torsh-backend 0.1.2

//! Resource Management Module for CUDA Optimization Execution Engine
//!
//! This module provides comprehensive resource management capabilities including
//! GPU memory allocation, CPU resource management, hardware resource tracking,
//! resource pools, allocation strategies, performance monitoring, and optimization
//! of resource utilization across the entire CUDA execution environment.

use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, VecDeque};
use std::sync::{
    atomic::{AtomicU64, AtomicUsize, Ordering},
    Arc, Mutex,
};
use std::time::{Duration, Instant};

/// Helper function for serde default Instant value
fn default_instant() -> Instant {
    Instant::now()
}

/// Helper function for serde default Option<Instant> value
fn default_option_instant() -> Option<Instant> {
    None
}

use super::config::{
    CpuAllocationConfig, GpuAllocationConfig, MemoryAllocationConfig, ResourceAllocationStrategy,
};
use super::task_management::{HardwareRequirement, ResourceRequirements, TaskId};

/// Comprehensive resource manager for CUDA optimization execution
///
/// Manages all system resources including GPU memory, CPU resources, hardware
/// resources, and provides advanced allocation strategies, monitoring, and optimization.
#[derive(Debug)]
pub struct OptimizationResourceManager {
    /// GPU resource management
    gpu_manager: Arc<Mutex<GpuResourceManager>>,

    /// CPU resource management
    cpu_manager: Arc<Mutex<CpuResourceManager>>,

    /// Memory resource management
    memory_manager: Arc<Mutex<MemoryResourceManager>>,

    /// Hardware resource tracking
    hardware_manager: Arc<Mutex<HardwareResourceManager>>,

    /// Resource allocation engine
    allocation_engine: Arc<Mutex<ResourceAllocationEngine>>,

    /// Resource monitoring system
    monitoring_system: Arc<Mutex<ResourceMonitoringSystem>>,

    /// Resource pools management
    pool_manager: Arc<Mutex<ResourcePoolManager>>,

    /// Resource scheduling system
    scheduler: Arc<Mutex<ResourceScheduler>>,

    /// Resource optimization engine
    optimization_engine: Arc<Mutex<ResourceOptimizationEngine>>,

    /// Performance tracking
    performance_tracker: Arc<Mutex<ResourcePerformanceTracker>>,

    /// Resource statistics
    statistics: Arc<Mutex<ResourceStatistics>>,

    /// Configuration
    config: ResourceManagementConfig,
}

/// Resource management configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceManagementConfig {
    /// Memory allocation configuration
    pub memory_config: MemoryAllocationConfig,

    /// CPU allocation configuration
    pub cpu_config: CpuAllocationConfig,

    /// GPU allocation configuration
    pub gpu_config: GpuAllocationConfig,

    /// Resource allocation strategy
    pub allocation_strategy: ResourceAllocationStrategy,

    /// Resource monitoring configuration
    pub monitoring_config: ResourceMonitoringConfig,

    /// Resource optimization settings
    pub optimization_config: ResourceOptimizationConfig,

    /// Resource pooling configuration
    pub pooling_config: ResourcePoolingConfig,

    /// Advanced resource settings
    pub advanced_config: AdvancedResourceConfig,
}

/// GPU resource manager for CUDA memory optimization
#[derive(Debug)]
pub struct GpuResourceManager {
    /// Available GPU devices
    available_devices: Vec<GpuDevice>,

    /// Current GPU allocations
    current_allocations: HashMap<TaskId, Vec<GpuAllocation>>,

    /// GPU memory pools
    memory_pools: HashMap<GpuDeviceId, GpuMemoryPool>,

    /// GPU utilization tracking
    utilization_tracker: GpuUtilizationTracker,

    /// GPU performance metrics
    performance_metrics: GpuPerformanceMetrics,

    /// GPU allocation history
    allocation_history: VecDeque<GpuAllocationRecord>,

    /// GPU resource limits
    resource_limits: GpuResourceLimits,

    /// GPU thermal management
    thermal_manager: GpuThermalManager,
}

/// CPU resource manager for compute resources
#[derive(Debug)]
pub struct CpuResourceManager {
    /// CPU core allocation tracking
    core_allocations: HashMap<TaskId, Vec<CpuCoreAllocation>>,

    /// CPU thread pool management
    thread_pools: HashMap<String, CpuThreadPool>,

    /// CPU utilization monitoring
    utilization_monitor: CpuUtilizationMonitor,

    /// CPU performance tracking
    performance_tracker: CpuPerformanceTracker,

    /// NUMA topology management
    numa_manager: NumaTopologyManager,

    /// CPU affinity management
    affinity_manager: CpuAffinityManager,

    /// CPU resource limits
    resource_limits: CpuResourceLimits,
}

/// Memory resource manager for system and GPU memory
#[derive(Debug)]
pub struct MemoryResourceManager {
    /// System memory allocations
    system_allocations: HashMap<TaskId, Vec<SystemMemoryAllocation>>,

    /// GPU memory allocations
    gpu_allocations: HashMap<TaskId, Vec<GpuMemoryAllocation>>,

    /// Memory pools for different types
    memory_pools: HashMap<MemoryPoolType, MemoryPool>,

    /// Memory utilization tracking
    utilization_tracker: MemoryUtilizationTracker,

    /// Memory fragmentation monitoring
    fragmentation_monitor: MemoryFragmentationMonitor,

    /// Memory optimization engine
    optimization_engine: MemoryOptimizationEngine,

    /// Memory pressure management
    pressure_manager: MemoryPressureManager,

    /// Memory allocation statistics
    allocation_stats: MemoryAllocationStatistics,
}

/// Hardware resource manager for specialized hardware
#[derive(Debug)]
pub struct HardwareResourceManager {
    /// Available hardware inventory
    hardware_inventory: HardwareInventory,

    /// Hardware resource allocations
    hardware_allocations: HashMap<TaskId, Vec<HardwareAllocation>>,

    /// Hardware capability tracking
    capability_tracker: HardwareCapabilityTracker,

    /// Hardware health monitoring
    health_monitor: HardwareHealthMonitor,

    /// Hardware performance metrics
    performance_metrics: HardwarePerformanceMetrics,

    /// Specialized accelerator management
    accelerator_manager: AcceleratorManager,
}

/// Resource allocation engine with advanced strategies
#[derive(Debug)]
pub struct ResourceAllocationEngine {
    /// Current allocation strategy
    allocation_strategy: ResourceAllocationStrategy,

    /// Allocation decision engine
    decision_engine: AllocationDecisionEngine,

    /// Resource reservation system
    reservation_system: ResourceReservationSystem,

    /// Allocation optimization algorithms
    optimization_algorithms: HashMap<String, Box<dyn AllocationAlgorithm>>,

    /// Machine learning predictor for allocation
    ml_predictor: Option<AllocationMLPredictor>,

    /// Allocation policy enforcement
    policy_enforcer: AllocationPolicyEnforcer,

    /// Resource conflict resolver
    conflict_resolver: ResourceConflictResolver,
}

/// Resource monitoring system for comprehensive tracking
#[derive(Debug)]
pub struct ResourceMonitoringSystem {
    /// Real-time monitoring agents
    monitoring_agents: HashMap<MonitoringTarget, MonitoringAgent>,

    /// Metrics collection system
    metrics_collector: MetricsCollector,

    /// Alert system for resource issues
    alert_system: ResourceAlertSystem,

    /// Performance dashboard data
    dashboard_data: DashboardData,

    /// Historical data storage
    historical_storage: HistoricalDataStorage,

    /// Anomaly detection system
    anomaly_detector: ResourceAnomalyDetector,
}

/// Resource pool manager for efficient resource reuse
#[derive(Debug)]
pub struct ResourcePoolManager {
    /// GPU memory pools
    gpu_memory_pools: HashMap<GpuDeviceId, GpuMemoryPool>,

    /// CPU thread pools
    cpu_thread_pools: HashMap<String, CpuThreadPool>,

    /// System memory pools
    system_memory_pools: HashMap<MemoryPoolType, MemoryPool>,

    /// Hardware resource pools
    hardware_pools: HashMap<HardwareType, HardwareResourcePool>,

    /// Pool optimization engine
    pool_optimizer: ResourcePoolOptimizer,

    /// Pool utilization tracker
    utilization_tracker: PoolUtilizationTracker,
}

/// Resource scheduler for coordinated resource allocation
#[derive(Debug)]
pub struct ResourceScheduler {
    /// Scheduling queue for resource requests
    scheduling_queue: VecDeque<ResourceRequest>,

    /// Scheduling algorithms
    scheduling_algorithms: HashMap<String, Box<dyn SchedulingAlgorithm>>,

    /// Current scheduling strategy
    current_strategy: SchedulingStrategy,

    /// Resource conflict detection
    conflict_detector: ResourceConflictDetector,

    /// Scheduling optimization engine
    optimization_engine: SchedulingOptimizationEngine,

    /// Load balancing system
    load_balancer: ResourceLoadBalancer,
}

// === Core Resource Types ===

/// GPU device representation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuDevice {
    /// Device unique identifier
    pub device_id: GpuDeviceId,

    /// Device name and model
    pub name: String,
    pub model: String,

    /// Compute capability
    pub compute_capability: (u32, u32),

    /// Total memory in bytes
    pub total_memory_bytes: usize,

    /// Available memory in bytes (simplified for serialization)
    pub available_memory_bytes: usize,

    /// Memory bandwidth in GB/s
    pub memory_bandwidth_gbps: f64,

    /// Core specifications
    pub cuda_cores: u32,
    pub rt_cores: Option<u32>,
    pub tensor_cores: Option<u32>,

    /// Clock speeds in MHz
    pub base_clock_mhz: u32,
    pub boost_clock_mhz: u32,
    pub memory_clock_mhz: u32,

    /// Power specifications
    pub max_power_watts: f64,
    pub current_power_watts: u64,

    /// Temperature monitoring
    pub temperature_celsius: u64,
    pub max_temperature_celsius: f64,

    /// Device capabilities
    pub capabilities: GpuCapabilities,

    /// Device status
    pub status: GpuDeviceStatus,

    /// Last updated timestamp
    #[serde(skip, default = "default_instant")]
    pub last_updated: Instant,
}

/// GPU device identifier
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub struct GpuDeviceId(pub u32);

/// GPU allocation record
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuAllocation {
    /// Allocation identifier
    pub allocation_id: AllocationId,

    /// Task that owns this allocation
    pub task_id: TaskId,

    /// GPU device ID
    pub device_id: GpuDeviceId,

    /// Memory allocation details
    pub memory_allocation: GpuMemoryAllocation,

    /// Compute resource allocation
    pub compute_allocation: GpuComputeAllocation,

    /// Allocation timestamp
    #[serde(skip, default = "default_instant")]
    pub allocated_at: Instant,

    /// Expected release time
    #[serde(skip, default = "default_option_instant")]
    pub expected_release: Option<Instant>,

    /// Allocation status
    pub status: AllocationStatus,

    /// Performance metrics
    pub performance_metrics: AllocationPerformanceMetrics,
}

/// GPU memory allocation details
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuMemoryAllocation {
    /// Memory address range
    pub address_range: MemoryAddressRange,

    /// Allocated size in bytes
    pub size_bytes: usize,

    /// Memory type (global, shared, etc.)
    pub memory_type: GpuMemoryType,

    /// Memory access pattern
    pub access_pattern: MemoryAccessPattern,

    /// Memory bandwidth utilization
    pub bandwidth_utilization: f64,

    /// Memory fragmentation info
    pub fragmentation_info: MemoryFragmentationInfo,
}

/// GPU compute resource allocation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuComputeAllocation {
    /// Streaming multiprocessors allocated
    pub sm_allocation: SmAllocation,

    /// Cuda cores allocated
    pub cuda_cores_allocated: u32,

    /// Tensor cores allocated (if available)
    pub tensor_cores_allocated: Option<u32>,

    /// Compute capability utilized
    pub compute_capability_used: (u32, u32),

    /// Clock frequency allocation
    pub clock_allocation: ClockAllocation,

    /// Power allocation
    pub power_allocation: PowerAllocation,
}

/// CPU core allocation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CpuCoreAllocation {
    /// Core identifier
    pub core_id: CpuCoreId,

    /// Task that owns this allocation
    pub task_id: TaskId,

    /// Core utilization percentage
    pub utilization_percentage: f64,

    /// NUMA node affinity
    pub numa_node: Option<NumaNodeId>,

    /// Thread allocation details
    pub thread_allocations: Vec<ThreadAllocation>,

    /// Allocation timestamp
    #[serde(skip, default = "default_instant")]
    pub allocated_at: Instant,

    /// Expected release time
    #[serde(skip, default = "default_option_instant")]
    pub expected_release: Option<Instant>,

    /// Performance metrics
    pub performance_metrics: CpuAllocationPerformanceMetrics,
}

/// System memory allocation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemMemoryAllocation {
    /// Allocation identifier
    pub allocation_id: AllocationId,

    /// Task that owns this allocation
    pub task_id: TaskId,

    /// Memory address range
    pub address_range: MemoryAddressRange,

    /// Allocated size in bytes
    pub size_bytes: usize,

    /// Memory pool source
    pub pool_source: MemoryPoolType,

    /// Memory protection flags
    pub protection_flags: MemoryProtectionFlags,

    /// NUMA locality
    pub numa_locality: NumaLocalityInfo,

    /// Allocation strategy used
    pub allocation_strategy: MemoryAllocationStrategy,

    /// Performance characteristics
    pub performance_characteristics: MemoryPerformanceCharacteristics,

    /// Allocation timestamp
    #[serde(skip, default = "default_instant")]
    pub allocated_at: Instant,
}

/// Hardware allocation for specialized resources
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareAllocation {
    /// Allocation identifier
    pub allocation_id: AllocationId,

    /// Task that owns this allocation
    pub task_id: TaskId,

    /// Hardware resource type
    pub hardware_type: HardwareType,

    /// Hardware device identifier
    pub device_id: HardwareDeviceId,

    /// Resource utilization details
    pub utilization_details: HardwareUtilizationDetails,

    /// Performance expectations
    pub performance_expectations: HardwarePerformanceExpectations,

    /// Allocation constraints
    pub allocation_constraints: Vec<HardwareAllocationConstraint>,

    /// Allocation timestamp
    pub allocated_at: Instant,
}

// === Resource Pools ===

/// GPU memory pool for efficient memory management
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuMemoryPool {
    /// Pool identifier
    pub pool_id: String,

    /// Associated GPU device
    pub device_id: GpuDeviceId,

    /// Total pool size in bytes
    pub total_size_bytes: usize,

    /// Available size in bytes (simplified for serialization)
    pub available_size_bytes: usize,

    /// Memory blocks in the pool
    pub memory_blocks: HashMap<BlockId, MemoryBlock>,

    /// Free blocks tracking
    pub free_blocks: BTreeMap<usize, Vec<BlockId>>,

    /// Allocation history
    pub allocation_history: VecDeque<PoolAllocationRecord>,

    /// Pool configuration
    pub pool_config: MemoryPoolConfig,

    /// Performance metrics
    pub performance_metrics: PoolPerformanceMetrics,

    /// Fragmentation statistics
    pub fragmentation_stats: FragmentationStatistics,
}

/// CPU thread pool for efficient thread management
#[derive(Debug)]
pub struct CpuThreadPool {
    /// Pool name
    pub name: String,

    /// Worker threads
    pub workers: Vec<ThreadWorker>,

    /// Task queue
    pub task_queue: VecDeque<ThreadTask>,

    /// Pool configuration
    pub config: ThreadPoolConfig,

    /// Performance metrics
    pub metrics: ThreadPoolMetrics,

    /// Load balancing strategy
    pub load_balancer: ThreadPoolLoadBalancer,
}

/// Memory pool for system memory management
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryPool {
    /// Pool identifier
    pub pool_id: String,

    /// Pool type
    pub pool_type: MemoryPoolType,

    /// Total pool size
    pub total_size_bytes: usize,

    /// Available size (simplified for serialization)
    pub available_size_bytes: usize,

    /// Memory blocks
    pub blocks: HashMap<BlockId, MemoryBlock>,

    /// Allocation algorithm
    pub allocation_algorithm: MemoryAllocationAlgorithm,

    /// Pool performance metrics
    pub performance_metrics: MemoryPoolPerformanceMetrics,
}

/// Hardware resource pool for specialized hardware
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareResourcePool {
    /// Pool identifier
    pub pool_id: String,

    /// Hardware type
    pub hardware_type: HardwareType,

    /// Available hardware resources
    pub available_resources: Vec<HardwareResourceInfo>,

    /// Currently allocated resources
    pub allocated_resources: HashMap<TaskId, Vec<HardwareResourceInfo>>,

    /// Pool utilization metrics
    pub utilization_metrics: HardwarePoolUtilizationMetrics,

    /// Resource sharing policies
    pub sharing_policies: HardwareSharingPolicies,
}

// === Resource Monitoring and Metrics ===

/// Resource utilization tracker for GPU devices
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct GpuUtilizationTracker {
    /// GPU utilization percentage
    pub gpu_utilization: f64,

    /// Memory utilization percentage
    pub memory_utilization: f64,

    /// Compute utilization breakdown
    pub compute_utilization: ComputeUtilizationBreakdown,

    /// Power utilization
    pub power_utilization: f64,

    /// Temperature readings
    pub temperature_readings: TemperatureReadings,

    /// Historical utilization data
    pub historical_data: Vec<UtilizationDataPoint>,
}

/// CPU utilization monitor
#[derive(Debug, Clone, Default)]
pub struct CpuUtilizationMonitor {
    /// Per-core utilization
    pub per_core_utilization: HashMap<CpuCoreId, f64>,

    /// Overall CPU utilization
    pub overall_utilization: f64,

    /// Load averages
    pub load_averages: LoadAverages,

    /// Context switch rate
    pub context_switches_per_second: f64,

    /// Cache hit rates
    pub cache_hit_rates: CacheHitRates,

    /// NUMA utilization
    pub numa_utilization: HashMap<NumaNodeId, f64>,
}

/// Memory utilization tracker
#[derive(Debug, Clone, Default)]
pub struct MemoryUtilizationTracker {
    /// System memory utilization
    pub system_memory_utilization: f64,

    /// GPU memory utilization per device
    pub gpu_memory_utilization: HashMap<GpuDeviceId, f64>,

    /// Memory bandwidth utilization
    pub bandwidth_utilization: f64,

    /// Memory pressure indicators
    pub pressure_indicators: MemoryPressureIndicators,

    /// Cache utilization
    pub cache_utilization: CacheUtilizationMetrics,

    /// Memory allocation efficiency
    pub allocation_efficiency: f64,
}

/// Resource performance metrics
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct ResourcePerformanceMetrics {
    /// Allocation efficiency
    pub allocation_efficiency: f64,

    /// Resource utilization rates
    pub utilization_rates: HashMap<String, f64>,

    /// Response times for allocations
    pub allocation_response_times: Vec<Duration>,

    /// Throughput metrics
    pub throughput_metrics: ThroughputMetrics,

    /// Quality of service metrics
    pub qos_metrics: QualityOfServiceMetrics,

    /// Resource contention metrics
    pub contention_metrics: ResourceContentionMetrics,
}

impl OptimizationResourceManager {
    /// Create a new resource manager
    pub fn new(config: ResourceManagementConfig) -> Self {
        Self {
            gpu_manager: Arc::new(Mutex::new(GpuResourceManager::new(&config))),
            cpu_manager: Arc::new(Mutex::new(CpuResourceManager::new(&config))),
            memory_manager: Arc::new(Mutex::new(MemoryResourceManager::new(&config))),
            hardware_manager: Arc::new(Mutex::new(HardwareResourceManager::new(&config))),
            allocation_engine: Arc::new(Mutex::new(ResourceAllocationEngine::new(&config))),
            monitoring_system: Arc::new(Mutex::new(ResourceMonitoringSystem::new(&config))),
            pool_manager: Arc::new(Mutex::new(ResourcePoolManager::new(&config))),
            scheduler: Arc::new(Mutex::new(ResourceScheduler::new(&config))),
            optimization_engine: Arc::new(Mutex::new(ResourceOptimizationEngine::new(&config))),
            performance_tracker: Arc::new(Mutex::new(ResourcePerformanceTracker::new())),
            statistics: Arc::new(Mutex::new(ResourceStatistics::new())),
            config,
        }
    }

    /// Allocate resources for a task
    pub fn allocate_resources(
        &self,
        task_id: TaskId,
        requirements: &ResourceRequirements,
    ) -> Result<ResourceAllocation, ResourceError> {
        // Validate resource request
        self.validate_resource_request(requirements)?;

        // Check resource availability
        let availability = self.check_resource_availability(requirements)?;
        if !availability.sufficient {
            return Err(ResourceError::InsufficientResources(
                availability.missing_resources,
            ));
        }

        // Execute allocation through allocation engine
        let mut allocation_engine = self.allocation_engine.lock().expect("lock should not be poisoned");
        let allocation_plan = allocation_engine.create_allocation_plan(task_id, requirements)?;

        // Allocate GPU resources if needed
        let gpu_allocations = if requirements.gpu_requirements.gpu_count > 0 {
            let mut gpu_manager = self.gpu_manager.lock().expect("lock should not be poisoned");
            gpu_manager.allocate_gpu_resources(task_id, &requirements.gpu_requirements)?
        } else {
            Vec::new()
        };

        // Allocate CPU resources if needed
        let cpu_allocations = if requirements.cpu_requirements.min_cores > 0 {
            let mut cpu_manager = self.cpu_manager.lock().expect("lock should not be poisoned");
            cpu_manager.allocate_cpu_resources(task_id, &requirements.cpu_requirements)?
        } else {
            Vec::new()
        };

        // Allocate memory resources
        let memory_allocations = {
            let mut memory_manager = self.memory_manager.lock().expect("lock should not be poisoned");
            memory_manager.allocate_memory_resources(task_id, &requirements.memory_requirements)?
        };

        // Allocate specialized hardware if needed
        let hardware_allocations = if !requirements.hardware_requirements.is_empty() {
            let mut hardware_manager = self.hardware_manager.lock().expect("lock should not be poisoned");
            hardware_manager
                .allocate_hardware_resources(task_id, &requirements.hardware_requirements)?
        } else {
            Vec::new()
        };

        // Create comprehensive resource allocation
        let allocation = ResourceAllocation {
            allocation_id: AllocationId::new(),
            task_id,
            gpu_allocations,
            cpu_allocations,
            memory_allocations,
            hardware_allocations,
            allocation_strategy_used: allocation_plan.strategy,
            allocated_at: Instant::now(),
            allocation_metadata: allocation_plan.metadata,
        };

        // Update statistics
        {
            let mut stats = self.statistics.lock().expect("lock should not be poisoned");
            stats.successful_allocations += 1;
            stats.total_allocated_resources += allocation.total_resource_count();
        }

        // Start resource monitoring
        {
            let mut monitoring = self.monitoring_system.lock().expect("lock should not be poisoned");
            monitoring.start_monitoring_allocation(&allocation)?;
        }

        Ok(allocation)
    }

    /// Deallocate resources for a task
    pub fn deallocate_resources(&self, task_id: TaskId) -> Result<(), ResourceError> {
        // Get allocation information
        let allocation_info = self.get_allocation_info(task_id)?;

        // Deallocate GPU resources
        if !allocation_info.gpu_allocations.is_empty() {
            let mut gpu_manager = self.gpu_manager.lock().expect("lock should not be poisoned");
            gpu_manager.deallocate_gpu_resources(task_id)?;
        }

        // Deallocate CPU resources
        if !allocation_info.cpu_allocations.is_empty() {
            let mut cpu_manager = self.cpu_manager.lock().expect("lock should not be poisoned");
            cpu_manager.deallocate_cpu_resources(task_id)?;
        }

        // Deallocate memory resources
        {
            let mut memory_manager = self.memory_manager.lock().expect("lock should not be poisoned");
            memory_manager.deallocate_memory_resources(task_id)?;
        }

        // Deallocate hardware resources
        if !allocation_info.hardware_allocations.is_empty() {
            let mut hardware_manager = self.hardware_manager.lock().expect("lock should not be poisoned");
            hardware_manager.deallocate_hardware_resources(task_id)?;
        }

        // Update statistics
        {
            let mut stats = self.statistics.lock().expect("lock should not be poisoned");
            stats.successful_deallocations += 1;
        }

        // Stop resource monitoring
        {
            let mut monitoring = self.monitoring_system.lock().expect("lock should not be poisoned");
            monitoring.stop_monitoring_allocation(task_id)?;
        }

        Ok(())
    }

    /// Get current resource utilization
    pub fn get_resource_utilization(&self) -> Result<ResourceUtilization, ResourceError> {
        let gpu_utilization = {
            let gpu_manager = self.gpu_manager.lock().expect("lock should not be poisoned");
            gpu_manager.get_current_utilization()
        };

        let cpu_utilization = {
            let cpu_manager = self.cpu_manager.lock().expect("lock should not be poisoned");
            cpu_manager.get_current_utilization()
        };

        let memory_utilization = {
            let memory_manager = self.memory_manager.lock().expect("lock should not be poisoned");
            memory_manager.get_current_utilization()
        };

        Ok(ResourceUtilization {
            gpu_utilization,
            cpu_utilization,
            memory_utilization,
            overall_utilization: self.calculate_overall_utilization(
                &gpu_utilization,
                &cpu_utilization,
                &memory_utilization,
            ),
            timestamp: Instant::now(),
        })
    }

    /// Get resource statistics
    pub fn get_resource_statistics(&self) -> ResourceStatistics {
        let stats = self.statistics.lock().expect("lock should not be poisoned");
        stats.clone()
    }

    /// Optimize resource allocation
    pub fn optimize_resources(&self) -> Result<OptimizationResults, ResourceError> {
        let mut optimization_engine = self.optimization_engine.lock().expect("lock should not be poisoned");
        optimization_engine.optimize_current_allocations()
    }

    // === Private Helper Methods ===

    fn validate_resource_request(
        &self,
        requirements: &ResourceRequirements,
    ) -> Result<(), ResourceError> {
        if requirements.memory_requirements.min_memory_bytes == 0 {
            return Err(ResourceError::InvalidResourceRequest(
                "Memory requirements cannot be zero".to_string(),
            ));
        }

        if requirements.cpu_requirements.min_cores == 0
            && requirements.gpu_requirements.gpu_count == 0
        {
            return Err(ResourceError::InvalidResourceRequest(
                "Must specify either CPU or GPU requirements".to_string(),
            ));
        }

        Ok(())
    }

    fn check_resource_availability(
        &self,
        requirements: &ResourceRequirements,
    ) -> Result<ResourceAvailability, ResourceError> {
        let mut missing_resources = Vec::new();

        // Check GPU availability
        if requirements.gpu_requirements.gpu_count > 0 {
            let gpu_manager = self.gpu_manager.lock().expect("lock should not be poisoned");
            if !gpu_manager.has_sufficient_gpu_resources(&requirements.gpu_requirements) {
                missing_resources.push("GPU".to_string());
            }
        }

        // Check CPU availability
        if requirements.cpu_requirements.min_cores > 0 {
            let cpu_manager = self.cpu_manager.lock().expect("lock should not be poisoned");
            if !cpu_manager.has_sufficient_cpu_resources(&requirements.cpu_requirements) {
                missing_resources.push("CPU".to_string());
            }
        }

        // Check memory availability
        let memory_manager = self.memory_manager.lock().expect("lock should not be poisoned");
        if !memory_manager.has_sufficient_memory(&requirements.memory_requirements) {
            missing_resources.push("Memory".to_string());
        }

        Ok(ResourceAvailability {
            sufficient: missing_resources.is_empty(),
            missing_resources,
        })
    }

    fn get_allocation_info(&self, task_id: TaskId) -> Result<ResourceAllocation, ResourceError> {
        // This would retrieve allocation information from internal tracking
        Err(ResourceError::AllocationNotFound(task_id))
    }

    fn calculate_overall_utilization(
        &self,
        gpu_util: &GpuUtilizationTracker,
        cpu_util: &CpuUtilizationMonitor,
        mem_util: &MemoryUtilizationTracker,
    ) -> f64 {
        let weights = (0.4, 0.3, 0.3); // GPU, CPU, Memory weights
        weights.0 * gpu_util.gpu_utilization
            + weights.1 * cpu_util.overall_utilization
            + weights.2 * mem_util.system_memory_utilization
    }
}

// === Resource Manager Implementations ===

impl GpuResourceManager {
    fn new(config: &ResourceManagementConfig) -> Self {
        Self {
            available_devices: Self::discover_gpu_devices(),
            current_allocations: HashMap::new(),
            memory_pools: HashMap::new(),
            utilization_tracker: GpuUtilizationTracker::default(),
            performance_metrics: GpuPerformanceMetrics::default(),
            allocation_history: VecDeque::new(),
            resource_limits: GpuResourceLimits::from_config(&config.gpu_config),
            thermal_manager: GpuThermalManager::new(),
        }
    }

    fn allocate_gpu_resources(
        &mut self,
        task_id: TaskId,
        requirements: &super::task_management::GpuRequirements,
    ) -> Result<Vec<GpuAllocation>, ResourceError> {
        let mut allocations = Vec::new();

        for _ in 0..requirements.gpu_count {
            let device = self.find_suitable_device(requirements)?;
            let allocation = self.allocate_on_device(task_id, device.device_id, requirements)?;
            allocations.push(allocation);
        }

        self.current_allocations
            .insert(task_id, allocations.clone());
        Ok(allocations)
    }

    fn deallocate_gpu_resources(&mut self, task_id: TaskId) -> Result<(), ResourceError> {
        if let Some(allocations) = self.current_allocations.remove(&task_id) {
            for allocation in allocations {
                self.deallocate_device_resources(allocation.device_id, allocation.allocation_id)?;
            }
        }
        Ok(())
    }

    fn has_sufficient_gpu_resources(
        &self,
        requirements: &super::task_management::GpuRequirements,
    ) -> bool {
        let suitable_devices = self
            .available_devices
            .iter()
            .filter(|device| self.device_meets_requirements(device, requirements))
            .count();

        suitable_devices >= requirements.gpu_count
    }

    fn get_current_utilization(&self) -> GpuUtilizationTracker {
        self.utilization_tracker.clone()
    }

    fn discover_gpu_devices() -> Vec<GpuDevice> {
        // This would use CUDA runtime to discover actual devices
        vec![GpuDevice {
            device_id: GpuDeviceId(0),
            name: "NVIDIA GeForce RTX 4090".to_string(),
            model: "RTX 4090".to_string(),
            compute_capability: (8, 9),
            total_memory_bytes: 24 * 1024 * 1024 * 1024, // 24GB
            available_memory_bytes: 24 * 1024 * 1024 * 1024,
            memory_bandwidth_gbps: 1008.0,
            cuda_cores: 16384,
            rt_cores: Some(128),
            tensor_cores: Some(512),
            base_clock_mhz: 2230,
            boost_clock_mhz: 2520,
            memory_clock_mhz: 10501,
            max_power_watts: 450.0,
            current_power_watts: 0,
            temperature_celsius: 30,
            max_temperature_celsius: 83.0,
            capabilities: GpuCapabilities::default(),
            status: GpuDeviceStatus::Available,
            last_updated: Instant::now(),
        }]
    }

    fn find_suitable_device(
        &self,
        requirements: &super::task_management::GpuRequirements,
    ) -> Result<&GpuDevice, ResourceError> {
        self.available_devices
            .iter()
            .find(|device| self.device_meets_requirements(device, requirements))
            .ok_or(ResourceError::NoSuitableDevice)
    }

    fn device_meets_requirements(
        &self,
        device: &GpuDevice,
        requirements: &super::task_management::GpuRequirements,
    ) -> bool {
        device.compute_capability >= requirements.min_compute_capability
            && device.available_memory_bytes >= requirements.min_gpu_memory_bytes
            && device.status == GpuDeviceStatus::Available
    }

    fn allocate_on_device(
        &mut self,
        task_id: TaskId,
        device_id: GpuDeviceId,
        requirements: &super::task_management::GpuRequirements,
    ) -> Result<GpuAllocation, ResourceError> {
        // Find the device
        let device = self
            .available_devices
            .iter_mut()
            .find(|d| d.device_id == device_id)
            .ok_or(ResourceError::DeviceNotFound)?;

        // Allocate memory
        let memory_allocation = GpuMemoryAllocation {
            address_range: MemoryAddressRange {
                start_address: 0, // Would be actual GPU memory address
                end_address: requirements.min_gpu_memory_bytes,
            },
            size_bytes: requirements.min_gpu_memory_bytes,
            memory_type: GpuMemoryType::Global,
            access_pattern: MemoryAccessPattern::Sequential,
            bandwidth_utilization: 0.0,
            fragmentation_info: MemoryFragmentationInfo::default(),
        };

        // Allocate compute resources
        let compute_allocation = GpuComputeAllocation {
            sm_allocation: SmAllocation::default(),
            cuda_cores_allocated: device.cuda_cores / 2, // Allocate half the cores
            tensor_cores_allocated: device.tensor_cores.map(|cores| cores / 2),
            compute_capability_used: device.compute_capability,
            clock_allocation: ClockAllocation::default(),
            power_allocation: PowerAllocation::default(),
        };

        // Update device availability
        let current_available = device.available_memory_bytes.load(Ordering::Relaxed);
        device.available_memory_bytes.store(
            current_available.saturating_sub(requirements.min_gpu_memory_bytes),
            Ordering::Relaxed,
        );

        Ok(GpuAllocation {
            allocation_id: AllocationId::new(),
            task_id,
            device_id,
            memory_allocation,
            compute_allocation,
            allocated_at: Instant::now(),
            expected_release: None,
            status: AllocationStatus::Active,
            performance_metrics: AllocationPerformanceMetrics::default(),
        })
    }

    fn deallocate_device_resources(
        &mut self,
        device_id: GpuDeviceId,
        allocation_id: AllocationId,
    ) -> Result<(), ResourceError> {
        // Find the device and return resources
        if let Some(device) = self
            .available_devices
            .iter_mut()
            .find(|d| d.device_id == device_id)
        {
            // This would involve actual GPU memory deallocation
            // For now, just mark as completed
        }
        Ok(())
    }
}

impl CpuResourceManager {
    fn new(config: &ResourceManagementConfig) -> Self {
        Self {
            core_allocations: HashMap::new(),
            thread_pools: HashMap::new(),
            utilization_monitor: CpuUtilizationMonitor::default(),
            performance_tracker: CpuPerformanceTracker::default(),
            numa_manager: NumaTopologyManager::new(),
            affinity_manager: CpuAffinityManager::new(),
            resource_limits: CpuResourceLimits::from_config(&config.cpu_config),
        }
    }

    fn allocate_cpu_resources(
        &mut self,
        task_id: TaskId,
        requirements: &super::task_management::CpuRequirements,
    ) -> Result<Vec<CpuCoreAllocation>, ResourceError> {
        let mut allocations = Vec::new();
        let cores_needed = requirements.min_cores.min(requirements.preferred_cores);

        for core_id in 0..cores_needed {
            let allocation = CpuCoreAllocation {
                core_id: CpuCoreId(core_id as u32),
                task_id,
                utilization_percentage: 0.0,
                numa_node: self.numa_manager.get_optimal_numa_node(),
                thread_allocations: Vec::new(),
                allocated_at: Instant::now(),
                expected_release: None,
                performance_metrics: CpuAllocationPerformanceMetrics::default(),
            };
            allocations.push(allocation);
        }

        self.core_allocations.insert(task_id, allocations.clone());
        Ok(allocations)
    }

    fn deallocate_cpu_resources(&mut self, task_id: TaskId) -> Result<(), ResourceError> {
        self.core_allocations.remove(&task_id);
        Ok(())
    }

    fn has_sufficient_cpu_resources(
        &self,
        requirements: &super::task_management::CpuRequirements,
    ) -> bool {
        // Simplified check - would involve actual CPU core availability
        let available_cores = num_cpus::get();
        available_cores >= requirements.min_cores
    }

    fn get_current_utilization(&self) -> CpuUtilizationMonitor {
        self.utilization_monitor.clone()
    }
}

impl MemoryResourceManager {
    fn new(config: &ResourceManagementConfig) -> Self {
        Self {
            system_allocations: HashMap::new(),
            gpu_allocations: HashMap::new(),
            memory_pools: Self::initialize_memory_pools(&config.memory_config),
            utilization_tracker: MemoryUtilizationTracker::default(),
            fragmentation_monitor: MemoryFragmentationMonitor::new(),
            optimization_engine: MemoryOptimizationEngine::new(),
            pressure_manager: MemoryPressureManager::new(),
            allocation_stats: MemoryAllocationStatistics::default(),
        }
    }

    fn allocate_memory_resources(
        &mut self,
        task_id: TaskId,
        requirements: &super::task_management::MemoryRequirements,
    ) -> Result<Vec<SystemMemoryAllocation>, ResourceError> {
        let mut allocations = Vec::new();

        let allocation = SystemMemoryAllocation {
            allocation_id: AllocationId::new(),
            task_id,
            address_range: MemoryAddressRange {
                start_address: 0, // Would be actual memory address
                end_address: requirements.min_memory_bytes,
            },
            size_bytes: requirements.min_memory_bytes,
            pool_source: MemoryPoolType::System,
            protection_flags: MemoryProtectionFlags::default(),
            numa_locality: NumaLocalityInfo::default(),
            allocation_strategy: MemoryAllocationStrategy::BestFit,
            performance_characteristics: MemoryPerformanceCharacteristics::default(),
            allocated_at: Instant::now(),
        };

        allocations.push(allocation);
        self.system_allocations.insert(task_id, allocations.clone());
        Ok(allocations)
    }

    fn deallocate_memory_resources(&mut self, task_id: TaskId) -> Result<(), ResourceError> {
        self.system_allocations.remove(&task_id);
        Ok(())
    }

    fn has_sufficient_memory(
        &self,
        requirements: &super::task_management::MemoryRequirements,
    ) -> bool {
        // Simplified check - would involve actual memory availability
        true // For now, assume sufficient memory
    }

    fn get_current_utilization(&self) -> MemoryUtilizationTracker {
        self.utilization_tracker.clone()
    }

    fn initialize_memory_pools(
        config: &MemoryAllocationConfig,
    ) -> HashMap<MemoryPoolType, MemoryPool> {
        let mut pools = HashMap::new();

        pools.insert(
            MemoryPoolType::System,
            MemoryPool {
                pool_id: "system_pool".to_string(),
                pool_type: MemoryPoolType::System,
                total_size_bytes: config.pool_size,
                available_size_bytes: config.pool_size,
                blocks: HashMap::new(),
                allocation_algorithm: MemoryAllocationAlgorithm::BestFit,
                performance_metrics: MemoryPoolPerformanceMetrics::default(),
            },
        );

        pools
    }
}

impl HardwareResourceManager {
    fn new(config: &ResourceManagementConfig) -> Self {
        Self {
            hardware_inventory: HardwareInventory::discover(),
            hardware_allocations: HashMap::new(),
            capability_tracker: HardwareCapabilityTracker::new(),
            health_monitor: HardwareHealthMonitor::new(),
            performance_metrics: HardwarePerformanceMetrics::default(),
            accelerator_manager: AcceleratorManager::new(),
        }
    }

    fn allocate_hardware_resources(
        &mut self,
        task_id: TaskId,
        requirements: &[HardwareRequirement],
    ) -> Result<Vec<HardwareAllocation>, ResourceError> {
        let mut allocations = Vec::new();

        for requirement in requirements {
            let allocation = HardwareAllocation {
                allocation_id: AllocationId::new(),
                task_id,
                hardware_type: requirement.hardware_type,
                device_id: HardwareDeviceId::new(),
                utilization_details: HardwareUtilizationDetails::default(),
                performance_expectations: HardwarePerformanceExpectations::default(),
                allocation_constraints: Vec::new(),
                allocated_at: Instant::now(),
            };
            allocations.push(allocation);
        }

        self.hardware_allocations
            .insert(task_id, allocations.clone());
        Ok(allocations)
    }

    fn deallocate_hardware_resources(&mut self, task_id: TaskId) -> Result<(), ResourceError> {
        self.hardware_allocations.remove(&task_id);
        Ok(())
    }
}

// === Error Handling ===

/// Resource management errors
#[derive(Debug, Clone)]
pub enum ResourceError {
    /// Insufficient resources available
    InsufficientResources(Vec<String>),
    /// Invalid resource request
    InvalidResourceRequest(String),
    /// Resource allocation failed
    AllocationFailed(String),
    /// Resource not found
    ResourceNotFound(String),
    /// Device not found
    DeviceNotFound,
    /// No suitable device available
    NoSuitableDevice,
    /// Allocation not found
    AllocationNotFound(TaskId),
    /// Resource deallocation failed
    DeallocationFailed(String),
    /// Resource optimization failed
    OptimizationFailed(String),
    /// Hardware error
    HardwareError(String),
}

// === Placeholder Types and Default Implementations ===

macro_rules! default_placeholder_type {
    ($name:ident) => {
        #[derive(Debug, Clone, Default, PartialEq, Eq, Hash, Serialize, Deserialize)]
        pub struct $name {
            pub placeholder: bool,
        }
    };
}

// Specialized macro for ID types that should be newtypes
macro_rules! newtype_id {
    ($name:ident) => {
        #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
        pub struct $name(pub u32);

        impl Default for $name {
            fn default() -> Self {
                Self(0)
            }
        }
    };
}

// Generate placeholder types for all the complex types that would be fully implemented
default_placeholder_type!(AllocationId);
default_placeholder_type!(GpuCapabilities);
default_placeholder_type!(GpuDeviceStatus);
default_placeholder_type!(AllocationStatus);
default_placeholder_type!(AllocationPerformanceMetrics);
default_placeholder_type!(MemoryAddressRange);
default_placeholder_type!(GpuMemoryType);
default_placeholder_type!(MemoryAccessPattern);
default_placeholder_type!(MemoryFragmentationInfo);
default_placeholder_type!(SmAllocation);
default_placeholder_type!(ClockAllocation);
default_placeholder_type!(PowerAllocation);
newtype_id!(CpuCoreId);
newtype_id!(NumaNodeId);
default_placeholder_type!(ThreadAllocation);
default_placeholder_type!(CpuAllocationPerformanceMetrics);
default_placeholder_type!(MemoryProtectionFlags);
default_placeholder_type!(NumaLocalityInfo);
default_placeholder_type!(MemoryAllocationStrategy);
default_placeholder_type!(MemoryPerformanceCharacteristics);
default_placeholder_type!(HardwareType);
default_placeholder_type!(HardwareDeviceId);
default_placeholder_type!(HardwareUtilizationDetails);
default_placeholder_type!(HardwarePerformanceExpectations);
default_placeholder_type!(HardwareAllocationConstraint);
default_placeholder_type!(BlockId);
default_placeholder_type!(MemoryBlock);
default_placeholder_type!(PoolAllocationRecord);
default_placeholder_type!(MemoryPoolConfig);
default_placeholder_type!(PoolPerformanceMetrics);
default_placeholder_type!(FragmentationStatistics);
default_placeholder_type!(ThreadWorker);
default_placeholder_type!(ThreadTask);
default_placeholder_type!(ThreadPoolConfig);
default_placeholder_type!(ThreadPoolMetrics);
default_placeholder_type!(ThreadPoolLoadBalancer);
default_placeholder_type!(MemoryAllocationAlgorithm);
default_placeholder_type!(MemoryPoolPerformanceMetrics);
default_placeholder_type!(HardwareResourceInfo);
default_placeholder_type!(HardwarePoolUtilizationMetrics);
default_placeholder_type!(HardwareSharingPolicies);
default_placeholder_type!(ComputeUtilizationBreakdown);
default_placeholder_type!(TemperatureReadings);
default_placeholder_type!(UtilizationDataPoint);
default_placeholder_type!(LoadAverages);
default_placeholder_type!(CacheHitRates);
default_placeholder_type!(MemoryPressureIndicators);
default_placeholder_type!(CacheUtilizationMetrics);
default_placeholder_type!(ThroughputMetrics);
default_placeholder_type!(QualityOfServiceMetrics);
default_placeholder_type!(ResourceContentionMetrics);

// Additional complex types
default_placeholder_type!(ResourceMonitoringConfig);
default_placeholder_type!(ResourceOptimizationConfig);
default_placeholder_type!(ResourcePoolingConfig);
default_placeholder_type!(AdvancedResourceConfig);
default_placeholder_type!(GpuPerformanceMetrics);
default_placeholder_type!(GpuResourceLimits);
default_placeholder_type!(GpuThermalManager);
default_placeholder_type!(CpuPerformanceTracker);
default_placeholder_type!(NumaTopologyManager);
default_placeholder_type!(CpuAffinityManager);
default_placeholder_type!(CpuResourceLimits);
default_placeholder_type!(MemoryFragmentationMonitor);
default_placeholder_type!(MemoryOptimizationEngine);
default_placeholder_type!(MemoryPressureManager);
default_placeholder_type!(MemoryAllocationStatistics);
default_placeholder_type!(HardwareInventory);
default_placeholder_type!(HardwareCapabilityTracker);
default_placeholder_type!(HardwareHealthMonitor);
default_placeholder_type!(HardwarePerformanceMetrics);
default_placeholder_type!(AcceleratorManager);
default_placeholder_type!(AllocationDecisionEngine);
default_placeholder_type!(ResourceReservationSystem);
default_placeholder_type!(AllocationMLPredictor);
default_placeholder_type!(AllocationPolicyEnforcer);
default_placeholder_type!(ResourceConflictResolver);
default_placeholder_type!(MonitoringTarget);
default_placeholder_type!(MonitoringAgent);
default_placeholder_type!(MetricsCollector);
default_placeholder_type!(ResourceAlertSystem);
default_placeholder_type!(DashboardData);
default_placeholder_type!(HistoricalDataStorage);
default_placeholder_type!(ResourceAnomalyDetector);
default_placeholder_type!(ResourcePoolOptimizer);
default_placeholder_type!(PoolUtilizationTracker);
default_placeholder_type!(ResourceRequest);
default_placeholder_type!(SchedulingStrategy);
default_placeholder_type!(ResourceConflictDetector);
default_placeholder_type!(SchedulingOptimizationEngine);
default_placeholder_type!(ResourceLoadBalancer);
default_placeholder_type!(ResourceOptimizationEngine);
default_placeholder_type!(ResourcePerformanceTracker);
default_placeholder_type!(ResourceStatistics);
default_placeholder_type!(ResourceAllocation);
default_placeholder_type!(ResourceAvailability);
default_placeholder_type!(ResourceUtilization);
default_placeholder_type!(OptimizationResults);

// Trait definitions for pluggable algorithms
pub trait AllocationAlgorithm: std::fmt::Debug + Send + Sync {
    fn allocate(
        &self,
        requirements: &ResourceRequirements,
    ) -> Result<ResourceAllocation, ResourceError>;
}

pub trait SchedulingAlgorithm: std::fmt::Debug + Send + Sync {
    fn schedule(&self, requests: &[ResourceRequest]) -> Vec<ResourceRequest>;
}

// Implement necessary traits
impl AllocationId {
    pub fn new() -> Self {
        Self::default()
    }
}

impl GpuDeviceStatus {
    const fn Available() -> Self {
        Self { placeholder: false }
    }
}

// PartialEq is already derived by the default_placeholder_type macro

impl HardwareInventory {
    fn discover() -> Self {
        Self::default()
    }
}

impl HardwareDeviceId {
    fn new() -> Self {
        Self::default()
    }
}

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

    #[test]
    fn test_resource_manager_creation() {
        let config = ResourceManagementConfig::default();
        let resource_manager = OptimizationResourceManager::new(config);
        let stats = resource_manager.get_resource_statistics();
        assert_eq!(stats.successful_allocations, 0);
    }

    #[test]
    fn test_gpu_device_creation() {
        let devices = GpuResourceManager::discover_gpu_devices();
        assert!(!devices.is_empty());

        let device = &devices[0];
        assert_eq!(device.device_id, GpuDeviceId(0));
        assert!(device.total_memory_bytes > 0);
        assert!(device.cuda_cores > 0);
    }

    #[test]
    fn test_resource_utilization_calculation() {
        let config = ResourceManagementConfig::default();
        let resource_manager = OptimizationResourceManager::new(config);

        let utilization = resource_manager.get_resource_utilization().expect("resource utilization retrieval should succeed");
        assert!(utilization.overall_utilization >= 0.0);
        assert!(utilization.overall_utilization <= 1.0);
    }

    #[test]
    fn test_memory_pool_initialization() {
        let config = MemoryAllocationConfig::default();
        let pools = MemoryResourceManager::initialize_memory_pools(&config);

        assert!(pools.contains_key(&MemoryPoolType::System));
        let system_pool = &pools[&MemoryPoolType::System];
        assert_eq!(system_pool.total_size_bytes, config.pool_size);
    }
}

// Default implementations for configuration types
impl Default for ResourceManagementConfig {
    fn default() -> Self {
        Self {
            memory_config: MemoryAllocationConfig::default(),
            cpu_config: CpuAllocationConfig::default(),
            gpu_config: GpuAllocationConfig::default(),
            allocation_strategy: ResourceAllocationStrategy::Dynamic,
            monitoring_config: ResourceMonitoringConfig::default(),
            optimization_config: ResourceOptimizationConfig::default(),
            pooling_config: ResourcePoolingConfig::default(),
            advanced_config: AdvancedResourceConfig::default(),
        }
    }
}

impl GpuResourceLimits {
    fn from_config(config: &GpuAllocationConfig) -> Self {
        Self::default()
    }
}

impl CpuResourceLimits {
    fn from_config(config: &CpuAllocationConfig) -> Self {
        Self::default()
    }
}

impl NumaTopologyManager {
    fn new() -> Self {
        Self::default()
    }

    fn get_optimal_numa_node(&self) -> Option<NumaNodeId> {
        None
    }
}

impl CpuAffinityManager {
    fn new() -> Self {
        Self::default()
    }
}

impl GpuThermalManager {
    fn new() -> Self {
        Self::default()
    }
}

impl MemoryFragmentationMonitor {
    fn new() -> Self {
        Self::default()
    }
}

impl MemoryOptimizationEngine {
    fn new() -> Self {
        Self::default()
    }
}

impl MemoryPressureManager {
    fn new() -> Self {
        Self::default()
    }
}

impl HardwareCapabilityTracker {
    fn new() -> Self {
        Self::default()
    }
}

impl HardwareHealthMonitor {
    fn new() -> Self {
        Self::default()
    }
}

impl AcceleratorManager {
    fn new() -> Self {
        Self::default()
    }
}

impl ResourceAllocationEngine {
    fn new(config: &ResourceManagementConfig) -> Self {
        Self {
            allocation_strategy: config.allocation_strategy,
            decision_engine: AllocationDecisionEngine::default(),
            reservation_system: ResourceReservationSystem::default(),
            optimization_algorithms: HashMap::new(),
            ml_predictor: None,
            policy_enforcer: AllocationPolicyEnforcer::default(),
            conflict_resolver: ResourceConflictResolver::default(),
        }
    }

    fn create_allocation_plan(
        &self,
        task_id: TaskId,
        requirements: &ResourceRequirements,
    ) -> Result<AllocationPlan, ResourceError> {
        Ok(AllocationPlan {
            strategy: self.allocation_strategy,
            metadata: HashMap::new(),
        })
    }
}

impl ResourceMonitoringSystem {
    fn new(config: &ResourceManagementConfig) -> Self {
        Self {
            monitoring_agents: HashMap::new(),
            metrics_collector: MetricsCollector::default(),
            alert_system: ResourceAlertSystem::default(),
            dashboard_data: DashboardData::default(),
            historical_storage: HistoricalDataStorage::default(),
            anomaly_detector: ResourceAnomalyDetector::default(),
        }
    }

    fn start_monitoring_allocation(
        &mut self,
        allocation: &ResourceAllocation,
    ) -> Result<(), ResourceError> {
        Ok(())
    }

    fn stop_monitoring_allocation(&mut self, task_id: TaskId) -> Result<(), ResourceError> {
        Ok(())
    }
}

impl ResourcePoolManager {
    fn new(config: &ResourceManagementConfig) -> Self {
        Self {
            gpu_memory_pools: HashMap::new(),
            cpu_thread_pools: HashMap::new(),
            system_memory_pools: HashMap::new(),
            hardware_pools: HashMap::new(),
            pool_optimizer: ResourcePoolOptimizer::default(),
            utilization_tracker: PoolUtilizationTracker::default(),
        }
    }
}

impl ResourceScheduler {
    fn new(config: &ResourceManagementConfig) -> Self {
        Self {
            scheduling_queue: VecDeque::new(),
            scheduling_algorithms: HashMap::new(),
            current_strategy: SchedulingStrategy::default(),
            conflict_detector: ResourceConflictDetector::default(),
            optimization_engine: SchedulingOptimizationEngine::default(),
            load_balancer: ResourceLoadBalancer::default(),
        }
    }
}

impl ResourceOptimizationEngine {
    fn new(config: &ResourceManagementConfig) -> Self {
        Self::default()
    }

    fn optimize_current_allocations(&self) -> Result<OptimizationResults, ResourceError> {
        Ok(OptimizationResults::default())
    }
}

impl ResourcePerformanceTracker {
    fn new() -> Self {
        Self::default()
    }
}

impl ResourceStatistics {
    fn new() -> Self {
        Self {
            successful_allocations: 0,
            successful_deallocations: 0,
            total_allocated_resources: 0,
            ..Default::default()
        }
    }

    fn total_resource_count(&self) -> usize {
        self.total_allocated_resources
    }
}

impl ResourceAllocation {
    fn total_resource_count(&self) -> usize {
        self.gpu_allocations.len()
            + self.cpu_allocations.len()
            + self.memory_allocations.len()
            + self.hardware_allocations.len()
    }
}

// Additional supporting types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AllocationPlan {
    pub strategy: ResourceAllocationStrategy,
    pub metadata: HashMap<String, String>,
}

impl Default for MemoryPoolType {
    fn default() -> Self {
        Self::System
    }
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum MemoryPoolType {
    System,
    GPU,
    Shared,
    Pinned,
}

// Stub types for incomplete implementation
#[derive(Debug, Clone)]
pub struct GpuAllocationRecord {
    pub task_id: TaskId,
    pub size_bytes: usize,
    pub timestamp: Instant,
}