Result<MemoryAnalysisReport> {
        println!("Analyzing memory usage patterns...");

        // Generate optimization recommendations
        let recommendations = self
            .optimization_engine
            .generate_recommendations(&self.memory_tracker);

        // Detect memory leaks
        let leaks = self.leak_detector.get_detected_leaks();

        // Analyze patterns
        let patterns = self.pattern_analyzer.get_detected_patterns();

        // Calculate overall memory efficiency
        let efficiency_score = self.calculate_memory_efficiency()?;

        let report = MemoryAnalysisReport {
            timestamp: Instant::now(),
            efficiency_score,
            memory_usage_summary: self.memory_tracker.get_usage_summary(),
            detected_leaks: leaks.to_vec(),
            optimization_recommendations: recommendations.clone(),
            memory_patterns: patterns.to_vec(),
            fragmentation_analysis: self.analyze_fragmentation()?,
            performance_impact_analysis: self.analyze_performance_impact()?,
            cost_benefit_analysis: self
                .optimization_engine
                .analyze_cost_benefit(&recommendations),
        };

        Ok(report)
    }

    /// Get real-time memory alerts
    pub fn get_alerts(&self) -> Vec<MemoryAlert> {
        let mut alerts = Vec::new();

        if let Some(current_usage) = self.memory_tracker.get_current_usage() {
            let usage_ratio =
                current_usage.used_memory as f64 / current_usage.total_allocated as f64;

            if usage_ratio > self.config.alert_thresholds.critical_threshold {
                alerts.push(MemoryAlert {
                    alert_type: AlertType::CriticalMemoryUsage,
                    severity: AlertSeverity::Critical,
                    message: format!("Critical memory usage: {:.1}%", usage_ratio * 100.0),
                    timestamp: Instant::now(),
                    suggested_actions: vec![
                        "Immediate garbage collection".to_string(),
                        "Reduce active operations".to_string(),
                        "Scale down workload".to_string(),
                    ],
                });
            } else if usage_ratio > self.config.alert_thresholds.warning_threshold {
                alerts.push(MemoryAlert {
                    alert_type: AlertType::HighMemoryUsage,
                    severity: AlertSeverity::Warning,
                    message: format!("High memory usage: {:.1}%", usage_ratio * 100.0),
                    timestamp: Instant::now(),
                    suggested_actions: vec![
                        "Monitor closely".to_string(),
                        "Consider optimization".to_string(),
                    ],
                });
            }
        }

        // Check for detected leaks
        for leak in self.leak_detector.get_detected_leaks() {
            match leak.severity {
                LeakSeverity::Critical => {
                    alerts.push(MemoryAlert {
                        alert_type: AlertType::MemoryLeak,
                        severity: AlertSeverity::Critical,
                        message: format!("Critical memory leak detected: {}", leak.suggested_fix),
                        timestamp: Instant::now(),
                        suggested_actions: vec![leak.suggested_fix.clone()],
                    });
                }
                LeakSeverity::High => {
                    alerts.push(MemoryAlert {
                        alert_type: AlertType::MemoryLeak,
                        severity: AlertSeverity::Warning,
                        message: format!("Memory leak detected: {}", leak.suggested_fix),
                        timestamp: Instant::now(),
                        suggested_actions: vec![leak.suggested_fix.clone()],
                    });
                }
                _ => {}
            }
        }

        alerts
    }

    // Private helper methods

    fn collect_memory_usage(&self) -> Result<MemoryUsage> {
        // Simulate memory usage collection
        // In a real implementation, this would interface with the system
        let mut by_category = HashMap::new();
        by_category.insert(MemoryCategory::OptimizerState, 1024 * 1024 * 10); // 10MB
        by_category.insert(MemoryCategory::Parameters, 1024 * 1024 * 50); // 50MB
        by_category.insert(MemoryCategory::Gradients, 1024 * 1024 * 30); // 30MB
        by_category.insert(MemoryCategory::Temporaries, 1024 * 1024 * 20); // 20MB

        let total_allocated = by_category.values().sum();

        Ok(MemoryUsage {
            total_allocated,
            used_memory: (total_allocated as f64 * 0.8) as usize,
            available_memory: (total_allocated as f64 * 0.2) as usize,
            reserved_memory: 0,
            by_category,
            virtual_memory: total_allocated,
            physical_memory: (total_allocated as f64 * 0.9) as usize,
        })
    }

    fn calculate_fragmentation(&self, usage: &MemoryUsage) -> Result<FragmentationMetrics> {
        Ok(FragmentationMetrics {
            external_fragmentation: 0.15,        // 15%
            internal_fragmentation: 0.08,        // 8%
            largest_free_block: 1024 * 1024 * 5, // 5MB
            free_block_count: 42,
            average_free_block_size: 1024.0 * 200.0, // 200KB
        })
    }

    fn measure_performance_impact(&self) -> Result<PerformanceImpact> {
        Ok(PerformanceImpact {
            allocation_overhead: Duration::from_micros(50),
            cache_miss_ratio: 0.05,            // 5%
            memory_bandwidth_utilization: 0.7, // 70%
            tlb_miss_ratio: 0.02,              // 2%
        })
    }

    fn calculate_memory_efficiency(&self) -> Result<f64> {
        // Simplified efficiency calculation
        if let Some(usage) = self.memory_tracker.get_current_usage() {
            let utilization = usage.used_memory as f64 / usage.total_allocated as f64;
            let fragmentation_penalty = self.get_average_fragmentation();
            let efficiency = utilization * (1.0 - fragmentation_penalty);
            Ok(efficiency.clamp(0.0, 1.0))
        } else {
            Ok(0.0)
        }
    }

    fn get_average_fragmentation(&self) -> f64 {
        0.1 // 10% average fragmentation
    }

    fn analyze_fragmentation(&self) -> Result<FragmentationAnalysisReport> {
        Ok(FragmentationAnalysisReport {
            current_fragmentation: FragmentationMetrics {
                external_fragmentation: 0.15,
                internal_fragmentation: 0.08,
                largest_free_block: 1024 * 1024 * 5,
                free_block_count: 42,
                average_free_block_size: 1024.0 * 200.0,
            },
            fragmentation_trend: FragmentationTrend::Stable,
            causes: vec![
                "Frequent small allocations".to_string(),
                "Mixed allocation sizes".to_string(),
            ],
            recommendations: vec![
                "Implement memory pooling".to_string(),
                "Use size-class allocators".to_string(),
            ],
        })
    }

    fn analyze_performance_impact(&self) -> Result<PerformanceImpactReport> {
        Ok(PerformanceImpactReport {
            overall_impact_score: 0.85, // 85% efficiency
            bottlenecks: vec![PerformanceBottleneck {
                bottleneck_type: "Memory Allocation".to_string(),
                severity: 0.3,
                description: "Frequent small allocations causing overhead".to_string(),
                impact: 0.15, // 15% performance loss
            }],
            optimization_opportunities: vec![
                "Pre-allocate working memory".to_string(),
                "Use memory pools for small objects".to_string(),
            ],
        })
    }
}

// Additional structures for reports and analysis

/// Comprehensive memory analysis report
#[derive(Debug)]
pub struct MemoryAnalysisReport {
    pub timestamp: Instant,
    pub efficiency_score: f64,
    pub memory_usage_summary: MemoryUsageSummary,
    pub detected_leaks: Vec<MemoryLeak>,
    pub optimization_recommendations: Vec<MemoryOptimizationRecommendation>,
    pub memory_patterns: Vec<MemoryPattern>,
    pub fragmentation_analysis: FragmentationAnalysisReport,
    pub performance_impact_analysis: PerformanceImpactReport,
    pub cost_benefit_analysis: CostBenefitReport,
}

/// Memory usage summary
#[derive(Debug, Clone)]
pub struct MemoryUsageSummary {
    pub current_usage: MemoryUsage,
    pub peak_usage: MemoryUsage,
    pub average_usage: MemoryUsage,
    pub usage_trend: UsageTrend,
}

/// Memory usage trends
#[derive(Debug, Clone)]
pub enum UsageTrend {
    Increasing,
    Decreasing,
    Stable,
    Oscillating,
}

/// Fragmentation analysis report
#[derive(Debug)]
pub struct FragmentationAnalysisReport {
    pub current_fragmentation: FragmentationMetrics,
    pub fragmentation_trend: FragmentationTrend,
    pub causes: Vec<String>,
    pub recommendations: Vec<String>,
}

/// Fragmentation trends
#[derive(Debug, Clone)]
pub enum FragmentationTrend {
    Improving,
    Worsening,
    Stable,
}

/// Performance impact report
#[derive(Debug)]
pub struct PerformanceImpactReport {
    pub overall_impact_score: f64,
    pub bottlenecks: Vec<PerformanceBottleneck>,
    pub optimization_opportunities: Vec<String>,
}

/// Performance bottleneck information
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
    pub bottleneck_type: String,
    pub severity: f64,
    pub description: String,
    pub impact: f64,
}

/// Cost-benefit analysis report
#[derive(Debug)]
pub struct CostBenefitReport {
    pub total_potential_savings: f64,
    pub implementation_costs: f64,
    pub roi_estimates: Vec<ROIEstimate>,
    pub risk_assessments: Vec<RiskAssessment>,
}

/// ROI estimate
#[derive(Debug, Clone)]
pub struct ROIEstimate {
    pub optimization_type: OptimizationType,
    pub estimated_roi: f64,
    pub time_to_break_even: Duration,
    pub confidence_level: f64,
}

/// Memory alert
#[derive(Debug, Clone)]
pub struct MemoryAlert {
    pub alert_type: AlertType,
    pub severity: AlertSeverity,
    pub message: String,
    pub timestamp: Instant,
    pub suggested_actions: Vec<String>,
}

/// Alert types
#[derive(Debug, Clone)]
pub enum AlertType {
    HighMemoryUsage,
    CriticalMemoryUsage,
    MemoryLeak,
    HighFragmentation,
    PerformanceDegradation,
}

/// Alert severity levels
#[derive(Debug, Clone)]
pub enum AlertSeverity {
    Info,
    Warning,
    Critical,
}

// Default implementations and helper methods

impl AdvancedMemoryTracker {
    fn new() -> Self {
        Self {
            current_usage: MemoryUsage::default(),
            peak_usage: MemoryUsage::default(),
            usage_history: VecDeque::new(),
            allocation_tracker: AllocationTracker::new(),
            pool_analyzer: MemoryPoolAnalyzer::new(),
            gc_metrics: GarbageCollectionMetrics::default(),
        }
    }

    fn initialize(&mut self) -> Result<()> {
        // Initialize tracking systems
        Ok(())
    }

    fn add_snapshot(&mut self, snapshot: MemorySnapshot) {
        self.usage_history.push_back(snapshot);
        // Maintain history size limit
        if self.usage_history.len() > 10000 {
            self.usage_history.pop_front();
        }
    }

    fn get_snapshots(&self) -> &VecDeque<MemorySnapshot> {
        &self.usage_history
    }

    fn get_current_usage(&self) -> Option<&MemoryUsage> {
        Some(&self.current_usage)
    }

    fn get_recent_allocations(&self) -> Vec<AllocationEvent> {
        // Return recent allocation events
        Vec::new()
    }

    fn get_recent_deallocations(&self) -> Vec<DeallocationEvent> {
        // Return recent deallocation events
        Vec::new()
    }

    fn get_usage_summary(&self) -> MemoryUsageSummary {
        MemoryUsageSummary {
            current_usage: self.current_usage.clone(),
            peak_usage: self.peak_usage.clone(),
            average_usage: self.calculate_average_usage(),
            usage_trend: self.calculate_usage_trend(),
        }
    }

    fn calculate_average_usage(&self) -> MemoryUsage {
        // Calculate average from history
        self.current_usage.clone() // Simplified
    }

    fn calculate_usage_trend(&self) -> UsageTrend {
        UsageTrend::Stable // Simplified
    }
}

impl AllocationTracker {
    fn new() -> Self {
        Self {
            total_allocations: 0,
            total_deallocations: 0,
            size_distribution: HashMap::new(),
            allocation_rate: VecDeque::new(),
            active_allocations: HashMap::new(),
            patterns: Vec::new(),
        }
    }
}

impl MemoryPoolAnalyzer {
    fn new() -> Self {
        Self {
            pool_opportunities: Vec::new(),
            pool_efficiency: 0.0,
            utilization_metrics: PoolUtilizationMetrics::default(),
        }
    }
}

impl MemoryLeakDetector {
    fn new() -> Self {
        Self {
            detectors: Vec::new(),
            detected_leaks: Vec::new(),
            false_positive_filter: FalsePositiveFilter::new(),
            correlation_analyzer: LeakCorrelationAnalyzer::new(),
        }
    }

    fn initialize(&mut self) -> Result<()> {
        // Initialize leak detection algorithms
        Ok(())
    }

    fn check_for_leaks(&mut self, snapshots: &VecDeque<MemorySnapshot>) -> Result<()> {
        // Run leak detection algorithms
        Ok(())
    }

    fn get_detected_leaks(&self) -> &[MemoryLeak] {
        &self.detected_leaks
    }
}

impl FalsePositiveFilter {
    fn new() -> Self {
        Self {
            known_patterns: Vec::new(),
            pattern_learner: PatternLearner::new(),
        }
    }
}

impl PatternLearner {
    fn new() -> Self {
        Self {
            training_data: Vec::new(),
            model_parameters: Vec::new(),
        }
    }
}

impl LeakCorrelationAnalyzer {
    fn new() -> Self {
        Self {
            correlation_matrix: Vec::new(),
            causal_relationships: Vec::new(),
        }
    }
}

impl OptimizationEngine {
    fn new() -> Self {
        Self {
            strategies: Vec::new(),
            recommendations: Vec::new(),
            cost_benefit_analyzer: CostBenefitAnalyzer::new(),
        }
    }

    fn generate_recommendations(
        &mut self,
        tracker: &AdvancedMemoryTracker,
    ) -> Vec<MemoryOptimizationRecommendation> {
        // Generate optimization recommendations
        vec![MemoryOptimizationRecommendation {
            recommendation_type: OptimizationType::AddMemoryPooling,
            priority: Priority::High,
            title: "Implement Memory Pooling".to_string(),
            description: "Add memory pools for frequently allocated objects".to_string(),
            implementation_steps: vec![
                "Identify frequently allocated sizes".to_string(),
                "Create size-specific pools".to_string(),
                "Integrate pool allocation".to_string(),
            ],
            estimated_effort: EstimatedEffort {
                development_hours: 16.0,
                testing_hours: 8.0,
                deployment_complexity: Complexity::Medium,
                expertise_level: ExpertiseLevel::Intermediate,
            },
            expected_benefits: ExpectedBenefits {
                memory_reduction_percent: 20.0,
                performance_improvement_percent: 15.0,
                allocation_reduction: 1000,
                fragmentation_improvement: 0.3,
                cost_savings: 500.0,
            },
            risk_assessment: RiskAssessment {
                risk_level: RiskLevel::Low,
                potential_issues: vec!["Pool sizing challenges".to_string()],
                mitigation_strategies: vec!["Start with conservative pool sizes".to_string()],
                rollback_plan: "Disable pooling if issues arise".to_string(),
            },
            code_examples: vec![CodeExample {
                title: "Memory Pool Implementation".to_string(),
                before_code: "let data = vec![0u8; size];".to_string(),
                after_code: "let data = pool.allocate(size);".to_string(),
                explanation: "Use memory pool instead of direct allocation".to_string(),
            }],
        }]
    }

    fn analyze_cost_benefit(
        &self,
        recommendations: &[MemoryOptimizationRecommendation],
    ) -> CostBenefitReport {
        CostBenefitReport {
            total_potential_savings: 2000.0,
            implementation_costs: 800.0,
            roi_estimates: vec![ROIEstimate {
                optimization_type: OptimizationType::AddMemoryPooling,
                estimated_roi: 2.5, // 250% ROI
                time_to_break_even: Duration::from_secs(3600 * 24 * 30), // 30 days
                confidence_level: 0.8,
            }],
            risk_assessments: vec![],
        }
    }
}

impl CostBenefitAnalyzer {
    fn new() -> Self {
        Self {
            cost_models: Vec::new(),
            benefit_models: Vec::new(),
            roi_calculator: ROICalculator {
                time_horizon: Duration::from_secs(3600 * 24 * 365), // 1 year
                discount_rate: 0.1,                                 // 10%
            },
        }
    }
}

impl MemoryPatternAnalyzer {
    fn new() -> Self {
        Self {
            detected_patterns: Vec::new(),
            pattern_history: VecDeque::new(),
            prediction_model: PatternPredictionModel {
                model_type: ModelType::LinearRegression,
                parameters: Vec::new(),
                accuracy: 0.0,
            },
        }
    }

    fn initialize(&mut self) -> Result<()> {
        // Initialize pattern analysis
        Ok(())
    }

    fn analyze_snapshot(&mut self, snapshot: &MemorySnapshot) -> Result<()> {
        // Analyze memory patterns in _snapshot
        Ok(())
    }

    fn get_detected_patterns(&self) -> &[MemoryPattern] {
        &self.detected_patterns
    }
}

// Default implementations

impl Default for GarbageCollectionMetrics {
    fn default() -> Self {
        Self {
            total_gc_events: 0,
            total_gc_time: Duration::from_secs(0),
            memory_reclaimed: 0,
            gc_pressure: 0.0,
        }
    }
}

impl Default for PoolUtilizationMetrics {
    fn default() -> Self {
        Self {
            average_utilization: 0.0,
            peak_utilization: 0.0,
            hit_rate: 0.0,
            miss_rate: 0.0,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_memory_optimizer_creation() {
        let config = MemoryOptimizerConfig::default();
        let optimizer = MemoryOptimizer::new(config);
        assert!(optimizer.config.enable_detailed_tracking);
    }

    #[test]
    fn test_memory_usage_default() {
        let usage = MemoryUsage::default();
        assert_eq!(usage.total_allocated, 0);
        assert_eq!(usage.used_memory, 0);
    }

    #[test]
    fn test_alert_generation() {
        let config = MemoryOptimizerConfig::default();
        let optimizer = MemoryOptimizer::new(config);
        let alerts = optimizer.get_alerts();
        // Should not generate alerts with default/empty state
        assert!(alerts.is_empty());
    }

    #[test]
    fn test_fragmentation_calculation() {
        let config = MemoryOptimizerConfig::default();
        let optimizer = MemoryOptimizer::new(config);
        let usage = MemoryUsage::default();
        let fragmentation = optimizer.calculate_fragmentation(&usage);
        assert!(fragmentation.is_ok());
    }
}