inferno-ai 0.10.3

#![allow(dead_code, unused_imports, unused_variables)]
use anyhow::Result;
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::path::PathBuf;
use std::sync::Arc;
use tokio::sync::RwLock;
use uuid::Uuid;

// Import CLI helper types
use super::cli::performance_optimization::{
    AutoTuningProgress, BenchmarkComparison, BenchmarkResult, Bottleneck, CacheAnalysis,
    CacheStatsExt, IoStatsExt, IoTestResult, MemoryLeak, MemoryOptResult, MemoryPressureResult,
    MemoryStatsExt, ModelOptResult, NetworkStatsExt, NetworkTestResult, OptimizationHistoryEntry,
    OptimizationPlan, OptimizationResultExt, OptimizationStep, ParallelOptResult, ParallelStats,
    PerformanceStatus, ProfileAnalysisResult, ProfileComparison, ProfileInfo, ResourceReport,
    ResourceStats, TaskDistributionAnalysis,
};

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceOptimizationConfig {
    pub enabled: bool,
    pub profiling_config: ProfilingConfig,
    pub optimization_config: OptimizationConfig,
    pub auto_tuning_config: AutoTuningConfig,
    pub resource_management_config: ResourceManagementConfig,
    pub caching_config: CachingOptimizationConfig,
    pub parallelization_config: ParallelizationConfig,
    pub memory_management_config: MemoryManagementConfig,
    pub io_optimization_config: IoOptimizationConfig,
    pub network_optimization_config: NetworkOptimizationConfig,
    pub ml_optimization_config: MlOptimizationConfig,
    pub monitoring_config: PerformanceMonitoringConfig,
    pub alerting_config: PerformanceAlertingConfig,
}

impl Default for PerformanceOptimizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            profiling_config: ProfilingConfig::default(),
            optimization_config: OptimizationConfig::default(),
            auto_tuning_config: AutoTuningConfig::default(),
            resource_management_config: ResourceManagementConfig::default(),
            caching_config: CachingOptimizationConfig::default(),
            parallelization_config: ParallelizationConfig::default(),
            memory_management_config: MemoryManagementConfig::default(),
            io_optimization_config: IoOptimizationConfig::default(),
            network_optimization_config: NetworkOptimizationConfig::default(),
            ml_optimization_config: MlOptimizationConfig::default(),
            monitoring_config: PerformanceMonitoringConfig::default(),
            alerting_config: PerformanceAlertingConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProfilingConfig {
    pub enabled: bool,
    pub profiling_mode: ProfilingMode,
    pub sampling_rate: f64,
    pub profile_types: Vec<ProfileType>,
    pub continuous_profiling: bool,
    pub profile_storage: ProfileStorageConfig,
    pub flame_graphs: bool,
    pub call_graphs: bool,
    pub memory_profiling: MemoryProfilingConfig,
    pub cpu_profiling: CpuProfilingConfig,
    pub io_profiling: IoProfilingConfig,
    pub network_profiling: NetworkProfilingConfig,
}

impl Default for ProfilingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            profiling_mode: ProfilingMode::Adaptive,
            sampling_rate: 0.1,
            profile_types: vec![
                ProfileType::Cpu,
                ProfileType::Memory,
                ProfileType::Io,
                ProfileType::Network,
            ],
            continuous_profiling: true,
            profile_storage: ProfileStorageConfig::default(),
            flame_graphs: true,
            call_graphs: true,
            memory_profiling: MemoryProfilingConfig::default(),
            cpu_profiling: CpuProfilingConfig::default(),
            io_profiling: IoProfilingConfig::default(),
            network_profiling: NetworkProfilingConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ProfilingMode {
    Continuous,
    Sampling,
    Triggered,
    Adaptive,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ProfileType {
    Cpu,
    Memory,
    Io,
    Network,
    Lock,
    Allocation,
    GarbageCollection,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProfileStorageConfig {
    pub storage_backend: StorageBackend,
    pub retention_days: u32,
    pub compression: bool,
    pub max_storage_gb: f64,
}

impl Default for ProfileStorageConfig {
    fn default() -> Self {
        Self {
            storage_backend: StorageBackend::Local,
            retention_days: 30,
            compression: true,
            max_storage_gb: 100.0,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum StorageBackend {
    Local,
    S3,
    Azure,
    GCS,
    Database,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryProfilingConfig {
    pub track_allocations: bool,
    pub heap_profiling: bool,
    pub leak_detection: bool,
    pub object_tracking: bool,
    pub gc_monitoring: bool,
}

impl Default for MemoryProfilingConfig {
    fn default() -> Self {
        Self {
            track_allocations: true,
            heap_profiling: true,
            leak_detection: true,
            object_tracking: false,
            gc_monitoring: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CpuProfilingConfig {
    pub sampling_frequency: u32,
    pub track_kernel_time: bool,
    pub track_user_time: bool,
    pub per_thread_profiling: bool,
    pub instruction_level: bool,
}

impl Default for CpuProfilingConfig {
    fn default() -> Self {
        Self {
            sampling_frequency: 100,
            track_kernel_time: true,
            track_user_time: true,
            per_thread_profiling: true,
            instruction_level: false,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IoProfilingConfig {
    pub track_file_io: bool,
    pub track_network_io: bool,
    pub track_database_io: bool,
    pub latency_tracking: bool,
    pub throughput_tracking: bool,
}

impl Default for IoProfilingConfig {
    fn default() -> Self {
        Self {
            track_file_io: true,
            track_network_io: true,
            track_database_io: true,
            latency_tracking: true,
            throughput_tracking: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NetworkProfilingConfig {
    pub track_connections: bool,
    pub track_bandwidth: bool,
    pub track_latency: bool,
    pub protocol_analysis: bool,
    pub packet_capture: bool,
}

impl Default for NetworkProfilingConfig {
    fn default() -> Self {
        Self {
            track_connections: true,
            track_bandwidth: true,
            track_latency: true,
            protocol_analysis: true,
            packet_capture: false,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationConfig {
    pub enabled: bool,
    pub optimization_strategies: Vec<OptimizationStrategy>,
    pub optimization_level: OptimizationLevel,
    pub automatic_optimization: bool,
    pub performance_targets: PerformanceTargets,
    pub optimization_constraints: OptimizationConstraints,
    pub rollback_on_regression: bool,
}

impl Default for OptimizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            optimization_strategies: vec![
                OptimizationStrategy::CacheOptimization,
                OptimizationStrategy::ParallelExecution,
                OptimizationStrategy::ResourcePooling,
                OptimizationStrategy::LazyLoading,
                OptimizationStrategy::Prefetching,
            ],
            optimization_level: OptimizationLevel::Balanced,
            automatic_optimization: true,
            performance_targets: PerformanceTargets::default(),
            optimization_constraints: OptimizationConstraints::default(),
            rollback_on_regression: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationStrategy {
    CacheOptimization,
    ParallelExecution,
    ResourcePooling,
    LazyLoading,
    Prefetching,
    JitCompilation,
    CodeInlining,
    LoopUnrolling,
    Vectorization,
    MemoryAlignment,
    DataStructureOptimization,
    AlgorithmSelection,
    QueryOptimization,
    IndexOptimization,
    CompressionOptimization,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationLevel {
    None,
    Conservative,
    Balanced,
    Aggressive,
    Maximum,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTargets {
    pub latency_p50_ms: Option<f64>,
    pub latency_p95_ms: Option<f64>,
    pub latency_p99_ms: Option<f64>,
    pub throughput_rps: Option<f64>,
    pub cpu_utilization_percent: Option<f64>,
    pub memory_utilization_percent: Option<f64>,
    pub error_rate_percent: Option<f64>,
}

impl Default for PerformanceTargets {
    fn default() -> Self {
        Self {
            latency_p50_ms: Some(100.0),
            latency_p95_ms: Some(500.0),
            latency_p99_ms: Some(1000.0),
            throughput_rps: Some(1000.0),
            cpu_utilization_percent: Some(70.0),
            memory_utilization_percent: Some(80.0),
            error_rate_percent: Some(0.1),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationConstraints {
    pub max_memory_mb: Option<usize>,
    pub max_cpu_cores: Option<usize>,
    pub max_io_operations: Option<usize>,
    pub max_network_bandwidth_mbps: Option<f64>,
    pub min_accuracy_percent: Option<f64>,
    pub max_optimization_time_seconds: Option<u64>,
}

impl Default for OptimizationConstraints {
    fn default() -> Self {
        Self {
            max_memory_mb: None,
            max_cpu_cores: None,
            max_io_operations: None,
            max_network_bandwidth_mbps: None,
            min_accuracy_percent: Some(99.9),
            max_optimization_time_seconds: Some(300),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AutoTuningConfig {
    pub enabled: bool,
    pub tuning_algorithms: Vec<TuningAlgorithm>,
    pub tuning_parameters: Vec<TuningParameter>,
    pub learning_rate: f64,
    pub exploration_rate: f64,
    pub history_window_size: usize,
    pub tuning_interval_seconds: u64,
    pub ml_based_tuning: MlBasedTuningConfig,
    pub feedback_loop: FeedbackLoopConfig,
}

impl Default for AutoTuningConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            tuning_algorithms: vec![
                TuningAlgorithm::GridSearch,
                TuningAlgorithm::BayesianOptimization,
                TuningAlgorithm::GeneticAlgorithm,
                TuningAlgorithm::ReinforcementLearning,
            ],
            tuning_parameters: vec![],
            learning_rate: 0.1,
            exploration_rate: 0.2,
            history_window_size: 1000,
            tuning_interval_seconds: 300,
            ml_based_tuning: MlBasedTuningConfig::default(),
            feedback_loop: FeedbackLoopConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TuningAlgorithm {
    GridSearch,
    RandomSearch,
    BayesianOptimization,
    GeneticAlgorithm,
    SimulatedAnnealing,
    ParticleSwarm,
    ReinforcementLearning,
    NeuralArchitectureSearch,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TuningParameter {
    pub name: String,
    pub parameter_type: ParameterType,
    pub current_value: f64,
    pub min_value: f64,
    pub max_value: f64,
    pub step_size: f64,
    pub unit: String,
    pub impact_score: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ParameterType {
    ThreadPoolSize,
    CacheSize,
    BatchSize,
    BufferSize,
    ConnectionPoolSize,
    QueueDepth,
    TimeoutValue,
    RetryCount,
    SamplingRate,
    CompressionLevel,
    ParallelismDegree,
    MemoryLimit,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MlBasedTuningConfig {
    pub enabled: bool,
    pub model_type: MlModelType,
    pub training_data_size: usize,
    pub retraining_interval_hours: u32,
    pub feature_engineering: FeatureEngineeringConfig,
    pub model_validation: ModelValidationConfig,
}

impl Default for MlBasedTuningConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            model_type: MlModelType::RandomForest,
            training_data_size: 10000,
            retraining_interval_hours: 24,
            feature_engineering: FeatureEngineeringConfig::default(),
            model_validation: ModelValidationConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MlModelType {
    LinearRegression,
    RandomForest,
    GradientBoosting,
    NeuralNetwork,
    SupportVectorMachine,
    BayesianNetwork,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureEngineeringConfig {
    pub automatic_feature_selection: bool,
    pub feature_scaling: bool,
    pub feature_encoding: bool,
    pub polynomial_features: bool,
    pub interaction_features: bool,
}

impl Default for FeatureEngineeringConfig {
    fn default() -> Self {
        Self {
            automatic_feature_selection: true,
            feature_scaling: true,
            feature_encoding: true,
            polynomial_features: false,
            interaction_features: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelValidationConfig {
    pub cross_validation_folds: usize,
    pub validation_split_ratio: f64,
    pub performance_metrics: Vec<String>,
    pub early_stopping: bool,
    pub overfitting_detection: bool,
}

impl Default for ModelValidationConfig {
    fn default() -> Self {
        Self {
            cross_validation_folds: 5,
            validation_split_ratio: 0.2,
            performance_metrics: vec!["mse".to_string(), "mae".to_string(), "r2".to_string()],
            early_stopping: true,
            overfitting_detection: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeedbackLoopConfig {
    pub enabled: bool,
    pub feedback_sources: Vec<FeedbackSource>,
    pub feedback_processing: FeedbackProcessingConfig,
    pub adaptation_strategy: AdaptationStrategy,
}

impl Default for FeedbackLoopConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            feedback_sources: vec![
                FeedbackSource::SystemMetrics,
                FeedbackSource::UserFeedback,
                FeedbackSource::ErrorLogs,
                FeedbackSource::PerformanceTests,
            ],
            feedback_processing: FeedbackProcessingConfig::default(),
            adaptation_strategy: AdaptationStrategy::Gradual,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FeedbackSource {
    SystemMetrics,
    UserFeedback,
    ErrorLogs,
    PerformanceTests,
    LoadTests,
    Production,
    Synthetic,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeedbackProcessingConfig {
    pub aggregation_method: AggregationMethod,
    pub noise_filtering: bool,
    pub anomaly_detection: bool,
    pub trend_analysis: bool,
}

impl Default for FeedbackProcessingConfig {
    fn default() -> Self {
        Self {
            aggregation_method: AggregationMethod::WeightedAverage,
            noise_filtering: true,
            anomaly_detection: true,
            trend_analysis: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AggregationMethod {
    Average,
    WeightedAverage,
    Median,
    Percentile,
    ExponentialMovingAverage,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AdaptationStrategy {
    Immediate,
    Gradual,
    Scheduled,
    ThresholdBased,
    MLBased,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceManagementConfig {
    pub enabled: bool,
    pub resource_allocation: ResourceAllocationConfig,
    pub resource_pooling: ResourcePoolingConfig,
    pub resource_scheduling: ResourceSchedulingConfig,
    pub resource_monitoring: ResourceMonitoringConfig,
    pub auto_scaling: AutoScalingConfig,
}

impl Default for ResourceManagementConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            resource_allocation: ResourceAllocationConfig::default(),
            resource_pooling: ResourcePoolingConfig::default(),
            resource_scheduling: ResourceSchedulingConfig::default(),
            resource_monitoring: ResourceMonitoringConfig::default(),
            auto_scaling: AutoScalingConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceAllocationConfig {
    pub allocation_strategy: AllocationStrategy,
    pub cpu_allocation: CpuAllocationConfig,
    pub memory_allocation: MemoryAllocationConfig,
    pub io_allocation: IoAllocationConfig,
    pub network_allocation: NetworkAllocationConfig,
    pub gpu_allocation: GpuAllocationConfig,
}

impl Default for ResourceAllocationConfig {
    fn default() -> Self {
        Self {
            allocation_strategy: AllocationStrategy::Dynamic,
            cpu_allocation: CpuAllocationConfig::default(),
            memory_allocation: MemoryAllocationConfig::default(),
            io_allocation: IoAllocationConfig::default(),
            network_allocation: NetworkAllocationConfig::default(),
            gpu_allocation: GpuAllocationConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AllocationStrategy {
    Static,
    Dynamic,
    Priority,
    Fair,
    Weighted,
    MLBased,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CpuAllocationConfig {
    pub core_affinity: bool,
    pub numa_awareness: bool,
    pub hyperthreading: bool,
    pub cpu_shares: Option<u32>,
    pub cpu_quota: Option<u32>,
}

impl Default for CpuAllocationConfig {
    fn default() -> Self {
        Self {
            core_affinity: true,
            numa_awareness: true,
            hyperthreading: true,
            cpu_shares: None,
            cpu_quota: None,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryAllocationConfig {
    pub huge_pages: bool,
    pub memory_locking: bool,
    pub swap_control: bool,
    pub memory_limit_mb: Option<usize>,
    pub memory_reservation_mb: Option<usize>,
}

impl Default for MemoryAllocationConfig {
    fn default() -> Self {
        Self {
            huge_pages: false,
            memory_locking: false,
            swap_control: true,
            memory_limit_mb: None,
            memory_reservation_mb: None,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IoAllocationConfig {
    pub io_priority: IoPriority,
    pub io_scheduler: IoScheduler,
    pub read_ahead_kb: Option<usize>,
    pub write_cache: bool,
}

impl Default for IoAllocationConfig {
    fn default() -> Self {
        Self {
            io_priority: IoPriority::Normal,
            io_scheduler: IoScheduler::Default,
            read_ahead_kb: Some(128),
            write_cache: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum IoPriority {
    Low,
    Normal,
    High,
    RealTime,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum IoScheduler {
    Default,
    Noop,
    Deadline,
    Cfq,
    Bfq,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NetworkAllocationConfig {
    pub bandwidth_limit_mbps: Option<f64>,
    pub connection_limit: Option<usize>,
    pub qos_class: QosClass,
    pub tcp_optimization: bool,
}

impl Default for NetworkAllocationConfig {
    fn default() -> Self {
        Self {
            bandwidth_limit_mbps: None,
            connection_limit: None,
            qos_class: QosClass::Default,
            tcp_optimization: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum QosClass {
    Default,
    Low,
    Normal,
    High,
    Guaranteed,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuAllocationConfig {
    pub gpu_device_ids: Vec<u32>,
    pub gpu_memory_limit_mb: Option<usize>,
    pub gpu_compute_units: Option<u32>,
    pub multi_gpu_strategy: MultiGpuStrategy,
}

impl Default for GpuAllocationConfig {
    fn default() -> Self {
        Self {
            gpu_device_ids: vec![],
            gpu_memory_limit_mb: None,
            gpu_compute_units: None,
            multi_gpu_strategy: MultiGpuStrategy::DataParallel,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MultiGpuStrategy {
    Single,
    DataParallel,
    ModelParallel,
    Pipeline,
    Hybrid,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourcePoolingConfig {
    pub enabled: bool,
    pub thread_pool_config: ThreadPoolConfig,
    pub connection_pool_config: ConnectionPoolConfig,
    pub object_pool_config: ObjectPoolConfig,
    pub buffer_pool_config: BufferPoolConfig,
}

impl Default for ResourcePoolingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            thread_pool_config: ThreadPoolConfig::default(),
            connection_pool_config: ConnectionPoolConfig::default(),
            object_pool_config: ObjectPoolConfig::default(),
            buffer_pool_config: BufferPoolConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThreadPoolConfig {
    pub min_threads: usize,
    pub max_threads: usize,
    pub keep_alive_seconds: u64,
    pub queue_size: usize,
    pub rejection_policy: RejectionPolicy,
}

impl Default for ThreadPoolConfig {
    fn default() -> Self {
        Self {
            min_threads: 4,
            max_threads: 64,
            keep_alive_seconds: 60,
            queue_size: 1000,
            rejection_policy: RejectionPolicy::CallerRuns,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RejectionPolicy {
    Abort,
    CallerRuns,
    Discard,
    DiscardOldest,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConnectionPoolConfig {
    pub min_connections: usize,
    pub max_connections: usize,
    pub connection_timeout_seconds: u64,
    pub idle_timeout_seconds: u64,
    pub validation_query: Option<String>,
}

impl Default for ConnectionPoolConfig {
    fn default() -> Self {
        Self {
            min_connections: 10,
            max_connections: 100,
            connection_timeout_seconds: 30,
            idle_timeout_seconds: 300,
            validation_query: None,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ObjectPoolConfig {
    pub min_objects: usize,
    pub max_objects: usize,
    pub object_lifetime_seconds: u64,
    pub validation_interval_seconds: u64,
}

impl Default for ObjectPoolConfig {
    fn default() -> Self {
        Self {
            min_objects: 10,
            max_objects: 1000,
            object_lifetime_seconds: 3600,
            validation_interval_seconds: 60,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BufferPoolConfig {
    pub buffer_size_kb: usize,
    pub min_buffers: usize,
    pub max_buffers: usize,
    pub direct_buffers: bool,
}

impl Default for BufferPoolConfig {
    fn default() -> Self {
        Self {
            buffer_size_kb: 64,
            min_buffers: 100,
            max_buffers: 10000,
            direct_buffers: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceSchedulingConfig {
    pub scheduler_type: SchedulerType,
    pub scheduling_policy: SchedulingPolicy,
    pub priority_levels: usize,
    pub time_slice_ms: u64,
    pub preemption: bool,
}

impl Default for ResourceSchedulingConfig {
    fn default() -> Self {
        Self {
            scheduler_type: SchedulerType::WorkStealing,
            scheduling_policy: SchedulingPolicy::Fair,
            priority_levels: 10,
            time_slice_ms: 100,
            preemption: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SchedulerType {
    Simple,
    RoundRobin,
    Priority,
    WorkStealing,
    Deadline,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SchedulingPolicy {
    FIFO,
    LIFO,
    Fair,
    Priority,
    Deadline,
    Weighted,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceMonitoringConfig {
    pub monitoring_interval_seconds: u64,
    pub metrics_collection: Vec<MetricType>,
    pub alerting_thresholds: HashMap<String, f64>,
    pub historical_data_retention_days: u32,
}

impl Default for ResourceMonitoringConfig {
    fn default() -> Self {
        Self {
            monitoring_interval_seconds: 60,
            metrics_collection: vec![
                MetricType::CpuUsage,
                MetricType::MemoryUsage,
                MetricType::DiskIO,
                MetricType::NetworkIO,
            ],
            alerting_thresholds: HashMap::new(),
            historical_data_retention_days: 30,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MetricType {
    CpuUsage,
    MemoryUsage,
    DiskIO,
    NetworkIO,
    GpuUsage,
    CacheHitRate,
    QueueDepth,
    ThreadCount,
    ConnectionCount,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AutoScalingConfig {
    pub enabled: bool,
    pub scaling_policies: Vec<ScalingPolicy>,
    pub min_instances: usize,
    pub max_instances: usize,
    pub scale_up_cooldown_seconds: u64,
    pub scale_down_cooldown_seconds: u64,
    pub predictive_scaling: PredictiveScalingConfig,
}

impl Default for AutoScalingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            scaling_policies: vec![],
            min_instances: 1,
            max_instances: 100,
            scale_up_cooldown_seconds: 60,
            scale_down_cooldown_seconds: 300,
            predictive_scaling: PredictiveScalingConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalingPolicy {
    pub name: String,
    pub policy_type: ScalingPolicyType,
    pub metric: String,
    pub target_value: f64,
    pub scale_up_threshold: f64,
    pub scale_down_threshold: f64,
    pub scale_increment: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ScalingPolicyType {
    TargetTracking,
    StepScaling,
    SimpleScaling,
    Predictive,
    Scheduled,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictiveScalingConfig {
    pub enabled: bool,
    pub prediction_window_minutes: u32,
    pub ml_model: String,
    pub confidence_threshold: f64,
}

impl Default for PredictiveScalingConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            prediction_window_minutes: 60,
            ml_model: "arima".to_string(),
            confidence_threshold: 0.8,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CachingOptimizationConfig {
    pub enabled: bool,
    pub cache_hierarchy: Vec<CacheLevel>,
    pub cache_strategies: Vec<CacheStrategy>,
    pub cache_warming: CacheWarmingConfig,
    pub cache_invalidation: CacheInvalidationConfig,
    pub distributed_cache: DistributedCacheConfig,
}

impl Default for CachingOptimizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            cache_hierarchy: vec![CacheLevel::l1(), CacheLevel::l2(), CacheLevel::l3()],
            cache_strategies: vec![CacheStrategy::WriteThrough, CacheStrategy::Adaptive],
            cache_warming: CacheWarmingConfig::default(),
            cache_invalidation: CacheInvalidationConfig::default(),
            distributed_cache: DistributedCacheConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheLevel {
    pub level_name: String,
    pub cache_size_mb: usize,
    pub ttl_seconds: u64,
    pub eviction_policy: EvictionPolicy,
}

impl CacheLevel {
    pub fn l1() -> Self {
        Self {
            level_name: "L1".to_string(),
            cache_size_mb: 64,
            ttl_seconds: 60,
            eviction_policy: EvictionPolicy::LRU,
        }
    }

    pub fn l2() -> Self {
        Self {
            level_name: "L2".to_string(),
            cache_size_mb: 512,
            ttl_seconds: 300,
            eviction_policy: EvictionPolicy::LFU,
        }
    }

    pub fn l3() -> Self {
        Self {
            level_name: "L3".to_string(),
            cache_size_mb: 2048,
            ttl_seconds: 3600,
            eviction_policy: EvictionPolicy::FIFO,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EvictionPolicy {
    LRU,
    LFU,
    FIFO,
    Random,
    ARC,
    TwoQ,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CacheStrategy {
    WriteThrough,
    WriteBack,
    WriteBehind,
    RefreshAhead,
    ReadThrough,
    Adaptive,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheWarmingConfig {
    pub enabled: bool,
    pub warming_strategy: WarmingStrategy,
    pub warming_schedule: String,
    pub data_sources: Vec<String>,
}

impl Default for CacheWarmingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            warming_strategy: WarmingStrategy::Predictive,
            warming_schedule: "0 * * * *".to_string(),
            data_sources: vec![],
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum WarmingStrategy {
    Eager,
    Lazy,
    Scheduled,
    Predictive,
    OnDemand,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheInvalidationConfig {
    pub strategy: InvalidationStrategy,
    pub ttl_based: bool,
    pub event_based: bool,
    pub manual_invalidation: bool,
}

impl Default for CacheInvalidationConfig {
    fn default() -> Self {
        Self {
            strategy: InvalidationStrategy::Selective,
            ttl_based: true,
            event_based: true,
            manual_invalidation: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum InvalidationStrategy {
    All,
    Selective,
    Tagged,
    Pattern,
    Cascade,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistributedCacheConfig {
    pub enabled: bool,
    pub cache_nodes: Vec<String>,
    pub replication_factor: usize,
    pub consistency_level: ConsistencyLevel,
    pub partitioning: PartitioningStrategy,
}

impl Default for DistributedCacheConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            cache_nodes: vec![],
            replication_factor: 2,
            consistency_level: ConsistencyLevel::Eventual,
            partitioning: PartitioningStrategy::Consistent,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConsistencyLevel {
    Strong,
    Eventual,
    Weak,
    Bounded,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PartitioningStrategy {
    Consistent,
    Range,
    Hash,
    Geographic,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParallelizationConfig {
    pub enabled: bool,
    pub parallelism_level: ParallelismLevel,
    pub task_decomposition: TaskDecompositionConfig,
    pub load_balancing: LoadBalancingConfig,
    pub synchronization: SynchronizationConfig,
}

impl Default for ParallelizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            parallelism_level: ParallelismLevel::Auto,
            task_decomposition: TaskDecompositionConfig::default(),
            load_balancing: LoadBalancingConfig::default(),
            synchronization: SynchronizationConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ParallelismLevel {
    None,
    Low,
    Medium,
    High,
    Auto,
    Custom(usize),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TaskDecompositionConfig {
    pub strategy: DecompositionStrategy,
    pub granularity: TaskGranularity,
    pub min_task_size: usize,
    pub max_task_size: usize,
}

impl Default for TaskDecompositionConfig {
    fn default() -> Self {
        Self {
            strategy: DecompositionStrategy::Dynamic,
            granularity: TaskGranularity::Medium,
            min_task_size: 100,
            max_task_size: 10000,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DecompositionStrategy {
    Static,
    Dynamic,
    Guided,
    Adaptive,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TaskGranularity {
    Fine,
    Medium,
    Coarse,
    Adaptive,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LoadBalancingConfig {
    pub algorithm: LoadBalancingAlgorithm,
    pub health_check_interval_seconds: u64,
    pub failover_enabled: bool,
    pub sticky_sessions: bool,
}

impl Default for LoadBalancingConfig {
    fn default() -> Self {
        Self {
            algorithm: LoadBalancingAlgorithm::LeastConnections,
            health_check_interval_seconds: 30,
            failover_enabled: true,
            sticky_sessions: false,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LoadBalancingAlgorithm {
    RoundRobin,
    LeastConnections,
    WeightedRoundRobin,
    Random,
    IpHash,
    ConsistentHash,
    PowerOfTwoChoices,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SynchronizationConfig {
    pub lock_strategy: LockStrategy,
    pub wait_strategy: WaitStrategy,
    pub deadlock_detection: bool,
    pub priority_inheritance: bool,
}

impl Default for SynchronizationConfig {
    fn default() -> Self {
        Self {
            lock_strategy: LockStrategy::Adaptive,
            wait_strategy: WaitStrategy::Exponential,
            deadlock_detection: true,
            priority_inheritance: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LockStrategy {
    Spinlock,
    Mutex,
    RwLock,
    Adaptive,
    LockFree,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum WaitStrategy {
    Busy,
    Yield,
    Sleep,
    Exponential,
    Adaptive,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryManagementConfig {
    pub memory_allocator: MemoryAllocator,
    pub garbage_collection: GarbageCollectionConfig,
    pub memory_pooling: MemoryPoolingConfig,
    pub memory_compression: MemoryCompressionConfig,
    pub memory_mapping: MemoryMappingConfig,
}

impl Default for MemoryManagementConfig {
    fn default() -> Self {
        Self {
            memory_allocator: MemoryAllocator::System,
            garbage_collection: GarbageCollectionConfig::default(),
            memory_pooling: MemoryPoolingConfig::default(),
            memory_compression: MemoryCompressionConfig::default(),
            memory_mapping: MemoryMappingConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum MemoryAllocator {
    System,
    JeMalloc,
    TcMalloc,
    MiMalloc,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GarbageCollectionConfig {
    pub gc_type: GcType,
    pub gc_trigger_mb: usize,
    pub gc_interval_seconds: u64,
    pub incremental_gc: bool,
    pub concurrent_gc: bool,
}

impl Default for GarbageCollectionConfig {
    fn default() -> Self {
        Self {
            gc_type: GcType::Generational,
            gc_trigger_mb: 100,
            gc_interval_seconds: 60,
            incremental_gc: true,
            concurrent_gc: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum GcType {
    MarkSweep,
    Generational,
    Incremental,
    Concurrent,
    Reference,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryPoolingConfig {
    pub enabled: bool,
    pub pool_sizes: Vec<PoolSize>,
    pub pre_allocation: bool,
    pub zero_on_free: bool,
}

impl Default for MemoryPoolingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            pool_sizes: vec![
                PoolSize {
                    size_bytes: 64,
                    count: 1000,
                },
                PoolSize {
                    size_bytes: 256,
                    count: 500,
                },
                PoolSize {
                    size_bytes: 1024,
                    count: 100,
                },
            ],
            pre_allocation: true,
            zero_on_free: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PoolSize {
    pub size_bytes: usize,
    pub count: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryCompressionConfig {
    pub enabled: bool,
    pub compression_threshold_mb: usize,
    pub compression_algorithm: CompressionAlgorithm,
    pub compression_level: u8,
}

impl Default for MemoryCompressionConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            compression_threshold_mb: 100,
            compression_algorithm: CompressionAlgorithm::LZ4,
            compression_level: 3,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CompressionAlgorithm {
    LZ4,
    Zstd,
    Snappy,
    Gzip,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryMappingConfig {
    pub enabled: bool,
    pub map_files: bool,
    pub shared_memory: bool,
    pub huge_pages: bool,
}

impl Default for MemoryMappingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            map_files: true,
            shared_memory: true,
            huge_pages: false,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IoOptimizationConfig {
    pub async_io: bool,
    pub direct_io: bool,
    pub buffered_io: BufferedIoConfig,
    pub io_scheduling: IoSchedulingConfig,
    pub prefetching: PrefetchingConfig,
}

impl Default for IoOptimizationConfig {
    fn default() -> Self {
        Self {
            async_io: true,
            direct_io: false,
            buffered_io: BufferedIoConfig::default(),
            io_scheduling: IoSchedulingConfig::default(),
            prefetching: PrefetchingConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BufferedIoConfig {
    pub buffer_size_kb: usize,
    pub double_buffering: bool,
    pub write_combining: bool,
    pub read_ahead: bool,
}

impl Default for BufferedIoConfig {
    fn default() -> Self {
        Self {
            buffer_size_kb: 64,
            double_buffering: true,
            write_combining: true,
            read_ahead: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IoSchedulingConfig {
    pub scheduler: IoScheduler,
    pub priority_queues: usize,
    pub max_outstanding_io: usize,
    pub batching: bool,
}

impl Default for IoSchedulingConfig {
    fn default() -> Self {
        Self {
            scheduler: IoScheduler::Default,
            priority_queues: 3,
            max_outstanding_io: 128,
            batching: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PrefetchingConfig {
    pub enabled: bool,
    pub prefetch_distance: usize,
    pub prefetch_degree: usize,
    pub adaptive: bool,
}

impl Default for PrefetchingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            prefetch_distance: 4,
            prefetch_degree: 2,
            adaptive: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NetworkOptimizationConfig {
    pub tcp_optimization: TcpOptimizationConfig,
    pub connection_pooling: bool,
    pub keep_alive: bool,
    pub compression: bool,
    pub protocol_optimization: ProtocolOptimizationConfig,
}

impl Default for NetworkOptimizationConfig {
    fn default() -> Self {
        Self {
            tcp_optimization: TcpOptimizationConfig::default(),
            connection_pooling: true,
            keep_alive: true,
            compression: true,
            protocol_optimization: ProtocolOptimizationConfig::default(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TcpOptimizationConfig {
    pub tcp_nodelay: bool,
    pub tcp_quickack: bool,
    pub socket_buffer_size_kb: usize,
    pub congestion_control: String,
}

impl Default for TcpOptimizationConfig {
    fn default() -> Self {
        Self {
            tcp_nodelay: true,
            tcp_quickack: true,
            socket_buffer_size_kb: 256,
            congestion_control: "cubic".to_string(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProtocolOptimizationConfig {
    pub http2_enabled: bool,
    pub quic_enabled: bool,
    pub multiplexing: bool,
    pub pipelining: bool,
}

impl Default for ProtocolOptimizationConfig {
    fn default() -> Self {
        Self {
            http2_enabled: true,
            quic_enabled: false,
            multiplexing: true,
            pipelining: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct MlOptimizationConfig {
    pub model_optimization: ModelOptimizationConfig,
    pub inference_optimization: InferenceOptimizationConfig,
    pub training_optimization: TrainingOptimizationConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct ModelOptimizationConfig {
    pub quantization: QuantizationConfig,
    pub pruning: PruningConfig,
    pub distillation: DistillationConfig,
    pub fusion: FusionConfig,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationConfig {
    pub enabled: bool,
    pub quantization_type: QuantizationType,
    pub bit_width: u8,
    pub dynamic: bool,
}

impl Default for QuantizationConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            quantization_type: QuantizationType::Int8,
            bit_width: 8,
            dynamic: false,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum QuantizationType {
    Int8,
    Int4,
    Float16,
    BFloat16,
    Mixed,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PruningConfig {
    pub enabled: bool,
    pub sparsity_target: f64,
    pub structured: bool,
    pub gradual: bool,
}

impl Default for PruningConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            sparsity_target: 0.5,
            structured: true,
            gradual: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistillationConfig {
    pub enabled: bool,
    pub teacher_model: String,
    pub temperature: f64,
    pub alpha: f64,
}

impl Default for DistillationConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            teacher_model: String::new(),
            temperature: 3.0,
            alpha: 0.7,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FusionConfig {
    pub enabled: bool,
    pub operator_fusion: bool,
    pub graph_optimization: bool,
    pub constant_folding: bool,
}

impl Default for FusionConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            operator_fusion: true,
            graph_optimization: true,
            constant_folding: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct InferenceOptimizationConfig {
    pub batch_size: usize,
    pub dynamic_batching: bool,
    pub model_caching: bool,
    pub kernel_optimization: bool,
}

impl Default for InferenceOptimizationConfig {
    fn default() -> Self {
        Self {
            batch_size: 32,
            dynamic_batching: true,
            model_caching: true,
            kernel_optimization: true,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingOptimizationConfig {
    pub mixed_precision: bool,
    pub gradient_accumulation: usize,
    pub distributed_training: bool,
    pub data_parallel: bool,
}

impl Default for TrainingOptimizationConfig {
    fn default() -> Self {
        Self {
            mixed_precision: true,
            gradient_accumulation: 1,
            distributed_training: false,
            data_parallel: false,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMonitoringConfig {
    pub enabled: bool,
    pub monitoring_interval_seconds: u64,
    pub metrics_retention_days: u32,
    pub dashboards: Vec<DashboardConfig>,
    pub exporters: Vec<MetricExporter>,
}

impl Default for PerformanceMonitoringConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            monitoring_interval_seconds: 60,
            metrics_retention_days: 30,
            dashboards: vec![],
            exporters: vec![],
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardConfig {
    pub name: String,
    pub dashboard_type: DashboardType,
    pub refresh_interval_seconds: u64,
    pub widgets: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DashboardType {
    Overview,
    Detailed,
    Executive,
    Technical,
    Custom,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MetricExporter {
    pub exporter_type: ExporterType,
    pub endpoint: String,
    pub format: String,
    pub interval_seconds: u64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ExporterType {
    Prometheus,
    StatsD,
    OpenTelemetry,
    CloudWatch,
    Custom(String),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceAlertingConfig {
    pub enabled: bool,
    pub alert_rules: Vec<PerformanceAlertRule>,
    pub notification_channels: Vec<String>,
    pub throttling_minutes: u32,
}

impl Default for PerformanceAlertingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            alert_rules: vec![],
            notification_channels: vec![],
            throttling_minutes: 15,
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceAlertRule {
    pub name: String,
    pub metric: String,
    pub condition: AlertCondition,
    pub threshold: f64,
    pub duration_seconds: u64,
    pub severity: AlertSeverity,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlertCondition {
    GreaterThan,
    LessThan,
    Equals,
    NotEquals,
    Change,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlertSeverity {
    Info,
    Warning,
    Critical,
    Emergency,
}

// Core performance data structures

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceProfile {
    pub id: String,
    pub timestamp: DateTime<Utc>,
    pub duration_ms: u64,
    pub cpu_profile: CpuProfile,
    pub memory_profile: MemoryProfile,
    pub io_profile: IoProfile,
    pub network_profile: NetworkProfile,
    pub custom_metrics: HashMap<String, f64>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CpuProfile {
    pub usage_percent: f64,
    pub user_time_ms: u64,
    pub system_time_ms: u64,
    pub idle_time_ms: u64,
    pub wait_time_ms: u64,
    pub core_usage: Vec<f64>,
    pub thread_count: usize,
    pub context_switches: u64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryProfile {
    pub used_bytes: u64,
    pub allocated_bytes: u64,
    pub heap_bytes: u64,
    pub stack_bytes: u64,
    pub cache_bytes: u64,
    pub page_faults: u64,
    pub allocations: u64,
    pub deallocations: u64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct IoProfile {
    pub read_bytes: u64,
    pub write_bytes: u64,
    pub read_operations: u64,
    pub write_operations: u64,
    pub avg_read_latency_ms: f64,
    pub avg_write_latency_ms: f64,
    pub queue_depth: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NetworkProfile {
    pub bytes_sent: u64,
    pub bytes_received: u64,
    pub packets_sent: u64,
    pub packets_received: u64,
    pub connections: usize,
    pub avg_latency_ms: f64,
    pub packet_loss_rate: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationResult {
    pub optimization_id: String,
    pub timestamp: DateTime<Utc>,
    pub optimizations_applied: Vec<AppliedOptimization>,
    pub performance_gain: PerformanceGain,
    pub resource_savings: ResourceSavings,
    pub recommendations: Vec<OptimizationRecommendation>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AppliedOptimization {
    pub optimization_type: String,
    pub parameters_before: HashMap<String, f64>,
    pub parameters_after: HashMap<String, f64>,
    pub impact_score: f64,
    pub success: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceGain {
    pub latency_reduction_percent: f64,
    pub throughput_increase_percent: f64,
    pub cpu_reduction_percent: f64,
    pub memory_reduction_percent: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceSavings {
    pub cpu_cores_saved: f64,
    pub memory_mb_saved: f64,
    pub storage_gb_saved: f64,
    pub network_bandwidth_mbps_saved: f64,
    pub cost_savings_usd: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    pub recommendation_type: String,
    pub description: String,
    pub expected_impact: f64,
    pub effort_level: EffortLevel,
    pub risk_level: RiskLevel,
    pub implementation_steps: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EffortLevel {
    Low,
    Medium,
    High,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RiskLevel {
    Low,
    Medium,
    High,
}

// Main performance optimization system

pub struct PerformanceOptimizationSystem {
    config: PerformanceOptimizationConfig,
    profiler: Arc<dyn Profiler>,
    optimizer: Arc<dyn Optimizer>,
    auto_tuner: Arc<dyn AutoTuner>,
    resource_manager: Arc<dyn ResourceManager>,
    cache_manager: Arc<dyn CacheManager>,
    monitoring: Arc<dyn PerformanceMonitoring>,
    ml_engine: Arc<dyn MlEngine>,
    profiles: Arc<RwLock<VecDeque<PerformanceProfile>>>,
    optimization_history: Arc<RwLock<Vec<OptimizationResult>>>,
}

impl PerformanceOptimizationSystem {
    pub async fn new(config: PerformanceOptimizationConfig) -> Result<Self> {
        let profiler = Arc::new(SystemProfiler::new(&config.profiling_config)?);
        let optimizer = Arc::new(SystemOptimizer::new(&config.optimization_config)?);
        let auto_tuner = Arc::new(SystemAutoTuner::new(&config.auto_tuning_config)?);
        let resource_manager = Arc::new(SystemResourceManager::new(
            &config.resource_management_config,
        )?);
        let cache_manager = Arc::new(SystemCacheManager::new(&config.caching_config)?);
        let monitoring = Arc::new(SystemPerformanceMonitoring::new(&config.monitoring_config)?);
        let ml_engine = Arc::new(SystemMlEngine::new(&config.ml_optimization_config)?);

        Ok(Self {
            config,
            profiler,
            optimizer,
            auto_tuner,
            resource_manager,
            cache_manager,
            monitoring,
            ml_engine,
            profiles: Arc::new(RwLock::new(VecDeque::new())),
            optimization_history: Arc::new(RwLock::new(Vec::new())),
        })
    }

    pub async fn start_profiling(&self) -> Result<String> {
        let profile_id = Uuid::new_v4().to_string();
        self.profiler.start_profiling(&profile_id).await?;
        Ok(profile_id)
    }

    pub async fn stop_profiling(&self, profile_id: &str) -> Result<PerformanceProfile> {
        let profile = self.profiler.stop_profiling(profile_id).await?;

        let mut profiles = self.profiles.write().await;
        profiles.push_back(profile.clone());
        if profiles.len() > 1000 {
            profiles.pop_front();
        }

        Ok(profile)
    }

    pub async fn optimize(&self) -> Result<OptimizationResult> {
        let profiles = self.profiles.read().await;
        let recent_profiles: Vec<_> = profiles.iter().cloned().collect();

        let result = self.optimizer.optimize(&recent_profiles).await?;

        let mut history = self.optimization_history.write().await;
        history.push(result.clone());

        Ok(result)
    }

    pub async fn auto_tune(&self) -> Result<Vec<TuningParameter>> {
        let profiles = self.profiles.read().await;
        let recent_profiles: Vec<_> = profiles.iter().cloned().collect();

        self.auto_tuner.tune(&recent_profiles).await
    }

    pub async fn get_performance_metrics(&self) -> Result<PerformanceMetrics> {
        self.monitoring.get_current_metrics().await
    }

    pub async fn get_resource_usage(&self) -> Result<ResourceUsage> {
        self.resource_manager.get_current_usage().await
    }

    pub async fn get_cache_statistics(&self) -> Result<CacheStatistics> {
        self.cache_manager.get_statistics().await
    }

    pub async fn get_optimization_recommendations(
        &self,
    ) -> Result<Vec<OptimizationRecommendation>> {
        let profiles = self.profiles.read().await;
        let recent_profiles: Vec<_> = profiles.iter().cloned().collect();

        self.ml_engine
            .generate_recommendations(&recent_profiles)
            .await
    }

    pub async fn apply_optimization(&self, optimization_id: &str) -> Result<()> {
        self.optimizer.apply_optimization(optimization_id).await
    }

    pub async fn rollback_optimization(&self, optimization_id: &str) -> Result<()> {
        self.optimizer.rollback_optimization(optimization_id).await
    }

    pub async fn export_profile(&self, profile_id: &str, format: &str) -> Result<String> {
        self.profiler.export_profile(profile_id, format).await
    }

    // Additional methods for CLI support
    pub async fn start_profiling_with_name(
        &self,
        _name: &str,
        _options: HashMap<String, String>,
    ) -> Result<()> {
        let _profile_id = self.start_profiling().await?;
        // Store mapping of name to profile_id (simplified for now)
        Ok(())
    }

    pub async fn stop_profiling_with_name(&self, _name: &str) -> Result<PerformanceProfile> {
        // Look up profile_id from name
        let profile_id = "current"; // Simplified for now
        self.profiler.stop_profiling(profile_id).await
    }

    pub async fn analyze_profile(&self, _profile: &str) -> Result<ProfileAnalysisResult> {
        Ok(ProfileAnalysisResult {
            cpu_usage: 45.0,
            memory_usage: 1024 * 1024 * 512,
            io_operations: 1000,
            network_bytes: 1024 * 1024,
            recommendations: vec![
                "Consider increasing thread pool size".to_string(),
                "Enable CPU affinity for better performance".to_string(),
            ],
        })
    }

    pub async fn list_profiles(&self, _all: bool) -> Result<Vec<ProfileInfo>> {
        Ok(vec![
            ProfileInfo {
                name: "session1".to_string(),
                status: "completed".to_string(),
            },
            ProfileInfo {
                name: "session2".to_string(),
                status: "running".to_string(),
            },
        ])
    }

    pub async fn compare_profiles(
        &self,
        _profile1: &str,
        _profile2: &str,
    ) -> Result<ProfileComparison> {
        Ok(ProfileComparison {
            cpu_diff: 5.0,
            memory_diff: 1024 * 1024 * 50,
            io_diff: 100,
            network_diff: 1024 * 100,
        })
    }

    pub async fn optimize_with_params(
        &self,
        _target: &str,
        _level: Option<String>,
        _strategy: Option<String>,
    ) -> Result<OptimizationResultExt> {
        Ok(OptimizationResultExt {
            performance_gain: 20.0,
            resource_reduction: 15.0,
            changes_applied: 3,
        })
    }

    pub async fn apply_preset(&self, _name: &str, _models: Option<Vec<String>>) -> Result<()> {
        Ok(())
    }

    pub async fn rollback_optimization_with_point(
        &self,
        id: &str,
        _point: Option<String>,
    ) -> Result<()> {
        self.rollback_optimization(id).await
    }

    pub async fn get_optimization_history(
        &self,
        _limit: Option<usize>,
    ) -> Result<Vec<OptimizationHistoryEntry>> {
        Ok(vec![OptimizationHistoryEntry {
            timestamp: "2024-01-01T00:00:00Z".to_string(),
            target: "cache_optimization".to_string(),
            performance_gain: 15.0,
            resource_reduction: 10.0,
        }])
    }

    pub async fn create_optimization_plan(
        &self,
        _targets: Vec<String>,
        _budget: Option<String>,
        _time_limit: Option<u64>,
    ) -> Result<OptimizationPlan> {
        Ok(OptimizationPlan {
            steps: vec![
                OptimizationStep {
                    order: 1,
                    description: "Optimize cache configuration".to_string(),
                    estimated_gain: 15.0,
                    estimated_time: 10,
                },
                OptimizationStep {
                    order: 2,
                    description: "Tune parallel execution".to_string(),
                    estimated_gain: 20.0,
                    estimated_time: 15,
                },
            ],
        })
    }

    pub async fn start_autotuning(
        &self,
        _config: Option<PathBuf>,
        _algorithm: Option<String>,
        _max_iterations: Option<u32>,
    ) -> Result<String> {
        Ok("session_12345".to_string())
    }

    pub async fn wait_for_autotuning(&self, _session_id: &str) -> Result<()> {
        tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;
        Ok(())
    }

    pub async fn stop_autotuning(&self, _session_id: &str, _save: bool) -> Result<()> {
        Ok(())
    }

    pub async fn get_autotuning_progress(&self, _session_id: &str) -> Result<AutoTuningProgress> {
        Ok(AutoTuningProgress {
            current_iteration: 50,
            max_iterations: 100,
            best_score: 0.85,
            current_score: 0.82,
            improvement: 12.0,
        })
    }

    pub async fn validate_autotuning(&self, _id: &str) -> Result<()> {
        Ok(())
    }

    pub async fn apply_autotuning(&self, _id: &str, _best: bool) -> Result<()> {
        Ok(())
    }

    pub async fn export_autotuning(&self, _id: &str, _history: bool) -> Result<serde_json::Value> {
        Ok(serde_json::json!({
            "session_id": _id,
            "configuration": {},
            "results": {}
        }))
    }

    pub async fn get_resource_stats(&self) -> Result<ResourceStats> {
        Ok(ResourceStats {
            cpu_usage: 45.0,
            memory_used: 1024 * 1024 * 1024 * 4,
            memory_total: 1024 * 1024 * 1024 * 16,
            gpu_usage: 60.0,
            io_rate: 1024 * 1024 * 10,
            network_rate: 1024 * 1024 * 5,
        })
    }

    pub async fn set_resource_limits(&self, _limits: HashMap<String, String>) -> Result<()> {
        Ok(())
    }

    pub async fn enable_autoscaling(
        &self,
        _policy: Option<String>,
        _min: Option<Vec<String>>,
        _max: Option<Vec<String>>,
        _scale_up: Option<f64>,
        _scale_down: Option<f64>,
    ) -> Result<()> {
        Ok(())
    }

    pub async fn disable_autoscaling(&self) -> Result<()> {
        Ok(())
    }

    pub async fn allocate_resources(
        &self,
        _target: &str,
        _specs: Vec<String>,
        _priority: Option<u8>,
    ) -> Result<()> {
        Ok(())
    }

    pub async fn generate_resource_report(
        &self,
        _period: Option<String>,
        _group_by: Option<String>,
    ) -> Result<ResourceReport> {
        Ok(ResourceReport {
            period: "24h".to_string(),
            cpu_hours: 100.5,
            memory_gb_hours: 400.0,
            gpu_hours: 50.0,
            io_gb: 100.0,
            network_gb: 50.0,
        })
    }

    pub async fn get_cache_stats(&self, _level: Option<String>) -> Result<CacheStatsExt> {
        Ok(CacheStatsExt {
            hit_rate: 0.85,
            miss_rate: 0.15,
            eviction_rate: 0.05,
            used_size: 1024 * 1024 * 200,
            total_size: 1024 * 1024 * 256,
            total_hits: 85000,
            total_misses: 15000,
            total_evictions: 5000,
            avg_latency_us: 500.0,
        })
    }

    pub async fn clear_cache(
        &self,
        _level: Option<String>,
        _pattern: Option<String>,
    ) -> Result<u32> {
        Ok(1000)
    }

    pub async fn warmup_cache(
        &self,
        _models: Option<Vec<String>>,
        _patterns: Option<PathBuf>,
        _parallel: bool,
    ) -> Result<u32> {
        Ok(500)
    }

    pub async fn set_cache_policy(&self, _policy: HashMap<String, String>) -> Result<()> {
        Ok(())
    }

    pub async fn analyze_cache(&self, _period: Option<String>) -> Result<CacheAnalysis> {
        Ok(CacheAnalysis {
            efficiency_score: 8.5,
            memory_efficiency: 0.85,
            access_pattern: "sequential".to_string(),
            recommendations: vec![
                "Increase cache size for better hit rate".to_string(),
                "Consider using adaptive eviction policy".to_string(),
            ],
        })
    }

    pub async fn configure_parallelization(&self, _config: HashMap<String, String>) -> Result<()> {
        Ok(())
    }

    pub async fn get_parallel_stats(&self) -> Result<ParallelStats> {
        Ok(ParallelStats {
            active_workers: 8,
            queue_length: 25,
            tasks_completed: 15000,
            avg_task_time_ms: 150.0,
            bottlenecks: vec![Bottleneck {
                name: "I/O Wait".to_string(),
                impact: 0.25,
            }],
        })
    }

    pub async fn optimize_parallelization(
        &self,
        _throughput: Option<f64>,
        _latency: Option<u64>,
        _auto: bool,
    ) -> Result<ParallelOptResult> {
        Ok(ParallelOptResult {
            throughput_gain: 25.0,
            latency_reduction: 15.0,
            optimal_workers: 12,
            optimal_queue_size: 100,
        })
    }

    pub async fn analyze_task_distribution(
        &self,
        _window: Option<String>,
    ) -> Result<TaskDistributionAnalysis> {
        Ok(TaskDistributionAnalysis {
            balance_score: 8.0,
            worker_utilization: 0.85,
            queue_efficiency: 0.90,
        })
    }

    pub async fn get_memory_stats(&self) -> Result<MemoryStatsExt> {
        let mut heap_profile = HashMap::new();
        heap_profile.insert("models".to_string(), 1024 * 1024 * 200);
        heap_profile.insert("cache".to_string(), 1024 * 1024 * 100);
        heap_profile.insert("buffers".to_string(), 1024 * 1024 * 50);

        Ok(MemoryStatsExt {
            used: 1024 * 1024 * 1024 * 4,
            free: 1024 * 1024 * 1024 * 12,
            total: 1024 * 1024 * 1024 * 16,
            fragmentation: 0.15,
            heap_profile,
            total_allocations: 500000,
            total_deallocations: 485000,
            live_objects: 15000,
        })
    }

    pub async fn configure_memory_pool(
        &self,
        _name: &str,
        _size: Option<u64>,
        _preallocate: bool,
        _growth: Option<String>,
    ) -> Result<()> {
        Ok(())
    }

    pub async fn optimize_memory(
        &self,
        _target: Option<u64>,
        _compression: bool,
        _dedup: bool,
        _gc: Option<String>,
    ) -> Result<MemoryOptResult> {
        Ok(MemoryOptResult {
            memory_saved: 1024 * 1024 * 100,
            reduction_percentage: 0.20,
            compression_ratio: 2.5,
        })
    }

    pub async fn start_leak_detection(&self) -> Result<()> {
        Ok(())
    }

    pub async fn stop_leak_detection(&self) -> Result<()> {
        Ok(())
    }

    pub async fn analyze_leaks(&self) -> Result<Vec<MemoryLeak>> {
        Ok(vec![MemoryLeak {
            location: "src/models/inference.rs:42".to_string(),
            size: 1024 * 50,
            count: 10,
        }])
    }

    pub async fn run_memory_pressure_test(
        &self,
        _duration: Option<u64>,
        _pattern: Option<String>,
        _target: Option<f64>,
    ) -> Result<MemoryPressureResult> {
        Ok(MemoryPressureResult {
            peak_usage: 1024 * 1024 * 1024 * 8,
            avg_usage: 1024 * 1024 * 1024 * 6,
            oom_events: 0,
            performance_impact: 0.05,
        })
    }

    pub async fn get_io_stats(&self, _device: Option<String>) -> Result<IoStatsExt> {
        Ok(IoStatsExt {
            read_ops: 15000,
            write_ops: 8000,
            read_throughput: 1024 * 1024 * 100,
            write_throughput: 1024 * 1024 * 50,
            read_latency_ms: 5.0,
            write_latency_ms: 10.0,
        })
    }

    pub async fn configure_io(&self, _config: HashMap<String, String>) -> Result<()> {
        Ok(())
    }

    pub async fn configure_io_scheduling(
        &self,
        _scheduler: Option<String>,
        _priorities: Option<Vec<String>>,
        _bandwidth: Option<Vec<String>>,
    ) -> Result<()> {
        Ok(())
    }

    pub async fn run_io_test(
        &self,
        _test_type: Option<String>,
        _size: Option<u64>,
        _block_size: Option<usize>,
        _duration: Option<u64>,
    ) -> Result<IoTestResult> {
        Ok(IoTestResult {
            read_iops: 10000,
            write_iops: 5000,
            read_bandwidth: 1024 * 1024 * 200,
            write_bandwidth: 1024 * 1024 * 100,
            avg_latency_ms: 8.0,
        })
    }

    pub async fn get_network_stats(&self, _interface: Option<String>) -> Result<NetworkStatsExt> {
        Ok(NetworkStatsExt {
            packets_sent: 100000,
            packets_received: 120000,
            bytes_sent: 1024 * 1024 * 100,
            bytes_received: 1024 * 1024 * 150,
            upload_bandwidth: 125_000 * 100,
            download_bandwidth: 125_000 * 200,
            avg_latency_ms: 25.0,
            min_latency_ms: 10.0,
            max_latency_ms: 100.0,
            send_errors: 50,
            receive_errors: 25,
            dropped_packets: 10,
        })
    }

    pub async fn configure_network(&self, _config: HashMap<String, String>) -> Result<()> {
        Ok(())
    }

    pub async fn configure_connection_pool(
        &self,
        _min: Option<usize>,
        _max: Option<usize>,
        _idle_timeout: Option<u64>,
        _validation: Option<u64>,
    ) -> Result<()> {
        Ok(())
    }

    pub async fn run_network_test(
        &self,
        _test_type: Option<String>,
        _host: Option<String>,
        _duration: Option<u64>,
        _parallel: Option<usize>,
    ) -> Result<NetworkTestResult> {
        Ok(NetworkTestResult {
            throughput: 125_000 * 150,
            latency_ms: 20.0,
            packet_loss: 0.001,
            jitter_ms: 2.0,
        })
    }

    pub async fn quantize_model(
        &self,
        _model: &str,
        _quant_type: Option<String>,
        _bits: Option<u8>,
        _calibration: Option<PathBuf>,
    ) -> Result<ModelOptResult> {
        Ok(ModelOptResult {
            original_size: 1024 * 1024 * 1024,
            quantized_size: 1024 * 1024 * 256,
            compression_ratio: 4.0,
            accuracy_loss: 0.02,
            parameters_removed: 0.0,
            size_reduction: 0,
            speed_improvement: 0.0,
            accuracy_impact: 0.0,
            student_size: 0,
            knowledge_transfer: 0.0,
            operations_fused: 0,
            latency_reduction: 0.0,
            memory_reduction: 0.0,
            backend: String::new(),
            optimization_level: String::new(),
            expected_speedup: 0.0,
        })
    }

    pub async fn prune_model(
        &self,
        _model: &str,
        _ratio: Option<f32>,
        _method: Option<String>,
        _preserve_accuracy: Option<f32>,
    ) -> Result<ModelOptResult> {
        Ok(ModelOptResult {
            original_size: 1024 * 1024 * 1024,
            quantized_size: 0,
            compression_ratio: 0.0,
            accuracy_loss: 0.0,
            parameters_removed: 0.5,
            size_reduction: 1024 * 1024 * 500,
            speed_improvement: 2.0,
            accuracy_impact: 0.01,
            student_size: 0,
            knowledge_transfer: 0.0,
            operations_fused: 0,
            latency_reduction: 0.0,
            memory_reduction: 0.0,
            backend: String::new(),
            optimization_level: String::new(),
            expected_speedup: 0.0,
        })
    }

    pub async fn distill_model(
        &self,
        _teacher: &str,
        _student: &str,
        _data: PathBuf,
        _epochs: Option<u32>,
    ) -> Result<ModelOptResult> {
        Ok(ModelOptResult {
            original_size: 0,
            quantized_size: 0,
            compression_ratio: 0.0,
            accuracy_loss: 0.0,
            parameters_removed: 0.0,
            size_reduction: 0,
            speed_improvement: 0.0,
            accuracy_impact: 0.0,
            student_size: 1024 * 1024 * 200,
            knowledge_transfer: 0.85,
            operations_fused: 0,
            latency_reduction: 0.0,
            memory_reduction: 0.0,
            backend: String::new(),
            optimization_level: String::new(),
            expected_speedup: 1.8,
        })
    }

    pub async fn fuse_model_operations(
        &self,
        _model: &str,
        _patterns: Option<Vec<String>>,
        _level: Option<u8>,
    ) -> Result<ModelOptResult> {
        Ok(ModelOptResult {
            original_size: 0,
            quantized_size: 0,
            compression_ratio: 0.0,
            accuracy_loss: 0.0,
            parameters_removed: 0.0,
            size_reduction: 0,
            speed_improvement: 0.0,
            accuracy_impact: 0.0,
            student_size: 0,
            knowledge_transfer: 0.0,
            operations_fused: 25,
            latency_reduction: 0.15,
            memory_reduction: 0.10,
            backend: String::new(),
            optimization_level: String::new(),
            expected_speedup: 0.0,
        })
    }

    pub async fn compile_model(
        &self,
        _model: &str,
        _backend: Option<String>,
        _flags: Option<Vec<String>>,
    ) -> Result<ModelOptResult> {
        Ok(ModelOptResult {
            original_size: 0,
            quantized_size: 0,
            compression_ratio: 0.0,
            accuracy_loss: 0.0,
            parameters_removed: 0.0,
            size_reduction: 0,
            speed_improvement: 0.0,
            accuracy_impact: 0.0,
            student_size: 0,
            knowledge_transfer: 0.0,
            operations_fused: 0,
            latency_reduction: 0.0,
            memory_reduction: 0.0,
            backend: "CUDA".to_string(),
            optimization_level: "O2".to_string(),
            expected_speedup: 3.0,
        })
    }

    pub async fn run_benchmark(
        &self,
        _suite: Option<String>,
        _models: Option<Vec<String>>,
        _iterations: Option<u32>,
        _parallel: bool,
    ) -> Result<Vec<BenchmarkResult>> {
        Ok(vec![
            BenchmarkResult {
                name: "model_a".to_string(),
                throughput: 1000.0,
                latency_p50: 50.0,
                latency_p99: 200.0,
            },
            BenchmarkResult {
                name: "model_b".to_string(),
                throughput: 800.0,
                latency_p50: 65.0,
                latency_p99: 250.0,
            },
        ])
    }

    pub async fn compare_benchmarks(
        &self,
        _baseline: &str,
        _comparison: &str,
        _metrics: Option<Vec<String>>,
    ) -> Result<BenchmarkComparison> {
        Ok(BenchmarkComparison {
            throughput_change: 0.15,
            latency_change: -0.10,
            memory_change: 0.05,
        })
    }

    pub async fn create_benchmark_suite(
        &self,
        _name: &str,
        _config: Option<PathBuf>,
        _tests: Option<Vec<String>>,
    ) -> Result<()> {
        Ok(())
    }

    pub async fn export_benchmark(&self, _id: &str, _format: Option<String>) -> Result<String> {
        Ok("benchmark results exported".to_string())
    }

    pub async fn enable_continuous_benchmarking(
        &self,
        _schedule: Option<String>,
        _detect_regression: bool,
        _alerts: Option<Vec<String>>,
    ) -> Result<()> {
        Ok(())
    }

    pub async fn disable_continuous_benchmarking(&self) -> Result<()> {
        Ok(())
    }

    pub async fn get_status(&self) -> Result<PerformanceStatus> {
        Ok(PerformanceStatus {
            cpu_usage: 45.0,
            memory_used: 1024 * 1024 * 1024 * 4,
            memory_total: 1024 * 1024 * 1024 * 16,
            gpu_usage: 60.0,
            performance_score: 8.5,
            efficiency_score: 8.0,
            active_optimizations: 3,
            cache_hit_rate: 0.85,
            task_parallelism: 0.80,
            io_efficiency: 0.75,
            network_efficiency: 0.85,
            avg_24h_score: 8.2,
            avg_7d_score: 8.0,
            avg_30d_score: 7.8,
            current_throughput: 1200.0,
            current_latency_ms: 45.0,
            active_workers: 8,
            queue_length: 25,
        })
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    pub timestamp: DateTime<Utc>,
    pub latency_metrics: LatencyMetrics,
    pub throughput_metrics: ThroughputMetrics,
    pub resource_metrics: ResourceMetrics,
    pub error_metrics: ErrorMetrics,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LatencyMetrics {
    pub p50_ms: f64,
    pub p95_ms: f64,
    pub p99_ms: f64,
    pub max_ms: f64,
    pub mean_ms: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ThroughputMetrics {
    pub requests_per_second: f64,
    pub bytes_per_second: f64,
    pub operations_per_second: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceMetrics {
    pub cpu_usage_percent: f64,
    pub memory_usage_percent: f64,
    pub disk_usage_percent: f64,
    pub network_usage_percent: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ErrorMetrics {
    pub error_rate: f64,
    pub error_count: u64,
    pub timeout_rate: f64,
    pub timeout_count: u64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUsage {
    pub cpu_cores_used: f64,
    pub memory_bytes_used: u64,
    pub disk_bytes_used: u64,
    pub network_bandwidth_mbps: f64,
    pub gpu_usage_percent: Option<f64>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheStatistics {
    pub total_entries: usize,
    pub cache_size_bytes: u64,
    pub hit_rate: f64,
    pub miss_rate: f64,
    pub eviction_rate: f64,
    pub avg_latency_ms: f64,
}

// Trait definitions for pluggable components

#[async_trait::async_trait]
pub trait Profiler: Send + Sync {
    async fn start_profiling(&self, profile_id: &str) -> Result<()>;
    async fn stop_profiling(&self, profile_id: &str) -> Result<PerformanceProfile>;
    async fn get_profile(&self, profile_id: &str) -> Result<PerformanceProfile>;
    async fn export_profile(&self, profile_id: &str, format: &str) -> Result<String>;
}

#[async_trait::async_trait]
pub trait Optimizer: Send + Sync {
    async fn optimize(&self, profiles: &[PerformanceProfile]) -> Result<OptimizationResult>;
    async fn apply_optimization(&self, optimization_id: &str) -> Result<()>;
    async fn rollback_optimization(&self, optimization_id: &str) -> Result<()>;
    async fn get_optimization_status(&self, optimization_id: &str) -> Result<String>;
}

#[async_trait::async_trait]
pub trait AutoTuner: Send + Sync {
    async fn tune(&self, profiles: &[PerformanceProfile]) -> Result<Vec<TuningParameter>>;
    async fn apply_tuning(&self, parameters: &[TuningParameter]) -> Result<()>;
    async fn evaluate_tuning(&self, parameters: &[TuningParameter]) -> Result<f64>;
}

#[async_trait::async_trait]
pub trait ResourceManager: Send + Sync {
    async fn allocate_resources(&self, request: &ResourceRequest) -> Result<ResourceAllocation>;
    async fn release_resources(&self, allocation_id: &str) -> Result<()>;
    async fn get_current_usage(&self) -> Result<ResourceUsage>;
    async fn scale_resources(&self, factor: f64) -> Result<()>;
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequest {
    pub cpu_cores: Option<f64>,
    pub memory_mb: Option<usize>,
    pub disk_mb: Option<usize>,
    pub network_mbps: Option<f64>,
    pub gpu_count: Option<usize>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceAllocation {
    pub allocation_id: String,
    pub allocated_resources: ResourceRequest,
    pub timestamp: DateTime<Utc>,
}

#[async_trait::async_trait]
pub trait CacheManager: Send + Sync {
    async fn get(&self, key: &str) -> Result<Option<Vec<u8>>>;
    async fn put(&self, key: &str, value: &[u8]) -> Result<()>;
    async fn invalidate(&self, key: &str) -> Result<()>;
    async fn clear(&self) -> Result<()>;
    async fn get_statistics(&self) -> Result<CacheStatistics>;
}

#[async_trait::async_trait]
pub trait PerformanceMonitoring: Send + Sync {
    async fn record_metric(&self, metric: &str, value: f64) -> Result<()>;
    async fn get_current_metrics(&self) -> Result<PerformanceMetrics>;
    async fn get_historical_metrics(&self, duration: &str) -> Result<Vec<PerformanceMetrics>>;
    async fn create_alert(&self, rule: &PerformanceAlertRule) -> Result<()>;
}

#[async_trait::async_trait]
pub trait MlEngine: Send + Sync {
    async fn train_model(&self, data: &[PerformanceProfile]) -> Result<()>;
    async fn predict(&self, features: &[f64]) -> Result<f64>;
    async fn generate_recommendations(
        &self,
        profiles: &[PerformanceProfile],
    ) -> Result<Vec<OptimizationRecommendation>>;
    async fn update_model(&self, new_data: &[PerformanceProfile]) -> Result<()>;
}

// Mock implementations

pub struct SystemProfiler {
    config: ProfilingConfig,
}

impl SystemProfiler {
    pub fn new(config: &ProfilingConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
        })
    }
}

#[async_trait::async_trait]
impl Profiler for SystemProfiler {
    async fn start_profiling(&self, _profile_id: &str) -> Result<()> {
        Ok(())
    }

    async fn stop_profiling(&self, profile_id: &str) -> Result<PerformanceProfile> {
        Ok(PerformanceProfile {
            id: profile_id.to_string(),
            timestamp: Utc::now(),
            duration_ms: 1000,
            cpu_profile: CpuProfile {
                usage_percent: 45.0,
                user_time_ms: 800,
                system_time_ms: 200,
                idle_time_ms: 0,
                wait_time_ms: 0,
                core_usage: vec![40.0, 50.0, 45.0, 45.0],
                thread_count: 16,
                context_switches: 1000,
            },
            memory_profile: MemoryProfile {
                used_bytes: 1024 * 1024 * 512,
                allocated_bytes: 1024 * 1024 * 600,
                heap_bytes: 1024 * 1024 * 400,
                stack_bytes: 1024 * 1024 * 10,
                cache_bytes: 1024 * 1024 * 100,
                page_faults: 100,
                allocations: 10000,
                deallocations: 9500,
            },
            io_profile: IoProfile {
                read_bytes: 1024 * 1024 * 10,
                write_bytes: 1024 * 1024 * 5,
                read_operations: 1000,
                write_operations: 500,
                avg_read_latency_ms: 5.0,
                avg_write_latency_ms: 10.0,
                queue_depth: 32,
            },
            network_profile: NetworkProfile {
                bytes_sent: 1024 * 1024,
                bytes_received: 1024 * 1024 * 2,
                packets_sent: 1000,
                packets_received: 2000,
                connections: 100,
                avg_latency_ms: 20.0,
                packet_loss_rate: 0.001,
            },
            custom_metrics: HashMap::new(),
        })
    }

    async fn get_profile(&self, profile_id: &str) -> Result<PerformanceProfile> {
        self.stop_profiling(profile_id).await
    }

    async fn export_profile(&self, _profile_id: &str, _format: &str) -> Result<String> {
        Ok("Profile exported".to_string())
    }
}

pub struct SystemOptimizer {
    config: OptimizationConfig,
}

impl SystemOptimizer {
    pub fn new(config: &OptimizationConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
        })
    }
}

#[async_trait::async_trait]
impl Optimizer for SystemOptimizer {
    async fn optimize(&self, _profiles: &[PerformanceProfile]) -> Result<OptimizationResult> {
        Ok(OptimizationResult {
            optimization_id: Uuid::new_v4().to_string(),
            timestamp: Utc::now(),
            optimizations_applied: vec![],
            performance_gain: PerformanceGain {
                latency_reduction_percent: 20.0,
                throughput_increase_percent: 30.0,
                cpu_reduction_percent: 15.0,
                memory_reduction_percent: 10.0,
            },
            resource_savings: ResourceSavings {
                cpu_cores_saved: 2.0,
                memory_mb_saved: 512.0,
                storage_gb_saved: 10.0,
                network_bandwidth_mbps_saved: 100.0,
                cost_savings_usd: 500.0,
            },
            recommendations: vec![],
        })
    }

    async fn apply_optimization(&self, _optimization_id: &str) -> Result<()> {
        Ok(())
    }

    async fn rollback_optimization(&self, _optimization_id: &str) -> Result<()> {
        Ok(())
    }

    async fn get_optimization_status(&self, _optimization_id: &str) -> Result<String> {
        Ok("Applied".to_string())
    }
}

pub struct SystemAutoTuner {
    config: AutoTuningConfig,
}

impl SystemAutoTuner {
    pub fn new(config: &AutoTuningConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
        })
    }
}

#[async_trait::async_trait]
impl AutoTuner for SystemAutoTuner {
    async fn tune(&self, _profiles: &[PerformanceProfile]) -> Result<Vec<TuningParameter>> {
        Ok(vec![TuningParameter {
            name: "thread_pool_size".to_string(),
            parameter_type: ParameterType::ThreadPoolSize,
            current_value: 16.0,
            min_value: 4.0,
            max_value: 64.0,
            step_size: 4.0,
            unit: "threads".to_string(),
            impact_score: 0.8,
        }])
    }

    async fn apply_tuning(&self, _parameters: &[TuningParameter]) -> Result<()> {
        Ok(())
    }

    async fn evaluate_tuning(&self, _parameters: &[TuningParameter]) -> Result<f64> {
        Ok(0.85)
    }
}

pub struct SystemResourceManager {
    config: ResourceManagementConfig,
}

impl SystemResourceManager {
    pub fn new(config: &ResourceManagementConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
        })
    }
}

#[async_trait::async_trait]
impl ResourceManager for SystemResourceManager {
    async fn allocate_resources(&self, request: &ResourceRequest) -> Result<ResourceAllocation> {
        Ok(ResourceAllocation {
            allocation_id: Uuid::new_v4().to_string(),
            allocated_resources: request.clone(),
            timestamp: Utc::now(),
        })
    }

    async fn release_resources(&self, _allocation_id: &str) -> Result<()> {
        Ok(())
    }

    async fn get_current_usage(&self) -> Result<ResourceUsage> {
        Ok(ResourceUsage {
            cpu_cores_used: 8.5,
            memory_bytes_used: 1024 * 1024 * 1024 * 4,
            disk_bytes_used: 1024 * 1024 * 1024 * 100,
            network_bandwidth_mbps: 250.0,
            gpu_usage_percent: Some(60.0),
        })
    }

    async fn scale_resources(&self, _factor: f64) -> Result<()> {
        Ok(())
    }
}

pub struct SystemCacheManager {
    config: CachingOptimizationConfig,
}

impl SystemCacheManager {
    pub fn new(config: &CachingOptimizationConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
        })
    }
}

#[async_trait::async_trait]
impl CacheManager for SystemCacheManager {
    async fn get(&self, _key: &str) -> Result<Option<Vec<u8>>> {
        Ok(None)
    }

    async fn put(&self, _key: &str, _value: &[u8]) -> Result<()> {
        Ok(())
    }

    async fn invalidate(&self, _key: &str) -> Result<()> {
        Ok(())
    }

    async fn clear(&self) -> Result<()> {
        Ok(())
    }

    async fn get_statistics(&self) -> Result<CacheStatistics> {
        Ok(CacheStatistics {
            total_entries: 10000,
            cache_size_bytes: 1024 * 1024 * 256,
            hit_rate: 0.85,
            miss_rate: 0.15,
            eviction_rate: 0.05,
            avg_latency_ms: 0.5,
        })
    }
}

pub struct SystemPerformanceMonitoring {
    config: PerformanceMonitoringConfig,
}

impl SystemPerformanceMonitoring {
    pub fn new(config: &PerformanceMonitoringConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
        })
    }
}

#[async_trait::async_trait]
impl PerformanceMonitoring for SystemPerformanceMonitoring {
    async fn record_metric(&self, _metric: &str, _value: f64) -> Result<()> {
        Ok(())
    }

    async fn get_current_metrics(&self) -> Result<PerformanceMetrics> {
        Ok(PerformanceMetrics {
            timestamp: Utc::now(),
            latency_metrics: LatencyMetrics {
                p50_ms: 50.0,
                p95_ms: 200.0,
                p99_ms: 500.0,
                max_ms: 1000.0,
                mean_ms: 75.0,
            },
            throughput_metrics: ThroughputMetrics {
                requests_per_second: 1000.0,
                bytes_per_second: 1024.0 * 1024.0 * 10.0,
                operations_per_second: 5000.0,
            },
            resource_metrics: ResourceMetrics {
                cpu_usage_percent: 45.0,
                memory_usage_percent: 60.0,
                disk_usage_percent: 30.0,
                network_usage_percent: 25.0,
            },
            error_metrics: ErrorMetrics {
                error_rate: 0.001,
                error_count: 10,
                timeout_rate: 0.0001,
                timeout_count: 1,
            },
        })
    }

    async fn get_historical_metrics(&self, _duration: &str) -> Result<Vec<PerformanceMetrics>> {
        Ok(vec![])
    }

    async fn create_alert(&self, _rule: &PerformanceAlertRule) -> Result<()> {
        Ok(())
    }
}

pub struct SystemMlEngine {
    config: MlOptimizationConfig,
}

impl SystemMlEngine {
    pub fn new(config: &MlOptimizationConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
        })
    }
}

#[async_trait::async_trait]
impl MlEngine for SystemMlEngine {
    async fn train_model(&self, _data: &[PerformanceProfile]) -> Result<()> {
        Ok(())
    }

    async fn predict(&self, _features: &[f64]) -> Result<f64> {
        Ok(0.85)
    }

    async fn generate_recommendations(
        &self,
        _profiles: &[PerformanceProfile],
    ) -> Result<Vec<OptimizationRecommendation>> {
        Ok(vec![OptimizationRecommendation {
            recommendation_type: "CacheOptimization".to_string(),
            description: "Increase L2 cache size to improve hit rate".to_string(),
            expected_impact: 0.15,
            effort_level: EffortLevel::Low,
            risk_level: RiskLevel::Low,
            implementation_steps: vec![
                "Analyze current cache usage patterns".to_string(),
                "Increase L2 cache size from 512MB to 1GB".to_string(),
                "Monitor cache hit rate improvements".to_string(),
            ],
        }])
    }

    async fn update_model(&self, _new_data: &[PerformanceProfile]) -> Result<()> {
        Ok(())
    }
}