sklears_compose/

performance_optimization.rs

//! # Performance Optimization Module
//!
//! This module provides comprehensive performance optimization capabilities for the
//! composable execution framework. It includes algorithms and strategies to optimize
//! execution performance, resource utilization, throughput, latency, and overall
//! system efficiency through intelligent optimization techniques.
//!
//! # Performance Optimization Architecture
//!
//! The performance optimization system is built around multiple specialized optimizers:
//!
//! ```text
//! PerformanceOptimizer (main coordinator)
//! ├── ThroughputOptimizer      // Maximize task completion rate
//! ├── LatencyOptimizer         // Minimize task execution latency
//! ├── ResourceOptimizer        // Optimize resource utilization
//! ├── EnergyOptimizer          // Minimize energy consumption
//! ├── CacheOptimizer           // Optimize cache hit rates
//! ├── MemoryOptimizer          // Optimize memory usage patterns
//! ├── NetworkOptimizer         // Optimize network performance
//! ├── LoadBalanceOptimizer     // Balance load distribution
//! ├── PipelineOptimizer        // Optimize execution pipelines
//! └── PredictiveOptimizer      // ML-based performance prediction
//! ```
//!
//! # Optimization Strategies
//!
//! ## Throughput Optimization
//! - **Parallel Processing**: Maximize concurrent task execution
//! - **Batch Processing**: Group similar tasks for efficiency
//! - **Pipeline Optimization**: Minimize pipeline stalls
//! - **Resource Scaling**: Dynamic resource allocation
//!
//! ## Latency Optimization
//! - **Task Prioritization**: High-priority task fast paths
//! - **Preemptive Scheduling**: Interrupt low-priority tasks
//! - **Cache Warming**: Proactive cache population
//! - **Resource Pre-allocation**: Avoid allocation delays
//!
//! ## Resource Optimization
//! - **Utilization Maximization**: Keep resources busy
//! - **Fragmentation Reduction**: Minimize resource waste
//! - **Locality Optimization**: CPU/memory/network locality
//! - **Dynamic Scaling**: Scale resources with demand
//!
//! ## Energy Optimization
//! - **Frequency Scaling**: Dynamic CPU frequency adjustment
//! - **Idle State Management**: Efficient power state transitions
//! - **Thermal Management**: Temperature-aware optimization
//! - **Workload Consolidation**: Reduce active hardware
//!
//! # Usage Examples
//!
//! ## Basic Performance Optimization
//! ```rust,ignore
//! use sklears_compose::performance_optimization::*;
//!
//! // Create performance optimizer with default configuration
//! let mut optimizer = PerformanceOptimizer::new()?;
//! optimizer.initialize()?;
//!
//! // Configure optimization goals
//! let goals = OptimizationGoals {
//!     primary_objective: OptimizationObjective::Throughput,
//!     secondary_objectives: vec![
//!         OptimizationObjective::ResourceUtilization,
//!         OptimizationObjective::EnergyEfficiency,
//!     ],
//!     constraints: OptimizationConstraints {
//!         max_latency: Some(Duration::from_millis(100)),
//!         min_throughput: Some(1000.0), // tasks/sec
//!         max_energy_consumption: Some(500.0), // watts
//!         ..Default::default()
//!     },
//! };
//!
//! optimizer.set_goals(goals)?;
//! ```
//!
//! ## Advanced Throughput Optimization
//! ```rust,ignore
//! // Configure throughput optimizer
//! let throughput_config = ThroughputOptimizerConfig {
//!     target_throughput: 2000.0, // tasks/sec
//!     optimization_strategy: ThroughputStrategy::MaxParallel,
//!     batch_size_optimization: true,
//!     pipeline_optimization: true,
//!     resource_scaling: true,
//!     load_balancing: true,
//! };
//!
//! let throughput_optimizer = ThroughputOptimizer::new(throughput_config)?;
//! optimizer.add_optimizer(Box::new(throughput_optimizer))?;
//!
//! // Start optimization loop
//! optimizer.start_optimization().await?;
//! ```
//!
//! ## Latency-Critical Optimization
//! ```rust,ignore
//! // Configure for ultra-low latency
//! let latency_config = LatencyOptimizerConfig {
//!     target_latency: Duration::from_micros(500), // 500μs target
//!     optimization_strategy: LatencyStrategy::PreemptiveScheduling,
//!     enable_cache_warming: true,
//!     enable_preallocation: true,
//!     enable_fast_paths: true,
//!     jitter_reduction: true,
//! };
//!
//! let latency_optimizer = LatencyOptimizer::new(latency_config)?;
//! optimizer.add_optimizer(Box::new(latency_optimizer))?;
//! ```
//!
//! ## Energy-Efficient Optimization
//! ```rust,ignore
//! // Configure for energy efficiency
//! let energy_config = EnergyOptimizerConfig {
//!     target_efficiency: 0.9, // 90% efficiency
//!     enable_frequency_scaling: true,
//!     enable_idle_states: true,
//!     enable_thermal_management: true,
//!     workload_consolidation: true,
//!     green_computing_mode: true,
//! };
//!
//! let energy_optimizer = EnergyOptimizer::new(energy_config)?;
//! optimizer.add_optimizer(Box::new(energy_optimizer))?;
//! ```
//!
//! ## Machine Learning-Based Optimization
//! ```rust,ignore
//! // Configure predictive optimizer
//! let ml_config = PredictiveOptimizerConfig {
//!     model_type: MLModelType::NeuralNetwork,
//!     training_data_size: 10000,
//!     prediction_horizon: Duration::from_secs(60),
//!     learning_rate: 0.001,
//!     enable_online_learning: true,
//!     feature_engineering: true,
//! };
//!
//! let predictive_optimizer = PredictiveOptimizer::new(ml_config)?;
//! optimizer.add_optimizer(Box::new(predictive_optimizer))?;
//! ```

use crate::execution_core::ExecutionStrategy;
use sklears_core::error::Result as SklResult;
use std::collections::{HashMap, VecDeque};
use std::fmt;
use std::future::Future;
use std::pin::Pin;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, SystemTime};

/// Main performance optimizer coordinating all optimization strategies
#[derive(Debug)]
pub struct PerformanceOptimizer {
    /// Optimization configuration
    config: OptimizerConfig,
    /// Optimization goals
    goals: OptimizationGoals,
    /// Specialized optimizers
    optimizers: Vec<Box<dyn SpecializedOptimizer>>,
    /// Performance metrics collector
    metrics: Arc<Mutex<PerformanceMetrics>>,
    /// Optimization history
    history: Arc<Mutex<VecDeque<OptimizationResult>>>,
    /// Current optimization state
    state: Arc<RwLock<OptimizerState>>,
    /// Performance baselines
    baselines: Arc<RwLock<PerformanceBaselines>>,
}

/// Optimizer configuration
#[derive(Debug, Clone)]
pub struct OptimizerConfig {
    /// Optimization interval
    pub optimization_interval: Duration,
    /// Enable continuous optimization
    pub continuous_optimization: bool,
    /// Performance measurement window
    pub measurement_window: Duration,
    /// Optimization aggressiveness (0.0 to 1.0)
    pub aggressiveness: f64,
    /// Stability threshold for changes
    pub stability_threshold: f64,
    /// Enable experimental optimizations
    pub experimental_optimizations: bool,
    /// Maximum optimization iterations
    pub max_iterations: usize,
    /// Convergence tolerance
    pub convergence_tolerance: f64,
}

/// Optimization goals and objectives
#[derive(Debug, Clone)]
pub struct OptimizationGoals {
    /// Primary optimization objective
    pub primary_objective: OptimizationObjective,
    /// Secondary objectives (in priority order)
    pub secondary_objectives: Vec<OptimizationObjective>,
    /// Optimization constraints
    pub constraints: OptimizationConstraints,
    /// Target performance metrics
    pub targets: PerformanceTargets,
    /// Optimization weights for multi-objective optimization
    pub weights: ObjectiveWeights,
}

/// Optimization objectives
#[derive(Debug, Clone, PartialEq)]
pub enum OptimizationObjective {
    /// Maximize task throughput
    Throughput,
    /// Minimize task latency
    Latency,
    /// Maximize resource utilization
    ResourceUtilization,
    /// Minimize energy consumption
    EnergyEfficiency,
    /// Minimize cost
    Cost,
    /// Maximize reliability
    Reliability,
    /// Minimize jitter/variance
    Stability,
    /// Custom objective
    Custom(String),
}

/// Optimization constraints
#[derive(Debug, Clone)]
pub struct OptimizationConstraints {
    /// Maximum acceptable latency
    pub max_latency: Option<Duration>,
    /// Minimum required throughput
    pub min_throughput: Option<f64>,
    /// Maximum energy consumption
    pub max_energy_consumption: Option<f64>,
    /// Maximum cost budget
    pub max_cost: Option<f64>,
    /// Minimum reliability requirement
    pub min_reliability: Option<f64>,
    /// Resource usage limits
    pub resource_limits: ResourceLimits,
    /// SLA requirements
    pub sla_requirements: Vec<SlaRequirement>,
}

/// Resource usage limits
#[derive(Debug, Clone)]
pub struct ResourceLimits {
    /// Maximum CPU utilization percentage
    pub max_cpu_utilization: Option<f64>,
    /// Maximum memory utilization percentage
    pub max_memory_utilization: Option<f64>,
    /// Maximum GPU utilization percentage
    pub max_gpu_utilization: Option<f64>,
    /// Maximum network utilization percentage
    pub max_network_utilization: Option<f64>,
    /// Maximum storage utilization percentage
    pub max_storage_utilization: Option<f64>,
}

/// SLA requirement
#[derive(Debug, Clone)]
pub struct SlaRequirement {
    /// SLA name
    pub name: String,
    /// Metric type
    pub metric_type: SlaMetricType,
    /// Target value
    pub target_value: f64,
    /// Tolerance
    pub tolerance: f64,
    /// Penalty for violation
    pub penalty: f64,
}

/// SLA metric types
#[derive(Debug, Clone, PartialEq)]
pub enum SlaMetricType {
    /// Latency
    Latency,
    /// Throughput
    Throughput,
    /// Availability
    Availability,
    /// ErrorRate
    ErrorRate,
    /// ResponseTime
    ResponseTime,
    /// Custom
    Custom(String),
}

/// Performance targets
#[derive(Debug, Clone)]
pub struct PerformanceTargets {
    /// Target throughput (tasks/second)
    pub throughput: Option<f64>,
    /// Target latency
    pub latency: Option<Duration>,
    /// Target resource utilization
    pub resource_utilization: Option<f64>,
    /// Target energy efficiency
    pub energy_efficiency: Option<f64>,
    /// Target cost efficiency
    pub cost_efficiency: Option<f64>,
    /// Target reliability (uptime percentage)
    pub reliability: Option<f64>,
}

/// Objective weights for multi-objective optimization
#[derive(Debug, Clone)]
pub struct ObjectiveWeights {
    /// Throughput weight
    pub throughput: f64,
    /// Latency weight
    pub latency: f64,
    /// Resource utilization weight
    pub resource_utilization: f64,
    /// Energy efficiency weight
    pub energy_efficiency: f64,
    /// Cost weight
    pub cost: f64,
    /// Reliability weight
    pub reliability: f64,
    /// Stability weight
    pub stability: f64,
}

/// Specialized optimizer trait
pub trait SpecializedOptimizer: Send + Sync + fmt::Debug {
    /// Get optimizer name
    fn name(&self) -> &str;

    /// Get optimization domain
    fn domain(&self) -> OptimizationDomain;

    /// Initialize the optimizer
    fn initialize(&mut self) -> SklResult<()>;

    /// Analyze current performance
    fn analyze_performance(&self, metrics: &PerformanceMetrics) -> SklResult<PerformanceAnalysis>;

    /// Generate optimization recommendations
    fn generate_recommendations(
        &self,
        analysis: &PerformanceAnalysis,
    ) -> SklResult<Vec<OptimizationRecommendation>>;

    /// Apply optimizations
    fn apply_optimizations(
        &mut self,
        recommendations: &[OptimizationRecommendation],
    ) -> Pin<Box<dyn Future<Output = SklResult<OptimizationResult>> + Send + '_>>;

    /// Get optimizer metrics
    fn get_metrics(&self) -> SklResult<OptimizerMetrics>;

    /// Update optimizer configuration
    fn update_config(&mut self, config: HashMap<String, String>) -> SklResult<()>;
}

/// Optimization domains
#[derive(Debug, Clone, PartialEq)]
pub enum OptimizationDomain {
    /// Throughput
    Throughput,
    /// Latency
    Latency,
    /// Resource
    Resource,
    /// Energy
    Energy,
    /// Cache
    Cache,
    /// Memory
    Memory,
    /// Network
    Network,
    /// LoadBalance
    LoadBalance,
    /// Pipeline
    Pipeline,
    /// Predictive
    Predictive,
}

/// Performance analysis result
#[derive(Debug, Clone)]
pub struct PerformanceAnalysis {
    /// Analysis timestamp
    pub timestamp: SystemTime,
    /// Analysis domain
    pub domain: OptimizationDomain,
    /// Current performance score (0.0 to 1.0)
    pub performance_score: f64,
    /// Bottlenecks identified
    pub bottlenecks: Vec<PerformanceBottleneck>,
    /// Optimization opportunities
    pub opportunities: Vec<OptimizationOpportunity>,
    /// Performance trends
    pub trends: PerformanceTrends,
    /// Analysis confidence
    pub confidence: f64,
}

/// Performance bottleneck
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
    /// Bottleneck type
    pub bottleneck_type: BottleneckType,
    /// Severity (0.0 to 1.0)
    pub severity: f64,
    /// Impact on performance
    pub impact: f64,
    /// Root cause
    pub root_cause: String,
    /// Resolution difficulty
    pub resolution_difficulty: f64,
}

/// Bottleneck types
#[derive(Debug, Clone, PartialEq)]
pub enum BottleneckType {
    CpuBound,
    MemoryBound,
    IoBound,
    NetworkBound,
    CacheMiss,
    ContentionLock,
    ResourceStarvation,
    Scheduling,
    Custom(String),
}

/// Optimization opportunity
#[derive(Debug, Clone)]
pub struct OptimizationOpportunity {
    /// Opportunity type
    pub opportunity_type: OpportunityType,
    /// Potential improvement
    pub potential_improvement: f64,
    /// Implementation effort
    pub implementation_effort: f64,
    /// Risk level
    pub risk_level: f64,
    /// Dependencies
    pub dependencies: Vec<String>,
}

/// Opportunity types
#[derive(Debug, Clone, PartialEq)]
pub enum OpportunityType {
    /// ParallelizationIncrease
    ParallelizationIncrease,
    /// CacheOptimization
    CacheOptimization,
    /// MemoryLayoutOptimization
    MemoryLayoutOptimization,
    /// AlgorithmOptimization
    AlgorithmOptimization,
    /// ResourceReallocation
    ResourceReallocation,
    /// LoadRebalancing
    LoadRebalancing,
    /// PipelineOptimization
    PipelineOptimization,
    /// BatchSizeOptimization
    BatchSizeOptimization,
    /// Custom
    Custom(String),
}

/// Performance trends analysis
#[derive(Debug, Clone, Default)]
pub struct PerformanceTrends {
    /// Throughput trend
    pub throughput_trend: TrendData,
    /// Latency trend
    pub latency_trend: TrendData,
    /// Resource utilization trend
    pub resource_trend: TrendData,
    /// Energy consumption trend
    pub energy_trend: TrendData,
    /// Error rate trend
    pub error_trend: TrendData,
}

/// Trend data
#[derive(Debug, Clone)]
pub struct TrendData {
    /// Trend direction
    pub direction: TrendDirection,
    /// Trend magnitude
    pub magnitude: f64,
    /// Trend confidence
    pub confidence: f64,
    /// Prediction for next period
    pub prediction: f64,
    /// Historical variance
    pub variance: f64,
}

/// Trend directions
#[derive(Debug, Clone, PartialEq)]
pub enum TrendDirection {
    /// Improving
    Improving,
    /// Degrading
    Degrading,
    /// Stable
    Stable,
    /// Oscillating
    Oscillating,
    /// Unknown
    Unknown,
}

/// Optimization recommendation
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    /// Recommendation ID
    pub id: String,
    /// Recommendation type
    pub recommendation_type: RecommendationType,
    /// Target component
    pub target: String,
    /// Recommended action
    pub action: OptimizationAction,
    /// Expected impact
    pub expected_impact: ExpectedImpact,
    /// Implementation priority
    pub priority: RecommendationPriority,
    /// Implementation risk
    pub risk: RiskAssessment,
}

/// Recommendation types
#[derive(Debug, Clone, PartialEq)]
pub enum RecommendationType {
    /// ConfigurationChange
    ConfigurationChange,
    /// ResourceReallocation
    ResourceReallocation,
    /// AlgorithmChange
    AlgorithmChange,
    /// ArchitectureChange
    ArchitectureChange,
    /// ParameterTuning
    ParameterTuning,
    /// CacheConfiguration
    CacheConfiguration,
    /// SchedulingPolicy
    SchedulingPolicy,
    /// LoadBalancing
    LoadBalancing,
}

/// Optimization actions
#[derive(Debug, Clone)]
pub enum OptimizationAction {
    /// Change configuration parameter
    ChangeParameter { name: String, value: String },
    /// Scale resource allocation
    ScaleResource { resource: String, factor: f64 },
    /// Change algorithm
    ChangeAlgorithm { from: String, to: String },
    /// Adjust batch size
    AdjustBatchSize { new_size: usize },
    /// Enable/disable feature
    ToggleFeature { feature: String, enabled: bool },
    /// Custom action
    Custom {
        action: String,
        parameters: HashMap<String, String>,
    },
}

/// Expected impact of optimization
#[derive(Debug, Clone)]
pub struct ExpectedImpact {
    /// Throughput improvement (percentage)
    pub throughput_improvement: f64,
    /// Latency reduction (percentage)
    pub latency_reduction: f64,
    /// Resource savings (percentage)
    pub resource_savings: f64,
    /// Energy savings (percentage)
    pub energy_savings: f64,
    /// Cost savings (percentage)
    pub cost_savings: f64,
    /// Implementation time
    pub implementation_time: Duration,
}

/// Recommendation priority
#[derive(Debug, Clone, PartialEq, PartialOrd)]
pub enum RecommendationPriority {
    /// Low
    Low,
    /// Medium
    Medium,
    /// High
    High,
    /// Critical
    Critical,
}

/// Risk assessment
#[derive(Debug, Clone)]
pub struct RiskAssessment {
    /// Overall risk level (0.0 to 1.0)
    pub risk_level: f64,
    /// Potential negative impacts
    pub negative_impacts: Vec<String>,
    /// Rollback difficulty
    pub rollback_difficulty: f64,
    /// Testing requirements
    pub testing_requirements: Vec<String>,
}

/// Optimization result
#[derive(Debug, Clone)]
pub struct OptimizationResult {
    /// Result ID
    pub id: String,
    /// Optimization timestamp
    pub timestamp: SystemTime,
    /// Optimization domain
    pub domain: OptimizationDomain,
    /// Applied recommendations
    pub applied_recommendations: Vec<String>,
    /// Performance before optimization
    pub before_metrics: PerformanceSnapshot,
    /// Performance after optimization
    pub after_metrics: PerformanceSnapshot,
    /// Actual impact
    pub actual_impact: ActualImpact,
    /// Success status
    pub success: bool,
    /// Error message if failed
    pub error_message: Option<String>,
}

/// Performance snapshot
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    /// Timestamp
    pub timestamp: SystemTime,
    /// Throughput (tasks/second)
    pub throughput: f64,
    /// Average latency
    pub latency: Duration,
    /// Resource utilization
    pub resource_utilization: f64,
    /// Energy consumption
    pub energy_consumption: f64,
    /// Error rate
    pub error_rate: f64,
    /// Cost rate
    pub cost_rate: f64,
}

/// Actual impact of optimization
#[derive(Debug, Clone)]
pub struct ActualImpact {
    /// Actual throughput improvement
    pub throughput_improvement: f64,
    /// Actual latency reduction
    pub latency_reduction: f64,
    /// Actual resource savings
    pub resource_savings: f64,
    /// Actual energy savings
    pub energy_savings: f64,
    /// Actual cost savings
    pub cost_savings: f64,
    /// Side effects observed
    pub side_effects: Vec<String>,
}

/// Performance metrics collector
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    /// Current throughput
    pub current_throughput: f64,
    /// Current latency
    pub current_latency: Duration,
    /// Resource utilization breakdown
    pub resource_utilization: ResourceUtilizationMetrics,
    /// Energy consumption metrics
    pub energy_metrics: EnergyMetrics,
    /// Cache performance metrics
    pub cache_metrics: CacheMetrics,
    /// Network performance metrics
    pub network_metrics: NetworkPerformanceMetrics,
    /// Pipeline metrics
    pub pipeline_metrics: PipelineMetrics,
    /// Quality metrics
    pub quality_metrics: QualityMetrics,
    /// Measurement timestamp
    pub timestamp: SystemTime,
}

/// Resource utilization metrics
#[derive(Debug, Clone)]
pub struct ResourceUtilizationMetrics {
    /// CPU utilization percentage
    pub cpu_utilization: f64,
    /// Memory utilization percentage
    pub memory_utilization: f64,
    /// GPU utilization percentage
    pub gpu_utilization: Option<f64>,
    /// Storage I/O utilization
    pub storage_utilization: f64,
    /// Network utilization
    pub network_utilization: f64,
    /// Utilization efficiency score
    pub efficiency_score: f64,
}

/// Energy consumption metrics
#[derive(Debug, Clone)]
pub struct EnergyMetrics {
    /// Total power consumption (watts)
    pub total_power: f64,
    /// CPU power consumption
    pub cpu_power: f64,
    /// GPU power consumption
    pub gpu_power: Option<f64>,
    /// Memory power consumption
    pub memory_power: f64,
    /// Storage power consumption
    pub storage_power: f64,
    /// Cooling power consumption
    pub cooling_power: f64,
    /// Energy efficiency (tasks/joule)
    pub energy_efficiency: f64,
}

/// Cache performance metrics
#[derive(Debug, Clone)]
pub struct CacheMetrics {
    /// L1 cache hit rate
    pub l1_hit_rate: f64,
    /// L2 cache hit rate
    pub l2_hit_rate: f64,
    /// L3 cache hit rate
    pub l3_hit_rate: f64,
    /// Memory cache hit rate
    pub memory_cache_hit_rate: f64,
    /// Storage cache hit rate
    pub storage_cache_hit_rate: f64,
    /// Average cache access time
    pub average_access_time: Duration,
}

/// Network performance metrics
#[derive(Debug, Clone)]
pub struct NetworkPerformanceMetrics {
    /// Bandwidth utilization
    pub bandwidth_utilization: f64,
    /// Average latency
    pub average_latency: Duration,
    /// Packet loss rate
    pub packet_loss_rate: f64,
    /// Jitter
    pub jitter: Duration,
    /// Connection efficiency
    pub connection_efficiency: f64,
}

/// Pipeline performance metrics
#[derive(Debug, Clone)]
pub struct PipelineMetrics {
    /// Pipeline throughput
    pub pipeline_throughput: f64,
    /// Stage utilization
    pub stage_utilization: Vec<f64>,
    /// Pipeline efficiency
    pub pipeline_efficiency: f64,
    /// Bottleneck stages
    pub bottleneck_stages: Vec<usize>,
    /// Average pipeline latency
    pub average_pipeline_latency: Duration,
}

/// Quality metrics
#[derive(Debug, Clone)]
pub struct QualityMetrics {
    /// Error rate
    pub error_rate: f64,
    /// Success rate
    pub success_rate: f64,
    /// Retry rate
    pub retry_rate: f64,
    /// Data quality score
    pub data_quality_score: f64,
    /// Service availability
    pub availability: f64,
}

/// Optimizer state
#[derive(Debug, Clone)]
pub struct OptimizerState {
    /// Is optimizer active?
    pub active: bool,
    /// Current optimization phase
    pub phase: OptimizationPhase,
    /// Iterations completed
    pub iterations_completed: usize,
    /// Last optimization time
    pub last_optimization: SystemTime,
    /// Optimization score
    pub optimization_score: f64,
    /// Convergence status
    pub converged: bool,
}

/// Optimization phases
#[derive(Debug, Clone, PartialEq)]
pub enum OptimizationPhase {
    /// Initialization
    Initialization,
    /// Analysis
    Analysis,
    /// RecommendationGeneration
    RecommendationGeneration,
    /// Implementation
    Implementation,
    /// Validation
    Validation,
    /// Monitoring
    Monitoring,
    /// Idle
    Idle,
}

/// Optimizer metrics
#[derive(Debug, Clone)]
pub struct OptimizerMetrics {
    /// Total optimizations performed
    pub total_optimizations: u64,
    /// Successful optimizations
    pub successful_optimizations: u64,
    /// Failed optimizations
    pub failed_optimizations: u64,
    /// Average improvement achieved
    pub average_improvement: f64,
    /// Optimization frequency
    pub optimization_frequency: f64,
    /// Time spent optimizing
    pub time_spent: Duration,
}

/// Performance baselines for comparison
#[derive(Debug, Clone)]
pub struct PerformanceBaselines {
    /// Baseline performance metrics
    pub baseline_metrics: PerformanceSnapshot,
    /// Best achieved performance
    pub best_performance: PerformanceSnapshot,
    /// Worst observed performance
    pub worst_performance: PerformanceSnapshot,
    /// Baseline establishment time
    pub baseline_time: SystemTime,
}

/// Throughput optimizer implementation
#[derive(Debug)]
pub struct ThroughputOptimizer {
    /// Configuration
    config: ThroughputOptimizerConfig,
    /// Current state
    state: ThroughputOptimizerState,
    /// Metrics
    metrics: OptimizerMetrics,
}

/// Throughput optimizer configuration
#[derive(Debug, Clone)]
pub struct ThroughputOptimizerConfig {
    /// Target throughput (tasks/second)
    pub target_throughput: f64,
    /// Optimization strategy
    pub strategy: ThroughputStrategy,
    /// Enable batch size optimization
    pub batch_size_optimization: bool,
    /// Enable pipeline optimization
    pub pipeline_optimization: bool,
    /// Enable resource scaling
    pub resource_scaling: bool,
    /// Enable load balancing
    pub load_balancing: bool,
    /// Maximum parallelism level
    pub max_parallelism: usize,
}

/// Throughput optimization strategies
#[derive(Debug, Clone, PartialEq)]
pub enum ThroughputStrategy {
    /// MaxParallel
    MaxParallel,
    /// OptimalBatching
    OptimalBatching,
    /// PipelineOptimization
    PipelineOptimization,
    /// AdaptiveScaling
    AdaptiveScaling,
    /// HybridApproach
    HybridApproach,
}

/// Throughput optimizer state
#[derive(Debug, Clone)]
pub struct ThroughputOptimizerState {
    /// Current parallelism level
    pub current_parallelism: usize,
    /// Current batch size
    pub current_batch_size: usize,
    /// Current throughput
    pub current_throughput: f64,
    /// Target gap
    pub target_gap: f64,
    /// Optimization attempts
    pub optimization_attempts: usize,
}

/// Latency optimizer implementation
#[derive(Debug)]
pub struct LatencyOptimizer {
    /// Configuration
    config: LatencyOptimizerConfig,
    /// Current state
    state: LatencyOptimizerState,
    /// Metrics
    metrics: OptimizerMetrics,
}

/// Latency optimizer configuration
#[derive(Debug, Clone)]
pub struct LatencyOptimizerConfig {
    /// Target latency
    pub target_latency: Duration,
    /// Optimization strategy
    pub strategy: LatencyStrategy,
    /// Enable cache warming
    pub enable_cache_warming: bool,
    /// Enable preallocation
    pub enable_preallocation: bool,
    /// Enable fast paths
    pub enable_fast_paths: bool,
    /// Enable jitter reduction
    pub jitter_reduction: bool,
    /// Latency tolerance
    pub tolerance: Duration,
}

/// Latency optimization strategies
#[derive(Debug, Clone, PartialEq)]
pub enum LatencyStrategy {
    /// PreemptiveScheduling
    PreemptiveScheduling,
    /// CacheOptimization
    CacheOptimization,
    /// PreallocationStrategy
    PreallocationStrategy,
    /// FastPathOptimization
    FastPathOptimization,
    /// JitterReduction
    JitterReduction,
}

/// Latency optimizer state
#[derive(Debug, Clone)]
pub struct LatencyOptimizerState {
    /// Current average latency
    pub current_latency: Duration,
    /// Current P95 latency
    pub p95_latency: Duration,
    /// Current P99 latency
    pub p99_latency: Duration,
    /// Target gap
    pub target_gap: Duration,
    /// Jitter level
    pub jitter: Duration,
}

/// Energy optimizer implementation
#[derive(Debug)]
pub struct EnergyOptimizer {
    /// Configuration
    config: EnergyOptimizerConfig,
    /// Current state
    state: EnergyOptimizerState,
    /// Metrics
    metrics: OptimizerMetrics,
}

/// Energy optimizer configuration
#[derive(Debug, Clone)]
pub struct EnergyOptimizerConfig {
    /// Target energy efficiency
    pub target_efficiency: f64,
    /// Enable frequency scaling
    pub enable_frequency_scaling: bool,
    /// Enable idle states
    pub enable_idle_states: bool,
    /// Enable thermal management
    pub enable_thermal_management: bool,
    /// Enable workload consolidation
    pub workload_consolidation: bool,
    /// Green computing mode
    pub green_computing_mode: bool,
    /// Maximum power consumption
    pub max_power_consumption: Option<f64>,
}

/// Energy optimizer state
#[derive(Debug, Clone)]
pub struct EnergyOptimizerState {
    /// Current power consumption
    pub current_power: f64,
    /// Current efficiency
    pub current_efficiency: f64,
    /// Thermal state
    pub thermal_state: ThermalState,
    /// Power management mode
    pub power_mode: PowerMode,
}

/// Thermal states
#[derive(Debug, Clone, PartialEq)]
pub enum ThermalState {
    /// Cool
    Cool,
    /// Warm
    Warm,
    /// Hot
    Hot,
    /// Critical
    Critical,
}

/// Power management modes
#[derive(Debug, Clone, PartialEq)]
pub enum PowerMode {
    /// Performance
    Performance,
    /// Balanced
    Balanced,
    /// PowerSaver
    PowerSaver,
    /// Green
    Green,
}

/// Machine learning-based predictive optimizer
#[derive(Debug)]
pub struct PredictiveOptimizer {
    /// Configuration
    config: PredictiveOptimizerConfig,
    /// ML models
    models: HashMap<String, MLModel>,
    /// Training data
    training_data: VecDeque<TrainingDataPoint>,
    /// Predictions
    predictions: HashMap<String, PerformancePrediction>,
    /// State
    state: PredictiveOptimizerState,
}

/// Predictive optimizer configuration
#[derive(Debug, Clone)]
pub struct PredictiveOptimizerConfig {
    /// ML model type
    pub model_type: MLModelType,
    /// Training data size
    pub training_data_size: usize,
    /// Prediction horizon
    pub prediction_horizon: Duration,
    /// Learning rate
    pub learning_rate: f64,
    /// Enable online learning
    pub enable_online_learning: bool,
    /// Feature engineering
    pub feature_engineering: bool,
    /// Model update frequency
    pub model_update_frequency: Duration,
}

/// ML model types
#[derive(Debug, Clone, PartialEq)]
pub enum MLModelType {
    /// LinearRegression
    LinearRegression,
    /// NeuralNetwork
    NeuralNetwork,
    /// RandomForest
    RandomForest,
    /// SupportVectorMachine
    SupportVectorMachine,
    /// GradientBoosting
    GradientBoosting,
    /// LSTM
    LSTM,
    /// Custom
    Custom(String),
}

/// ML model abstraction
#[derive(Debug, Clone)]
pub struct MLModel {
    /// Model name
    pub name: String,
    /// Model type
    pub model_type: MLModelType,
    /// Model parameters
    pub parameters: HashMap<String, f64>,
    /// Training accuracy
    pub accuracy: f64,
    /// Last training time
    pub last_trained: SystemTime,
    /// Prediction count
    pub prediction_count: u64,
}

/// Training data point
#[derive(Debug, Clone)]
pub struct TrainingDataPoint {
    /// Timestamp
    pub timestamp: SystemTime,
    /// Features
    pub features: Vec<f64>,
    /// Target values
    pub targets: Vec<f64>,
    /// Context metadata
    pub context: HashMap<String, String>,
}

/// Performance prediction
#[derive(Debug, Clone)]
pub struct PerformancePrediction {
    /// Prediction timestamp
    pub timestamp: SystemTime,
    /// Predicted throughput
    pub predicted_throughput: f64,
    /// Predicted latency
    pub predicted_latency: Duration,
    /// Predicted resource usage
    pub predicted_resource_usage: f64,
    /// Prediction confidence
    pub confidence: f64,
    /// Prediction horizon
    pub horizon: Duration,
}

/// Predictive optimizer state
#[derive(Debug, Clone)]
pub struct PredictiveOptimizerState {
    /// Models trained
    pub models_trained: usize,
    /// Training data points
    pub training_data_points: usize,
    /// Predictions made
    pub predictions_made: u64,
    /// Average prediction accuracy
    pub average_accuracy: f64,
    /// Last model update
    pub last_model_update: SystemTime,
}

// Implementation of main PerformanceOptimizer
impl PerformanceOptimizer {
    /// Create a new performance optimizer
    pub fn new() -> SklResult<Self> {
        Ok(Self {
            config: OptimizerConfig::default(),
            goals: OptimizationGoals::default(),
            optimizers: Vec::new(),
            metrics: Arc::new(Mutex::new(PerformanceMetrics::default())),
            history: Arc::new(Mutex::new(VecDeque::new())),
            state: Arc::new(RwLock::new(OptimizerState::default())),
            baselines: Arc::new(RwLock::new(PerformanceBaselines::default())),
        })
    }

    /// Initialize the optimizer
    pub fn initialize(&mut self) -> SklResult<()> {
        let mut state = self.state.write().unwrap();
        state.active = true;
        state.phase = OptimizationPhase::Initialization;
        state.last_optimization = SystemTime::now();
        Ok(())
    }

    /// Set optimization goals
    pub fn set_goals(&mut self, goals: OptimizationGoals) -> SklResult<()> {
        self.goals = goals;
        Ok(())
    }

    /// Add a specialized optimizer
    pub fn add_optimizer(&mut self, optimizer: Box<dyn SpecializedOptimizer>) -> SklResult<()> {
        self.optimizers.push(optimizer);
        Ok(())
    }

    /// Start optimization loop
    pub async fn start_optimization(&mut self) -> SklResult<()> {
        loop {
            self.optimization_iteration().await?;
            tokio::time::sleep(self.config.optimization_interval).await;

            let state = self.state.read().unwrap();
            if !state.active {
                break;
            }
        }
        Ok(())
    }

    /// Perform a single optimization iteration
    async fn optimization_iteration(&mut self) -> SklResult<()> {
        // Update state
        {
            let mut state = self.state.write().unwrap();
            state.phase = OptimizationPhase::Analysis;
            state.iterations_completed += 1;
        }

        // Collect current metrics
        let current_metrics = self.collect_metrics()?;

        // Analyze performance with all optimizers
        let mut all_recommendations = Vec::new();
        for optimizer in &self.optimizers {
            let analysis = optimizer.analyze_performance(&current_metrics)?;
            let recommendations = optimizer.generate_recommendations(&analysis)?;
            all_recommendations.extend(recommendations);
        }

        // Prioritize and filter recommendations
        let selected_recommendations = self.select_recommendations(&all_recommendations)?;

        // Apply optimizations
        {
            let mut state = self.state.write().unwrap();
            state.phase = OptimizationPhase::Implementation;
        }

        for recommendation in selected_recommendations {
            self.apply_recommendation(&recommendation).await?;
        }

        // Monitor results
        {
            let mut state = self.state.write().unwrap();
            state.phase = OptimizationPhase::Monitoring;
            state.last_optimization = SystemTime::now();
        }

        Ok(())
    }

    /// Collect current performance metrics
    fn collect_metrics(&self) -> SklResult<PerformanceMetrics> {
        // Placeholder implementation - would collect real metrics
        Ok(PerformanceMetrics::default())
    }

    /// Select best recommendations to apply
    fn select_recommendations(
        &self,
        recommendations: &[OptimizationRecommendation],
    ) -> SklResult<Vec<OptimizationRecommendation>> {
        // Simple selection based on priority and expected impact
        let mut selected = recommendations
            .iter()
            .filter(|r| r.priority >= RecommendationPriority::Medium)
            .filter(|r| r.risk.risk_level < 0.7) // Low to medium risk only
            .cloned()
            .collect::<Vec<_>>();

        // Sort by expected impact
        selected.sort_by(|a, b| {
            b.expected_impact
                .throughput_improvement
                .partial_cmp(&a.expected_impact.throughput_improvement)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        // Take top N recommendations
        selected.truncate(5);
        Ok(selected)
    }

    /// Apply a single recommendation
    async fn apply_recommendation(
        &mut self,
        recommendation: &OptimizationRecommendation,
    ) -> SklResult<()> {
        // Placeholder implementation - would apply actual optimization
        println!("Applying optimization: {:?}", recommendation.action);
        Ok(())
    }

    /// Get optimization status
    #[must_use]
    pub fn get_status(&self) -> OptimizerState {
        self.state.read().unwrap().clone()
    }

    /// Stop optimization
    pub fn stop(&mut self) -> SklResult<()> {
        let mut state = self.state.write().unwrap();
        state.active = false;
        Ok(())
    }
}

// Implementation stubs for specialized optimizers
impl SpecializedOptimizer for ThroughputOptimizer {
    fn name(&self) -> &'static str {
        "ThroughputOptimizer"
    }

    fn domain(&self) -> OptimizationDomain {
        OptimizationDomain::Throughput
    }

    fn initialize(&mut self) -> SklResult<()> {
        Ok(())
    }

    fn analyze_performance(&self, metrics: &PerformanceMetrics) -> SklResult<PerformanceAnalysis> {
        Ok(PerformanceAnalysis {
            timestamp: SystemTime::now(),
            domain: OptimizationDomain::Throughput,
            performance_score: 0.7,
            bottlenecks: Vec::new(),
            opportunities: Vec::new(),
            trends: PerformanceTrends::default(),
            confidence: 0.8,
        })
    }

    fn generate_recommendations(
        &self,
        analysis: &PerformanceAnalysis,
    ) -> SklResult<Vec<OptimizationRecommendation>> {
        Ok(Vec::new())
    }

    fn apply_optimizations(
        &mut self,
        recommendations: &[OptimizationRecommendation],
    ) -> Pin<Box<dyn Future<Output = SklResult<OptimizationResult>> + Send + '_>> {
        Box::pin(async move {
            Ok(OptimizationResult {
                id: uuid::Uuid::new_v4().to_string(),
                timestamp: SystemTime::now(),
                domain: OptimizationDomain::Throughput,
                applied_recommendations: Vec::new(),
                before_metrics: PerformanceSnapshot::default(),
                after_metrics: PerformanceSnapshot::default(),
                actual_impact: ActualImpact::default(),
                success: true,
                error_message: None,
            })
        })
    }

    fn get_metrics(&self) -> SklResult<OptimizerMetrics> {
        Ok(self.metrics.clone())
    }

    fn update_config(&mut self, _config: HashMap<String, String>) -> SklResult<()> {
        Ok(())
    }
}

impl ThroughputOptimizer {
    pub fn new(config: ThroughputOptimizerConfig) -> SklResult<Self> {
        Ok(Self {
            config,
            state: ThroughputOptimizerState::default(),
            metrics: OptimizerMetrics::default(),
        })
    }
}

impl LatencyOptimizer {
    pub fn new(config: LatencyOptimizerConfig) -> SklResult<Self> {
        Ok(Self {
            config,
            state: LatencyOptimizerState::default(),
            metrics: OptimizerMetrics::default(),
        })
    }
}

impl EnergyOptimizer {
    pub fn new(config: EnergyOptimizerConfig) -> SklResult<Self> {
        Ok(Self {
            config,
            state: EnergyOptimizerState::default(),
            metrics: OptimizerMetrics::default(),
        })
    }
}

impl PredictiveOptimizer {
    pub fn new(config: PredictiveOptimizerConfig) -> SklResult<Self> {
        Ok(Self {
            config,
            models: HashMap::new(),
            training_data: VecDeque::new(),
            predictions: HashMap::new(),
            state: PredictiveOptimizerState::default(),
        })
    }
}

// Default implementations
impl Default for OptimizerConfig {
    fn default() -> Self {
        Self {
            optimization_interval: Duration::from_secs(30),
            continuous_optimization: true,
            measurement_window: Duration::from_secs(60),
            aggressiveness: 0.5,
            stability_threshold: 0.1,
            experimental_optimizations: false,
            max_iterations: 100,
            convergence_tolerance: 0.01,
        }
    }
}

impl Default for OptimizationGoals {
    fn default() -> Self {
        Self {
            primary_objective: OptimizationObjective::Throughput,
            secondary_objectives: vec![
                OptimizationObjective::ResourceUtilization,
                OptimizationObjective::EnergyEfficiency,
            ],
            constraints: OptimizationConstraints::default(),
            targets: PerformanceTargets::default(),
            weights: ObjectiveWeights::default(),
        }
    }
}

impl Default for OptimizationConstraints {
    fn default() -> Self {
        Self {
            max_latency: Some(Duration::from_millis(100)),
            min_throughput: Some(100.0),
            max_energy_consumption: None,
            max_cost: None,
            min_reliability: Some(0.99),
            resource_limits: ResourceLimits::default(),
            sla_requirements: Vec::new(),
        }
    }
}

impl Default for ResourceLimits {
    fn default() -> Self {
        Self {
            max_cpu_utilization: Some(90.0),
            max_memory_utilization: Some(90.0),
            max_gpu_utilization: Some(90.0),
            max_network_utilization: Some(80.0),
            max_storage_utilization: Some(80.0),
        }
    }
}

impl Default for PerformanceTargets {
    fn default() -> Self {
        Self {
            throughput: Some(1000.0),
            latency: Some(Duration::from_millis(10)),
            resource_utilization: Some(80.0),
            energy_efficiency: Some(0.8),
            cost_efficiency: Some(0.7),
            reliability: Some(0.999),
        }
    }
}

impl Default for ObjectiveWeights {
    fn default() -> Self {
        Self {
            throughput: 0.3,
            latency: 0.2,
            resource_utilization: 0.2,
            energy_efficiency: 0.1,
            cost: 0.1,
            reliability: 0.05,
            stability: 0.05,
        }
    }
}

impl Default for PerformanceMetrics {
    fn default() -> Self {
        Self {
            current_throughput: 0.0,
            current_latency: Duration::from_millis(0),
            resource_utilization: ResourceUtilizationMetrics::default(),
            energy_metrics: EnergyMetrics::default(),
            cache_metrics: CacheMetrics::default(),
            network_metrics: NetworkPerformanceMetrics::default(),
            pipeline_metrics: PipelineMetrics::default(),
            quality_metrics: QualityMetrics::default(),
            timestamp: SystemTime::now(),
        }
    }
}

impl Default for ResourceUtilizationMetrics {
    fn default() -> Self {
        Self {
            cpu_utilization: 0.0,
            memory_utilization: 0.0,
            gpu_utilization: None,
            storage_utilization: 0.0,
            network_utilization: 0.0,
            efficiency_score: 0.0,
        }
    }
}

impl Default for EnergyMetrics {
    fn default() -> Self {
        Self {
            total_power: 0.0,
            cpu_power: 0.0,
            gpu_power: None,
            memory_power: 0.0,
            storage_power: 0.0,
            cooling_power: 0.0,
            energy_efficiency: 0.0,
        }
    }
}

impl Default for CacheMetrics {
    fn default() -> Self {
        Self {
            l1_hit_rate: 0.0,
            l2_hit_rate: 0.0,
            l3_hit_rate: 0.0,
            memory_cache_hit_rate: 0.0,
            storage_cache_hit_rate: 0.0,
            average_access_time: Duration::from_nanos(0),
        }
    }
}

impl Default for NetworkPerformanceMetrics {
    fn default() -> Self {
        Self {
            bandwidth_utilization: 0.0,
            average_latency: Duration::from_millis(0),
            packet_loss_rate: 0.0,
            jitter: Duration::from_millis(0),
            connection_efficiency: 0.0,
        }
    }
}

impl Default for PipelineMetrics {
    fn default() -> Self {
        Self {
            pipeline_throughput: 0.0,
            stage_utilization: Vec::new(),
            pipeline_efficiency: 0.0,
            bottleneck_stages: Vec::new(),
            average_pipeline_latency: Duration::from_millis(0),
        }
    }
}

impl Default for QualityMetrics {
    fn default() -> Self {
        Self {
            error_rate: 0.0,
            success_rate: 1.0,
            retry_rate: 0.0,
            data_quality_score: 1.0,
            availability: 1.0,
        }
    }
}

impl Default for OptimizerState {
    fn default() -> Self {
        Self {
            active: false,
            phase: OptimizationPhase::Idle,
            iterations_completed: 0,
            last_optimization: SystemTime::now(),
            optimization_score: 0.0,
            converged: false,
        }
    }
}

impl Default for OptimizerMetrics {
    fn default() -> Self {
        Self {
            total_optimizations: 0,
            successful_optimizations: 0,
            failed_optimizations: 0,
            average_improvement: 0.0,
            optimization_frequency: 0.0,
            time_spent: Duration::from_secs(0),
        }
    }
}

impl Default for PerformanceBaselines {
    fn default() -> Self {
        Self {
            baseline_metrics: PerformanceSnapshot::default(),
            best_performance: PerformanceSnapshot::default(),
            worst_performance: PerformanceSnapshot::default(),
            baseline_time: SystemTime::now(),
        }
    }
}

impl Default for PerformanceSnapshot {
    fn default() -> Self {
        Self {
            timestamp: SystemTime::now(),
            throughput: 0.0,
            latency: Duration::from_millis(0),
            resource_utilization: 0.0,
            energy_consumption: 0.0,
            error_rate: 0.0,
            cost_rate: 0.0,
        }
    }
}

impl Default for ActualImpact {
    fn default() -> Self {
        Self {
            throughput_improvement: 0.0,
            latency_reduction: 0.0,
            resource_savings: 0.0,
            energy_savings: 0.0,
            cost_savings: 0.0,
            side_effects: Vec::new(),
        }
    }
}

impl Default for TrendData {
    fn default() -> Self {
        Self {
            direction: TrendDirection::Stable,
            magnitude: 0.0,
            confidence: 0.0,
            prediction: 0.0,
            variance: 0.0,
        }
    }
}

impl Default for ThroughputOptimizerState {
    fn default() -> Self {
        Self {
            current_parallelism: 1,
            current_batch_size: 10,
            current_throughput: 0.0,
            target_gap: 0.0,
            optimization_attempts: 0,
        }
    }
}

impl Default for LatencyOptimizerState {
    fn default() -> Self {
        Self {
            current_latency: Duration::from_millis(0),
            p95_latency: Duration::from_millis(0),
            p99_latency: Duration::from_millis(0),
            target_gap: Duration::from_millis(0),
            jitter: Duration::from_millis(0),
        }
    }
}

impl Default for EnergyOptimizerState {
    fn default() -> Self {
        Self {
            current_power: 0.0,
            current_efficiency: 0.0,
            thermal_state: ThermalState::Cool,
            power_mode: PowerMode::Balanced,
        }
    }
}

impl Default for PredictiveOptimizerState {
    fn default() -> Self {
        Self {
            models_trained: 0,
            training_data_points: 0,
            predictions_made: 0,
            average_accuracy: 0.0,
            last_model_update: SystemTime::now(),
        }
    }
}

// External dependencies
extern crate uuid;

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

    #[test]
    fn test_performance_optimizer_creation() {
        let result = PerformanceOptimizer::new();
        assert!(result.is_ok());
    }

    #[test]
    fn test_throughput_optimizer() {
        let config = ThroughputOptimizerConfig {
            target_throughput: 1000.0,
            strategy: ThroughputStrategy::MaxParallel,
            batch_size_optimization: true,
            pipeline_optimization: true,
            resource_scaling: true,
            load_balancing: true,
            max_parallelism: 10,
        };

        let result = ThroughputOptimizer::new(config);
        assert!(result.is_ok());

        let optimizer = result.unwrap();
        assert_eq!(optimizer.name(), "ThroughputOptimizer");
        assert_eq!(optimizer.domain(), OptimizationDomain::Throughput);
    }

    #[test]
    fn test_optimization_objectives() {
        let objectives = vec![
            OptimizationObjective::Throughput,
            OptimizationObjective::Latency,
            OptimizationObjective::ResourceUtilization,
            OptimizationObjective::EnergyEfficiency,
        ];

        for objective in objectives {
            assert!(matches!(objective, _)); // Accept any OptimizationObjective variant
        }
    }

    #[test]
    fn test_performance_metrics() {
        let metrics = PerformanceMetrics::default();
        assert_eq!(metrics.current_throughput, 0.0);
        assert_eq!(metrics.current_latency, Duration::from_millis(0));
        assert_eq!(metrics.resource_utilization.cpu_utilization, 0.0);
    }

    #[test]
    fn test_optimization_constraints() {
        let constraints = OptimizationConstraints::default();
        assert_eq!(constraints.max_latency, Some(Duration::from_millis(100)));
        assert_eq!(constraints.min_throughput, Some(100.0));
        assert_eq!(constraints.min_reliability, Some(0.99));
    }

    #[test]
    fn test_objective_weights() {
        let weights = ObjectiveWeights::default();
        let total_weight = weights.throughput
            + weights.latency
            + weights.resource_utilization
            + weights.energy_efficiency
            + weights.cost
            + weights.reliability
            + weights.stability;
        assert!((total_weight - 1.0).abs() < 0.001); // Should sum to 1.0
    }

    #[test]
    fn test_trend_directions() {
        let directions = vec![
            TrendDirection::Improving,
            TrendDirection::Degrading,
            TrendDirection::Stable,
            TrendDirection::Oscillating,
            TrendDirection::Unknown,
        ];

        for direction in directions {
            assert!(matches!(direction, _)); // Accept any TrendDirection variant
        }
    }

    #[test]
    fn test_optimizer_state() {
        let state = OptimizerState::default();
        assert!(!state.active);
        assert_eq!(state.phase, OptimizationPhase::Idle);
        assert_eq!(state.iterations_completed, 0);
        assert!(!state.converged);
    }

    #[test]
    fn test_recommendation_priority() {
        assert!(RecommendationPriority::Critical > RecommendationPriority::High);
        assert!(RecommendationPriority::High > RecommendationPriority::Medium);
        assert!(RecommendationPriority::Medium > RecommendationPriority::Low);
    }

    #[test]
    fn test_thermal_states() {
        let states = vec![
            ThermalState::Cool,
            ThermalState::Warm,
            ThermalState::Hot,
            ThermalState::Critical,
        ];

        for state in states {
            assert!(matches!(state, _)); // Accept any ThermalState variant
        }
    }

    #[tokio::test]
    async fn test_optimization_iteration() {
        let mut optimizer = PerformanceOptimizer::new().unwrap();
        optimizer.initialize().unwrap();

        // Add a throughput optimizer
        let throughput_config = ThroughputOptimizerConfig {
            target_throughput: 1000.0,
            strategy: ThroughputStrategy::MaxParallel,
            batch_size_optimization: true,
            pipeline_optimization: true,
            resource_scaling: true,
            load_balancing: true,
            max_parallelism: 10,
        };
        let throughput_optimizer = ThroughputOptimizer::new(throughput_config).unwrap();
        optimizer
            .add_optimizer(Box::new(throughput_optimizer))
            .unwrap();

        // Test a single optimization iteration
        let result = optimizer.optimization_iteration().await;
        assert!(result.is_ok());
    }
}