optirs-learned 0.3.1

// Resource management for optimization coordination

use scirs2_core::numeric::Float;
use std::collections::{HashMap, VecDeque, BTreeMap};
use std::fmt::Debug;
use std::time::{Duration, Instant, SystemTime};
use std::sync::{Arc, Mutex};

use super::{
    ResourceAllocationStrategy, OptimizationContext, LandscapeFeatures,
    ResourceRequirements, ComputationalBudget,
};
use crate::error::Result;

/// Resource manager for coordinating optimization resources
#[derive(Debug)]
pub struct ResourceManager<T: Float + Debug + Send + Sync + 'static> {
    /// Available resources
    available_resources: ResourcePool,

    /// Resource allocation tracker
    allocation_tracker: ResourceAllocationTracker<T>,

    /// Resource optimization engine
    optimization_engine: ResourceOptimizationEngine<T>,

    /// Load balancer
    load_balancer: LoadBalancer<T>,

    /// Resource allocation strategy
    allocation_strategy: ResourceAllocationStrategy,

    /// Performance monitoring
    performance_monitor: ResourcePerformanceMonitor<T>,

    /// Resource constraints
    constraints: ResourceConstraints,

    /// Allocation history
    allocation_history: VecDeque<AllocationRecord<T>>,
}

impl<T: Float + Debug + Send + Sync + 'static> ResourceManager<T> {
    /// Create new resource manager
    pub fn new() -> Result<Self> {
        Ok(Self {
            available_resources: ResourcePool::new()?,
            allocation_tracker: ResourceAllocationTracker::new()?,
            optimization_engine: ResourceOptimizationEngine::new()?,
            load_balancer: LoadBalancer::new()?,
            allocation_strategy: ResourceAllocationStrategy::Adaptive,
            performance_monitor: ResourcePerformanceMonitor::new(),
            constraints: ResourceConstraints::default(),
            allocation_history: VecDeque::new(),
        })
    }

    /// Allocate resources to optimizers
    pub fn allocate_resources(
        &mut self,
        optimizers: &[String],
        context: &OptimizationContext<T>,
    ) -> Result<ResourceAllocation<T>> {
        let start_time = Instant::now();

        // Phase 1: Resource Requirements Analysis
        let requirements = self.analyze_resource_requirements(optimizers, context)?;

        // Phase 2: Availability Assessment
        let availability = self.assess_resource_availability(&requirements)?;

        // Phase 3: Allocation Strategy Execution
        let allocation = match self.allocation_strategy {
            ResourceAllocationStrategy::Uniform => {
                self.allocate_uniform(optimizers, &availability, context)
            }
            ResourceAllocationStrategy::PerformanceWeighted => {
                self.allocate_performance_weighted(optimizers, &availability, context)
            }
            ResourceAllocationStrategy::Adaptive => {
                self.allocate_adaptive(optimizers, &availability, context)
            }
            ResourceAllocationStrategy::PriorityBased => {
                self.allocate_priority_based(optimizers, &availability, context)
            }
            ResourceAllocationStrategy::DynamicLoadBalancing => {
                self.allocate_dynamic_load_balancing(optimizers, &availability, context)
            }
        }?;

        // Phase 4: Load Balancing
        let balanced_allocation = self.load_balancer.balance_allocation(&allocation, &availability)?;

        // Phase 5: Resource Optimization
        let optimized_allocation = self.optimization_engine.optimize_allocation(
            &balanced_allocation,
            &requirements,
            &availability,
        )?;

        // Phase 6: Allocation Validation and Enforcement
        self.validate_and_enforce_allocation(&optimized_allocation)?;

        // Phase 7: Tracking and Monitoring
        self.track_allocation(&optimized_allocation, start_time.elapsed())?;

        Ok(optimized_allocation)
    }

    /// Analyze resource requirements for optimizers
    fn analyze_resource_requirements(
        &self,
        optimizers: &[String],
        context: &OptimizationContext<T>,
    ) -> Result<HashMap<String, OptimizerResourceRequirements>> {
        let mut requirements = HashMap::new();

        for optimizer_id in optimizers {
            let base_requirements = self.get_base_requirements(optimizer_id)?;
            let context_adjusted = self.adjust_for_context(&base_requirements, context)?;
            let final_requirements = self.apply_historical_adjustments(optimizer_id, &context_adjusted)?;

            requirements.insert(optimizer_id.clone(), final_requirements);
        }

        Ok(requirements)
    }

    /// Assess current resource availability
    fn assess_resource_availability(
        &mut self,
        requirements: &HashMap<String, OptimizerResourceRequirements>,
    ) -> Result<ResourceAvailability> {
        // Get current resource state
        let mut availability = self.available_resources.get_current_availability()?;

        // Account for already allocated resources
        let allocated_resources = self.allocation_tracker.get_total_allocated_resources()?;
        availability.subtract_allocated(&allocated_resources)?;

        // Predict future availability
        let predicted_availability = self.performance_monitor.predict_future_availability(
            &availability,
            Duration::from_secs(60), // 1 minute horizon
        )?;

        // Apply resource constraints
        availability.apply_constraints(&self.constraints)?;

        Ok(availability)
    }

    /// Uniform allocation strategy
    fn allocate_uniform(
        &self,
        optimizers: &[String],
        availability: &ResourceAvailability,
        _context: &OptimizationContext<T>,
    ) -> Result<ResourceAllocation<T>> {
        let mut allocation = ResourceAllocation::new();

        // Divide resources equally among optimizers
        let optimizer_count = optimizers.len();
        if optimizer_count == 0 {
            return Ok(allocation);
        }

        for optimizer_id in optimizers {
            let cpu_allocation = availability.cpu_cores as f64 / optimizer_count as f64;
            let memory_allocation = availability.memory_mb / optimizer_count;
            let gpu_allocation = availability.gpu_memory_mb / optimizer_count;

            let optimizer_resources = OptimizerResources {
                cpu_allocation,
                memory_allocation,
                gpu_memory_allocation: gpu_allocation,
                time_allocation: availability.time_budget / optimizer_count as u32,
                priority: T::one(),
                quality_of_service: QualityOfService::Standard,
            };

            allocation.allocations.insert(optimizer_id.clone(), optimizer_resources);
        }

        Ok(allocation)
    }

    /// Performance-weighted allocation strategy
    fn allocate_performance_weighted(
        &self,
        optimizers: &[String],
        availability: &ResourceAvailability,
        _context: &OptimizationContext<T>,
    ) -> Result<ResourceAllocation<T>> {
        let mut allocation = ResourceAllocation::new();

        // Get performance weights for each optimizer
        let performance_weights = self.get_performance_weights(optimizers)?;
        let total_weight: T = performance_weights.values().fold(T::zero(), |acc, &w| acc + w);

        if total_weight <= T::zero() {
            // Fallback to uniform allocation
            return self.allocate_uniform(optimizers, availability, _context);
        }

        for optimizer_id in optimizers {
            let weight = performance_weights.get(optimizer_id).cloned().unwrap_or(T::one());
            let weight_ratio = weight / total_weight;

            let cpu_allocation = availability.cpu_cores as f64 * weight_ratio.to_f64().expect("unwrap failed");
            let memory_allocation = (availability.memory_mb as f64 * weight_ratio.to_f64().expect("unwrap failed")) as usize;
            let gpu_allocation = (availability.gpu_memory_mb as f64 * weight_ratio.to_f64().expect("unwrap failed")) as usize;
            let time_allocation = Duration::from_secs_f64(
                availability.time_budget.as_secs_f64() * weight_ratio.to_f64().expect("unwrap failed")
            );

            let optimizer_resources = OptimizerResources {
                cpu_allocation,
                memory_allocation,
                gpu_memory_allocation: gpu_allocation,
                time_allocation,
                priority: weight,
                quality_of_service: self.determine_qos_from_weight(weight)?,
            };

            allocation.allocations.insert(optimizer_id.clone(), optimizer_resources);
        }

        Ok(allocation)
    }

    /// Adaptive allocation strategy
    fn allocate_adaptive(
        &self,
        optimizers: &[String],
        availability: &ResourceAvailability,
        context: &OptimizationContext<T>,
    ) -> Result<ResourceAllocation<T>> {
        let mut allocation = ResourceAllocation::new();

        // Analyze current optimization landscape
        let landscape_complexity = self.assess_landscape_complexity(context)?;
        let optimizer_suitability = self.assess_optimizer_suitability(optimizers, context)?;

        // Dynamic weight calculation based on context
        let mut adaptive_weights = HashMap::new();
        let mut total_weight = T::zero();

        for optimizer_id in optimizers {
            let base_weight = optimizer_suitability.get(optimizer_id).cloned().unwrap_or(T::one());
            let complexity_adjustment = self.calculate_complexity_adjustment(optimizer_id, landscape_complexity)?;
            let historical_adjustment = self.get_historical_performance_adjustment(optimizer_id)?;

            let adaptive_weight = base_weight * complexity_adjustment * historical_adjustment;
            adaptive_weights.insert(optimizer_id.clone(), adaptive_weight);
            total_weight = total_weight + adaptive_weight;
        }

        // Allocate resources based on adaptive weights
        if total_weight <= T::zero() {
            return self.allocate_uniform(optimizers, availability, context);
        }

        for optimizer_id in optimizers {
            let weight = adaptive_weights.get(optimizer_id).cloned().unwrap_or(T::one());
            let weight_ratio = weight / total_weight;

            // Apply adaptive scaling factors
            let cpu_scale = self.calculate_cpu_scaling_factor(optimizer_id, context)?;
            let memory_scale = self.calculate_memory_scaling_factor(optimizer_id, context)?;

            let cpu_allocation = availability.cpu_cores as f64 * weight_ratio.to_f64().expect("unwrap failed") * cpu_scale;
            let memory_allocation = (availability.memory_mb as f64 * weight_ratio.to_f64().expect("unwrap failed") * memory_scale) as usize;
            let gpu_allocation = (availability.gpu_memory_mb as f64 * weight_ratio.to_f64().expect("unwrap failed")) as usize;
            let time_allocation = Duration::from_secs_f64(
                availability.time_budget.as_secs_f64() * weight_ratio.to_f64().expect("unwrap failed")
            );

            let optimizer_resources = OptimizerResources {
                cpu_allocation,
                memory_allocation,
                gpu_memory_allocation: gpu_allocation,
                time_allocation,
                priority: weight,
                quality_of_service: QualityOfService::Adaptive,
            };

            allocation.allocations.insert(optimizer_id.clone(), optimizer_resources);
        }

        Ok(allocation)
    }

    /// Priority-based allocation strategy
    fn allocate_priority_based(
        &self,
        optimizers: &[String],
        availability: &ResourceAvailability,
        context: &OptimizationContext<T>,
    ) -> Result<ResourceAllocation<T>> {
        let mut allocation = ResourceAllocation::new();

        // Get priority rankings
        let priorities = self.get_optimizer_priorities(optimizers, context)?;
        let mut sorted_optimizers: Vec<_> = priorities.iter().collect();
        sorted_optimizers.sort_by(|a, b| b.1.partial_cmp(a.1).expect("unwrap failed"));

        // Allocate resources in priority order
        let mut remaining_cpu = availability.cpu_cores as f64;
        let mut remaining_memory = availability.memory_mb;
        let mut remaining_gpu = availability.gpu_memory_mb;
        let mut remaining_time = availability.time_budget;

        for (optimizer_id, &priority) in sorted_optimizers {
            let requirements = self.get_base_requirements(optimizer_id)?;

            // Allocate based on priority and remaining resources
            let cpu_allocation = (requirements.cpu_cores as f64).min(remaining_cpu);
            let memory_allocation = requirements.memory_mb.min(remaining_memory);
            let gpu_allocation = requirements.gpu_memory_mb.min(remaining_gpu);
            let time_allocation = requirements.max_time.min(remaining_time);

            let optimizer_resources = OptimizerResources {
                cpu_allocation,
                memory_allocation,
                gpu_memory_allocation: gpu_allocation,
                time_allocation,
                priority,
                quality_of_service: self.determine_qos_from_priority(priority)?,
            };

            allocation.allocations.insert(optimizer_id.clone(), optimizer_resources);

            // Update remaining resources
            remaining_cpu -= cpu_allocation;
            remaining_memory = remaining_memory.saturating_sub(memory_allocation);
            remaining_gpu = remaining_gpu.saturating_sub(gpu_allocation);
            remaining_time = remaining_time.saturating_sub(time_allocation);
        }

        Ok(allocation)
    }

    /// Dynamic load balancing allocation
    fn allocate_dynamic_load_balancing(
        &mut self,
        optimizers: &[String],
        availability: &ResourceAvailability,
        context: &OptimizationContext<T>,
    ) -> Result<ResourceAllocation<T>> {
        // Start with adaptive allocation
        let mut allocation = self.allocate_adaptive(optimizers, availability, context)?;

        // Apply load balancing adjustments
        self.load_balancer.apply_dynamic_balancing(&mut allocation, availability)?;

        Ok(allocation)
    }

    /// Validate and enforce resource allocation
    fn validate_and_enforce_allocation(&mut self, allocation: &ResourceAllocation<T>) -> Result<()> {
        // Validate allocation against constraints
        for (optimizer_id, resources) in &allocation.allocations {
            self.validate_optimizer_allocation(optimizer_id, resources)?;
        }

        // Check total resource usage
        let total_usage = allocation.calculate_total_usage()?;
        self.validate_total_usage(&total_usage)?;

        // Reserve allocated resources
        self.allocation_tracker.reserve_resources(allocation)?;

        Ok(())
    }

    /// Track resource allocation
    fn track_allocation(&mut self, allocation: &ResourceAllocation<T>, duration: Duration) -> Result<()> {
        let record = AllocationRecord {
            timestamp: SystemTime::now(),
            allocation: allocation.clone(),
            allocation_time: duration,
            efficiency_score: self.calculate_allocation_efficiency(allocation)?,
        };

        self.allocation_history.push_back(record);
        if self.allocation_history.len() > 1000 {
            self.allocation_history.pop_front();
        }

        // Update performance monitoring
        self.performance_monitor.record_allocation(allocation, duration)?;

        Ok(())
    }

    /// Get efficiency score
    pub fn get_efficiency_score(&self) -> Result<T> {
        if self.allocation_history.is_empty() {
            return Ok(scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()));
        }

        let recent_scores: Vec<_> = self.allocation_history
            .iter()
            .rev()
            .take(10)
            .map(|record| record.efficiency_score)
            .collect();

        let average_score = recent_scores.iter().fold(T::zero(), |acc, &score| acc + score)
            / T::from(recent_scores.len()).expect("unwrap failed");

        Ok(average_score)
    }

    /// Helper methods
    fn get_base_requirements(&self, optimizer_id: &str) -> Result<OptimizerResourceRequirements> {
        // Optimizer-specific resource requirements
        let requirements = match optimizer_id {
            "adam" => OptimizerResourceRequirements {
                cpu_cores: 1,
                memory_mb: 100,
                gpu_memory_mb: 50,
                max_time: Duration::from_secs(60),
                io_bandwidth: 10,
            },
            "sgd_momentum" => OptimizerResourceRequirements {
                cpu_cores: 1,
                memory_mb: 50,
                gpu_memory_mb: 25,
                max_time: Duration::from_secs(30),
                io_bandwidth: 5,
            },
            "learned_lstm" => OptimizerResourceRequirements {
                cpu_cores: 2,
                memory_mb: 200,
                gpu_memory_mb: 150,
                max_time: Duration::from_secs(120),
                io_bandwidth: 20,
            },
            _ => OptimizerResourceRequirements {
                cpu_cores: 1,
                memory_mb: 100,
                gpu_memory_mb: 50,
                max_time: Duration::from_secs(60),
                io_bandwidth: 10,
            },
        };

        Ok(requirements)
    }

    fn adjust_for_context(
        &self,
        base_requirements: &OptimizerResourceRequirements,
        context: &OptimizationContext<T>,
    ) -> Result<OptimizerResourceRequirements> {
        // Scale requirements based on problem dimensionality
        let dimensionality_scale = (context.dimensionality as f64 / 100.0).max(0.1).min(10.0);

        let adjusted = OptimizerResourceRequirements {
            cpu_cores: ((base_requirements.cpu_cores as f64 * dimensionality_scale) as usize).max(1),
            memory_mb: ((base_requirements.memory_mb as f64 * dimensionality_scale) as usize).max(50),
            gpu_memory_mb: ((base_requirements.gpu_memory_mb as f64 * dimensionality_scale) as usize),
            max_time: Duration::from_secs_f64(
                base_requirements.max_time.as_secs_f64() * dimensionality_scale
            ),
            io_bandwidth: ((base_requirements.io_bandwidth as f64 * dimensionality_scale) as usize).max(1),
        };

        Ok(adjusted)
    }

    fn apply_historical_adjustments(
        &self,
        optimizer_id: &str,
        requirements: &OptimizerResourceRequirements,
    ) -> Result<OptimizerResourceRequirements> {
        // Get historical resource usage for this optimizer
        let historical_usage = self.allocation_tracker.get_historical_usage(optimizer_id)?;

        if historical_usage.is_empty() {
            return Ok(requirements.clone());
        }

        // Calculate adjustment factors based on historical efficiency
        let cpu_efficiency = historical_usage.average_cpu_efficiency;
        let memory_efficiency = historical_usage.average_memory_efficiency;

        let adjusted = OptimizerResourceRequirements {
            cpu_cores: ((requirements.cpu_cores as f64 / cpu_efficiency) as usize).max(1),
            memory_mb: ((requirements.memory_mb as f64 / memory_efficiency) as usize).max(50),
            gpu_memory_mb: requirements.gpu_memory_mb, // Keep GPU allocation stable
            max_time: requirements.max_time,
            io_bandwidth: requirements.io_bandwidth,
        };

        Ok(adjusted)
    }

    fn get_performance_weights(&self, optimizers: &[String]) -> Result<HashMap<String, T>> {
        let mut weights = HashMap::new();

        for optimizer_id in optimizers {
            let historical_performance = self.performance_monitor.get_optimizer_performance(optimizer_id)?;
            let weight = T::from(historical_performance).unwrap_or(T::one());
            weights.insert(optimizer_id.clone(), weight);
        }

        Ok(weights)
    }

    fn determine_qos_from_weight(&self, weight: T) -> Result<QualityOfService> {
        let weight_value = weight.to_f64().unwrap_or(0.5);

        if weight_value > 0.8 {
            Ok(QualityOfService::Premium)
        } else if weight_value > 0.6 {
            Ok(QualityOfService::Standard)
        } else {
            Ok(QualityOfService::Basic)
        }
    }

    fn determine_qos_from_priority(&self, priority: T) -> Result<QualityOfService> {
        self.determine_qos_from_weight(priority)
    }

    fn assess_landscape_complexity(&self, context: &OptimizationContext<T>) -> Result<f64> {
        // Simplified complexity assessment
        let dimensionality_complexity = (context.dimensionality as f64).ln() / 10.0;
        let iteration_complexity = (context.iteration as f64 / 1000.0).min(1.0);

        Ok((dimensionality_complexity + iteration_complexity) / 2.0)
    }

    fn assess_optimizer_suitability(
        &self,
        optimizers: &[String],
        _context: &OptimizationContext<T>,
    ) -> Result<HashMap<String, T>> {
        let mut suitability = HashMap::new();

        for optimizer_id in optimizers {
            // Simplified suitability based on optimizer type
            let score = match optimizer_id.as_str() {
                "adam" => scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()),
                "sgd_momentum" => scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero()),
                "learned_lstm" => scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
                _ => scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()),
            };

            suitability.insert(optimizer_id.clone(), score);
        }

        Ok(suitability)
    }

    fn calculate_complexity_adjustment(&self, optimizer_id: &str, complexity: f64) -> Result<T> {
        // Adjust based on optimizer's ability to handle complexity
        let base_adjustment = match optimizer_id {
            "learned_lstm" => 1.2, // Better with complex problems
            "adam" => 1.0 + complexity * 0.2,
            "sgd_momentum" => 1.0 - complexity * 0.1,
            _ => 1.0,
        };

        Ok(scirs2_core::numeric::NumCast::from(base_adjustment).unwrap_or_else(|| T::zero()))
    }

    fn get_historical_performance_adjustment(&self, optimizer_id: &str) -> Result<T> {
        let performance = self.performance_monitor.get_optimizer_performance(optimizer_id)?;
        Ok(scirs2_core::numeric::NumCast::from(0.8 + performance * 0.4).unwrap_or_else(|| T::zero())) // Scale between 0.8 and 1.2
    }

    fn calculate_cpu_scaling_factor(&self, optimizer_id: &str, _context: &OptimizationContext<T>) -> Result<f64> {
        // CPU scaling based on optimizer characteristics
        match optimizer_id {
            "learned_lstm" => Ok(1.5), // More CPU intensive
            "adam" => Ok(1.1),
            "sgd_momentum" => Ok(0.9),
            _ => Ok(1.0),
        }
    }

    fn calculate_memory_scaling_factor(&self, optimizer_id: &str, _context: &OptimizationContext<T>) -> Result<f64> {
        // Memory scaling based on optimizer characteristics
        match optimizer_id {
            "learned_lstm" => Ok(2.0), // More memory intensive
            "adam" => Ok(1.2),
            "sgd_momentum" => Ok(0.8),
            _ => Ok(1.0),
        }
    }

    fn get_optimizer_priorities(
        &self,
        optimizers: &[String],
        _context: &OptimizationContext<T>,
    ) -> Result<HashMap<String, T>> {
        let mut priorities = HashMap::new();

        for (i, optimizer_id) in optimizers.iter().enumerate() {
            // Simple priority based on order and performance
            let base_priority = T::from(1.0 - (i as f64 / optimizers.len() as f64) * 0.5).expect("unwrap failed");
            let performance_adjustment = self.get_historical_performance_adjustment(optimizer_id)?;
            let final_priority = base_priority * performance_adjustment;

            priorities.insert(optimizer_id.clone(), final_priority);
        }

        Ok(priorities)
    }

    fn validate_optimizer_allocation(&self, _optimizer_id: &str, resources: &OptimizerResources<T>) -> Result<()> {
        // Validate individual optimizer allocation
        if resources.cpu_allocation < 0.0 {
            return Err(crate::error::OptimError::Other("Invalid CPU allocation".to_string()));
        }

        if resources.memory_allocation == 0 {
            return Err(crate::error::OptimError::Other("Zero memory allocation".to_string()));
        }

        Ok(())
    }

    fn validate_total_usage(&self, usage: &ResourceUsage) -> Result<()> {
        let available = self.available_resources.get_current_availability()?;

        if usage.total_cpu_cores > available.cpu_cores as f64 {
            return Err(crate::error::OptimError::Other("CPU allocation exceeds availability".to_string()));
        }

        if usage.total_memory_mb > available.memory_mb {
            return Err(crate::error::OptimError::Other("Memory allocation exceeds availability".to_string()));
        }

        Ok(())
    }

    fn calculate_allocation_efficiency(&self, allocation: &ResourceAllocation<T>) -> Result<T> {
        // Calculate efficiency score based on resource utilization
        let total_usage = allocation.calculate_total_usage()?;
        let available = self.available_resources.get_current_availability()?;

        let cpu_utilization = total_usage.total_cpu_cores / available.cpu_cores as f64;
        let memory_utilization = total_usage.total_memory_mb as f64 / available.memory_mb as f64;

        let efficiency = (cpu_utilization + memory_utilization) / 2.0;
        Ok(T::from(efficiency.min(1.0)).expect("unwrap failed"))
    }

    /// Reset resource manager state
    pub fn reset(&mut self) -> Result<()> {
        self.allocation_tracker.reset()?;
        self.optimization_engine.reset()?;
        self.load_balancer.reset()?;
        self.performance_monitor.reset();
        self.allocation_history.clear();
        Ok(())
    }
}

/// Resource pool management
#[derive(Debug)]
pub struct ResourcePool {
    /// Available CPU cores
    cpu_cores: usize,

    /// Available memory in MB
    memory_mb: usize,

    /// Available GPU memory in MB
    gpu_memory_mb: usize,

    /// Network bandwidth
    network_bandwidth: usize,

    /// Storage capacity
    storage_capacity: usize,
}

impl ResourcePool {
    pub fn new() -> Result<Self> {
        Ok(Self {
            cpu_cores: num_cpus::get(),
            memory_mb: Self::get_system_memory()?,
            gpu_memory_mb: 0, // Would be detected from GPU
            network_bandwidth: 1000, // MB/s
            storage_capacity: 100 * 1024, // 100 GB
        })
    }

    fn get_system_memory() -> Result<usize> {
        // Simplified memory detection
        Ok(8 * 1024) // 8 GB default
    }

    pub fn get_current_availability(&self) -> Result<ResourceAvailability> {
        Ok(ResourceAvailability {
            cpu_cores: self.cpu_cores,
            memory_mb: self.memory_mb,
            gpu_memory_mb: self.gpu_memory_mb,
            network_bandwidth: self.network_bandwidth,
            storage_capacity: self.storage_capacity,
            time_budget: Duration::from_secs(3600), // 1 hour
        })
    }
}

/// Supporting data structures

#[derive(Debug, Clone)]
pub struct ResourceAllocation<T: Float + Debug + Send + Sync + 'static> {
    pub allocations: HashMap<String, OptimizerResources<T>>,
    pub allocation_metadata: AllocationMetadata<T>,
}

impl<T: Float + Debug + Send + Sync + 'static> ResourceAllocation<T> {
    pub fn new() -> Self {
        Self {
            allocations: HashMap::new(),
            allocation_metadata: AllocationMetadata::default(),
        }
    }

    pub fn get_allocation(&self, optimizer_id: &str) -> Option<OptimizerResources<T>> {
        self.allocations.get(optimizer_id).cloned()
    }

    pub fn calculate_total_usage(&self) -> Result<ResourceUsage> {
        let mut total_cpu = 0.0;
        let mut total_memory = 0;
        let mut total_gpu = 0;

        for resources in self.allocations.values() {
            total_cpu += resources.cpu_allocation;
            total_memory += resources.memory_allocation;
            total_gpu += resources.gpu_memory_allocation;
        }

        Ok(ResourceUsage {
            total_cpu_cores: total_cpu,
            total_memory_mb: total_memory,
            total_gpu_memory_mb: total_gpu,
        })
    }
}

#[derive(Debug, Clone)]
pub struct OptimizerResources<T: Float + Debug + Send + Sync + 'static> {
    pub cpu_allocation: f64,
    pub memory_allocation: usize,
    pub gpu_memory_allocation: usize,
    pub time_allocation: Duration,
    pub priority: T,
    pub quality_of_service: QualityOfService,
}

impl<T: Float + Debug + Send + Sync + 'static + Default> Default for OptimizerResources<T> {
    fn default() -> Self {
        Self {
            cpu_allocation: 1.0,
            memory_allocation: 100,
            gpu_memory_allocation: 0,
            time_allocation: Duration::from_secs(60),
            priority: Default::default(),
            quality_of_service: QualityOfService::Standard,
        }
    }
}

#[derive(Debug, Clone)]
pub struct ResourceAvailability {
    pub cpu_cores: usize,
    pub memory_mb: usize,
    pub gpu_memory_mb: usize,
    pub network_bandwidth: usize,
    pub storage_capacity: usize,
    pub time_budget: Duration,
}

impl ResourceAvailability {
    pub fn subtract_allocated(&mut self, allocated: &ResourceUsage) -> Result<()> {
        self.cpu_cores = (self.cpu_cores as f64 - allocated.total_cpu_cores).max(0.0) as usize;
        self.memory_mb = self.memory_mb.saturating_sub(allocated.total_memory_mb);
        self.gpu_memory_mb = self.gpu_memory_mb.saturating_sub(allocated.total_gpu_memory_mb);
        Ok(())
    }

    pub fn apply_constraints(&mut self, constraints: &ResourceConstraints) -> Result<()> {
        self.cpu_cores = self.cpu_cores.min(constraints.max_cpu_cores);
        self.memory_mb = self.memory_mb.min(constraints.max_memory_mb);
        self.gpu_memory_mb = self.gpu_memory_mb.min(constraints.max_gpu_memory_mb);
        Ok(())
    }
}

#[derive(Debug, Clone)]
pub struct ResourceUsage {
    pub total_cpu_cores: f64,
    pub total_memory_mb: usize,
    pub total_gpu_memory_mb: usize,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum QualityOfService {
    Basic,
    Standard,
    Premium,
    Adaptive,
}

#[derive(Debug, Clone)]
pub struct OptimizerResourceRequirements {
    pub cpu_cores: usize,
    pub memory_mb: usize,
    pub gpu_memory_mb: usize,
    pub max_time: Duration,
    pub io_bandwidth: usize,
}

#[derive(Debug)]
pub struct ResourceConstraints {
    pub max_cpu_cores: usize,
    pub max_memory_mb: usize,
    pub max_gpu_memory_mb: usize,
    pub max_concurrent_optimizers: usize,
}

impl Default for ResourceConstraints {
    fn default() -> Self {
        Self {
            max_cpu_cores: num_cpus::get(),
            max_memory_mb: 4 * 1024, // 4 GB
            max_gpu_memory_mb: 2 * 1024, // 2 GB
            max_concurrent_optimizers: 8,
        }
    }
}

#[derive(Debug, Clone)]
pub struct AllocationRecord<T: Float + Debug + Send + Sync + 'static> {
    pub timestamp: SystemTime,
    pub allocation: ResourceAllocation<T>,
    pub allocation_time: Duration,
    pub efficiency_score: T,
}

#[derive(Debug, Clone)]
pub struct AllocationMetadata<T: Float + Debug + Send + Sync + 'static> {
    pub allocation_strategy: String,
    pub total_efficiency: T,
    pub load_balance_score: T,
}

impl<T: Float + Debug + Send + Sync + 'static> Default for AllocationMetadata<T> {
    fn default() -> Self {
        Self {
            allocation_strategy: "balanced".to_string(),
            total_efficiency: T::one(),
            load_balance_score: T::one(),
        }
    }
}

// Placeholder implementations for supporting components

#[derive(Debug)]
pub struct ResourceAllocationTracker<T: Float + Debug + Send + Sync + 'static> {
    active_allocations: HashMap<String, OptimizerResources<T>>,
    allocation_history: VecDeque<AllocationRecord<T>>,
    usage_statistics: UsageStatistics,
}

impl<T: Float + Debug + Send + Sync + 'static> ResourceAllocationTracker<T> {
    pub fn new() -> Result<Self> {
        Ok(Self {
            active_allocations: HashMap::new(),
            allocation_history: VecDeque::new(),
            usage_statistics: UsageStatistics::new(),
        })
    }

    pub fn reserve_resources(&mut self, allocation: &ResourceAllocation<T>) -> Result<()> {
        for (optimizer_id, resources) in &allocation.allocations {
            self.active_allocations.insert(optimizer_id.clone(), resources.clone());
        }
        Ok(())
    }

    pub fn get_total_allocated_resources(&self) -> Result<ResourceUsage> {
        let mut total_cpu = 0.0;
        let mut total_memory = 0;
        let mut total_gpu = 0;

        for resources in self.active_allocations.values() {
            total_cpu += resources.cpu_allocation;
            total_memory += resources.memory_allocation;
            total_gpu += resources.gpu_memory_allocation;
        }

        Ok(ResourceUsage {
            total_cpu_cores: total_cpu,
            total_memory_mb: total_memory,
            total_gpu_memory_mb: total_gpu,
        })
    }

    pub fn get_historical_usage(&self, _optimizer_id: &str) -> Result<HistoricalUsage> {
        Ok(HistoricalUsage {
            average_cpu_efficiency: 0.8,
            average_memory_efficiency: 0.9,
            total_allocations: 10,
        })
    }

    pub fn reset(&mut self) -> Result<()> {
        self.active_allocations.clear();
        self.allocation_history.clear();
        self.usage_statistics = UsageStatistics::new();
        Ok(())
    }
}

#[derive(Debug)]
pub struct ResourceOptimizationEngine<T: Float + Debug + Send + Sync + 'static> {
    optimization_algorithms: Vec<Box<dyn OptimizationAlgorithm<T>>>,
}

impl<T: Float + Debug + Send + Sync + 'static> ResourceOptimizationEngine<T> {
    pub fn new() -> Result<Self> {
        Ok(Self {
            optimization_algorithms: Vec::new(),
        })
    }

    pub fn optimize_allocation(
        &mut self,
        allocation: &ResourceAllocation<T>,
        _requirements: &HashMap<String, OptimizerResourceRequirements>,
        _availability: &ResourceAvailability,
    ) -> Result<ResourceAllocation<T>> {
        // For now, return the allocation as-is
        Ok(allocation.clone())
    }

    pub fn reset(&mut self) -> Result<()> {
        self.optimization_algorithms.clear();
        Ok(())
    }
}

#[derive(Debug)]
pub struct LoadBalancer<T: Float + Debug + Send + Sync + 'static> {
    balancing_strategy: LoadBalancingStrategy,
    load_metrics: LoadMetrics<T>,
}

impl<T: Float + Debug + Send + Sync + 'static> LoadBalancer<T> {
    pub fn new() -> Result<Self> {
        Ok(Self {
            balancing_strategy: LoadBalancingStrategy::RoundRobin,
            load_metrics: LoadMetrics::new(),
        })
    }

    pub fn balance_allocation(
        &mut self,
        allocation: &ResourceAllocation<T>,
        _availability: &ResourceAvailability,
    ) -> Result<ResourceAllocation<T>> {
        // Simple load balancing - return as-is for now
        Ok(allocation.clone())
    }

    pub fn apply_dynamic_balancing(
        &mut self,
        _allocation: &mut ResourceAllocation<T>,
        _availability: &ResourceAvailability,
    ) -> Result<()> {
        // Dynamic balancing adjustments
        Ok(())
    }

    pub fn reset(&mut self) -> Result<()> {
        self.load_metrics = LoadMetrics::new();
        Ok(())
    }
}

#[derive(Debug)]
pub struct ResourcePerformanceMonitor<T: Float + Debug + Send + Sync + 'static> {
    performance_history: HashMap<String, VecDeque<f64>>,
    allocation_records: VecDeque<AllocationRecord<T>>,
}

impl<T: Float + Debug + Send + Sync + 'static> ResourcePerformanceMonitor<T> {
    pub fn new() -> Self {
        Self {
            performance_history: HashMap::new(),
            allocation_records: VecDeque::new(),
        }
    }

    pub fn record_allocation(&mut self, allocation: &ResourceAllocation<T>, _duration: Duration) -> Result<()> {
        // Record allocation for monitoring
        let record = AllocationRecord {
            timestamp: SystemTime::now(),
            allocation: allocation.clone(),
            allocation_time: _duration,
            efficiency_score: scirs2_core::numeric::NumCast::from(0.8).unwrap_or_else(|| T::zero()), // Placeholder
        };

        self.allocation_records.push_back(record);
        if self.allocation_records.len() > 1000 {
            self.allocation_records.pop_front();
        }

        Ok(())
    }

    pub fn get_optimizer_performance(&self, optimizer_id: &str) -> Result<f64> {
        Ok(self.performance_history.get(optimizer_id)
            .and_then(|history| history.back())
            .cloned()
            .unwrap_or(0.7))
    }

    pub fn predict_future_availability(
        &self,
        current: &ResourceAvailability,
        _horizon: Duration,
    ) -> Result<ResourceAvailability> {
        // Simple prediction - return current availability
        Ok(current.clone())
    }

    pub fn reset(&mut self) {
        self.performance_history.clear();
        self.allocation_records.clear();
    }
}

// Additional supporting types
#[derive(Debug)]
pub struct UsageStatistics {
    pub total_cpu_time: Duration,
    pub total_memory_usage: usize,
    pub allocation_count: usize,
}

impl UsageStatistics {
    pub fn new() -> Self {
        Self {
            total_cpu_time: Duration::new(0, 0),
            total_memory_usage: 0,
            allocation_count: 0,
        }
    }
}

#[derive(Debug)]
pub struct HistoricalUsage {
    pub average_cpu_efficiency: f64,
    pub average_memory_efficiency: f64,
    pub total_allocations: usize,
}

impl HistoricalUsage {
    pub fn is_empty(&self) -> bool {
        self.total_allocations == 0
    }
}

pub trait OptimizationAlgorithm<T: Float + Debug + Send + Sync + 'static>: Send + Sync + std::fmt::Debug {
    fn optimize(&mut self, allocation: &ResourceAllocation<T>) -> Result<ResourceAllocation<T>>;
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum LoadBalancingStrategy {
    RoundRobin,
    LeastLoaded,
    WeightedRoundRobin,
    Dynamic,
}

#[derive(Debug)]
pub struct LoadMetrics<T: Float + Debug + Send + Sync + 'static> {
    pub current_load: T,
    pub load_history: VecDeque<T>,
    pub load_variance: T,
}

impl<T: Float + Debug + Send + Sync + 'static> LoadMetrics<T> {
    pub fn new() -> Self {
        Self {
            current_load: T::zero(),
            load_history: VecDeque::new(),
            load_variance: T::zero(),
        }
    }
}

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

    #[test]
    fn test_resource_manager_creation() {
        let manager = ResourceManager::<f32>::new();
        assert!(manager.is_ok());
    }

    #[test]
    fn test_resource_pool_creation() {
        let pool = ResourcePool::new();
        assert!(pool.is_ok());

        let p = pool.expect("unwrap failed");
        assert!(p.cpu_cores > 0);
        assert!(p.memory_mb > 0);
    }

    #[test]
    fn test_resource_allocation() {
        let mut allocation = ResourceAllocation::<f32>::new();
        let resources = OptimizerResources::default();
        allocation.allocations.insert("test_optimizer".to_string(), resources);

        let usage = allocation.calculate_total_usage();
        assert!(usage.is_ok());
    }

    #[test]
    fn test_resource_availability() {
        let mut availability = ResourceAvailability {
            cpu_cores: 8,
            memory_mb: 1024,
            gpu_memory_mb: 512,
            network_bandwidth: 1000,
            storage_capacity: 10000,
            time_budget: Duration::from_secs(3600),
        };

        let usage = ResourceUsage {
            total_cpu_cores: 2.0,
            total_memory_mb: 256,
            total_gpu_memory_mb: 128,
        };

        assert!(availability.subtract_allocated(&usage).is_ok());
        assert_eq!(availability.cpu_cores, 6);
        assert_eq!(availability.memory_mb, 768);
    }
}