scirs2-core 0.4.2

//! Tensor operations, performance prediction, and adaptive scheduling
//!
//! This module contains components for predicting tensor operation performance,
//! adaptive scheduling, resource allocation, and load balancing across devices.

use super::*;
use crate::error::{CoreError, CoreResult};

#[cfg(feature = "gpu")]
use std::collections::HashMap;
#[cfg(feature = "gpu")]
use std::time::{Duration, Instant};

#[cfg(feature = "gpu")]
use crate::gpu::{
    auto_tuning::{KernelParameters, PerformanceMetrics, TuningResult},
    tensor_cores::{TensorCoreConfig, TensorOperation},
    GpuBackend,
};

#[cfg(all(feature = "serde", feature = "gpu"))]
#[cfg(feature = "serialization")]
use serde::{Deserialize, Serialize};

/// Performance predictor for optimization guidance
#[allow(dead_code)]
#[derive(Debug)]
pub struct PerformancePredictor {
    /// Prediction models
    prediction_models: HashMap<String, PredictionModel>,
    /// Historical data
    historical_data: Vec<super::PerformanceDataPoint>,
    /// Prediction accuracy
    prediction_accuracy: HashMap<String, f64>,
    /// Model selection criteria
    model_selection: ModelSelectionCriteria,
}

/// Prediction model for performance estimation
#[allow(dead_code)]
#[derive(Debug)]
pub struct PredictionModel {
    /// Model type
    model_type: PredictionModelType,
    /// Model parameters
    parameters: Vec<f64>,
    /// Feature importance
    feature_importance: HashMap<String, f64>,
    /// Prediction confidence
    confidence_intervals: ConfidenceIntervals,
}

/// Types of prediction models
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum PredictionModelType {
    LinearRegression,
    PolynomialRegression,
    RandomForestRegressor,
    GradientBoosting,
    NeuralNetworkRegressor,
    SupportVectorRegression,
    GaussianProcessRegression,
}

/// Confidence intervals for predictions
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConfidenceIntervals {
    /// Lower bound
    pub lower_bound: f64,
    /// Upper bound
    pub upper_bound: f64,
    /// Confidence level
    pub confidence_level: f64,
}

/// Model selection criteria
#[allow(dead_code)]
#[derive(Debug)]
pub struct ModelSelectionCriteria {
    /// Cross-validation folds
    pub cv_folds: usize,
    /// Scoring metrics
    pub scoring_metrics: Vec<ScoringMetric>,
    /// Model complexity penalty
    pub complexity_penalty: f64,
    /// Selection strategy
    pub selection_strategy: SelectionStrategy,
}

/// Scoring metrics for model evaluation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ScoringMetric {
    MeanSquaredError,
    MeanAbsoluteError,
    RSquared,
    AdjustedRSquared,
    CrossValidationScore,
    InformationCriteria,
}

/// Model selection strategies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum SelectionStrategy {
    BestScore,
    EnsembleAveraging,
    BayesianModelAveraging,
    StackedGeneralization,
}

/// Performance prediction result
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformancePrediction {
    /// Predicted execution time
    pub predicted_execution_time: Duration,
    /// Predicted throughput
    pub predicted_throughput: f64,
    /// Predicted memory usage
    pub predicted_memory_usage: f64,
    /// Predicted power consumption
    pub predicted_power_consumption: f64,
    /// Confidence interval
    pub confidence_interval: (f64, f64),
    /// Prediction accuracy
    pub prediction_accuracy: f64,
}

/// Adaptive scheduler for intelligent workload management
#[allow(dead_code)]
#[derive(Debug)]
pub struct AdaptiveScheduler {
    /// Scheduling strategies
    scheduling_strategies: HashMap<String, SchedulingStrategy>,
    /// Resource allocation
    #[allow(dead_code)]
    resource_allocator: ResourceAllocator,
    /// Load balancer
    #[allow(dead_code)]
    load_balancer: LoadBalancer,
    /// Priority manager
    #[allow(dead_code)]
    priority_manager: PriorityManager,
    /// Scheduling history
    #[allow(dead_code)]
    scheduling_history: Vec<SchedulingDecision>,
}

/// Scheduling strategy
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SchedulingStrategy {
    /// Strategy name
    pub name: String,
    /// Algorithm type
    pub algorithm: SchedulingAlgorithm,
    /// Parameters
    pub parameters: HashMap<String, f64>,
    /// Effectiveness score
    pub effectiveness: f64,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
}

/// Scheduling algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum SchedulingAlgorithm {
    FirstComeFirstServe,
    ShortestJobFirst,
    PriorityBased,
    RoundRobin,
    MultilevelFeedback,
    DeadlineMonotonic,
    EarliestDeadlineFirst,
    ProportionalShare,
}

/// Resource requirements specification
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    /// Compute requirements
    pub compute_units: usize,
    /// Memory requirements
    pub memory_gb: f64,
    /// Bandwidth requirements
    pub bandwidth_gbps: f64,
    /// Energy requirements
    pub energy_budget_joules: f64,
    /// Latency requirements
    pub max_latency_ms: f64,
}

/// Resource allocator for efficient resource management
#[allow(dead_code)]
#[derive(Debug)]
pub struct ResourceAllocator {
    /// Available resources
    #[allow(dead_code)]
    available_resources: HashMap<GpuBackend, AvailableResources>,
    /// Allocation strategies
    #[allow(dead_code)]
    allocation_strategies: Vec<AllocationStrategy>,
    /// Resource utilization
    #[allow(dead_code)]
    resource_utilization: ResourceUtilization,
    /// Allocation history
    #[allow(dead_code)]
    allocation_history: Vec<AllocationDecision>,
}

/// Available resources on a device
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AvailableResources {
    /// Available compute units
    pub compute_units: usize,
    /// Available memory
    pub memory_gb: f64,
    /// Available bandwidth
    pub bandwidth_gbps: f64,
    /// Power budget
    pub power_budget_watts: f64,
    /// Thermal headroom
    pub thermal_headroom_celsius: f64,
}

/// Resource allocation strategies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum AllocationStrategy {
    BestFit,
    FirstFit,
    WorstFit,
    NextFit,
    BuddySystem,
    SlabAllocation,
}

/// Resource utilization tracking
#[allow(dead_code)]
#[derive(Debug, Clone, Default)]
pub struct ResourceUtilization {
    /// Compute utilization
    pub compute_utilization: HashMap<GpuBackend, f64>,
    /// Memory utilization
    pub memory_utilization: HashMap<GpuBackend, f64>,
    /// Bandwidth utilization
    pub bandwidth_utilization: HashMap<GpuBackend, f64>,
    /// Power utilization
    pub power_utilization: HashMap<GpuBackend, f64>,
}

/// Resource allocation decision
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AllocationDecision {
    /// Request ID
    pub request_id: String,
    /// Allocated device
    pub device: GpuBackend,
    /// Allocated resources
    pub allocated_resources: AllocatedResources,
    /// Allocation time
    pub allocation_time: Instant,
    /// Expected duration
    pub expected_duration: Duration,
}

/// Allocated resources
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AllocatedResources {
    /// Compute units allocated
    pub compute_units: usize,
    /// Memory allocated
    pub memory_gb: f64,
    /// Bandwidth allocated
    pub bandwidth_gbps: f64,
    /// Power allocated
    pub power_watts: f64,
}

/// Load balancer for multi-device coordination
#[allow(dead_code)]
#[derive(Debug)]
pub struct LoadBalancer {
    /// Load balancing algorithm
    #[allow(dead_code)]
    algorithm: LoadBalancingAlgorithm,
    /// Device loads
    #[allow(dead_code)]
    device_loads: HashMap<GpuBackend, DeviceLoad>,
    /// Balancing history
    #[allow(dead_code)]
    balancing_history: Vec<BalancingDecision>,
    /// Performance metrics
    #[allow(dead_code)]
    balancing_metrics: BalancingMetrics,
}

/// Load balancing algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum LoadBalancingAlgorithm {
    RoundRobin,
    LeastConnections,
    WeightedRoundRobin,
    ResourceBased,
    ResponseTimeBased,
    AdaptiveWeighted,
}

/// Device load information
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct DeviceLoad {
    /// Current workload
    pub current_workload: f64,
    /// Queue length
    pub queue_length: usize,
    /// Response time
    pub response_time: Duration,
    /// Utilization metrics
    pub utilization: ResourceUtilization,
}

/// Load balancing decision
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct BalancingDecision {
    /// Source device
    pub source_device: GpuBackend,
    /// Target device
    pub target_device: GpuBackend,
    /// Workload transferred
    pub workload_size: f64,
    /// Decision time
    pub decision_time: Instant,
    /// Reason for balancing
    pub reason: String,
}

/// Load balancing performance metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct BalancingMetrics {
    /// Load variance across devices
    pub load_variance: f64,
    /// Average response time
    pub avg_response_time: Duration,
    /// Throughput
    pub throughput: f64,
    /// Balancing efficiency
    pub balancing_efficiency: f64,
}

/// Priority manager for task prioritization
#[allow(dead_code)]
#[derive(Debug)]
pub struct PriorityManager {
    /// Priority algorithms
    #[allow(dead_code)]
    priority_algorithms: Vec<PriorityAlgorithm>,
    /// Task priorities
    #[allow(dead_code)]
    task_priorities: HashMap<String, TaskPriority>,
    /// Priority adjustments
    #[allow(dead_code)]
    priority_adjustments: Vec<PriorityAdjustment>,
    /// Fairness metrics
    #[allow(dead_code)]
    fairness_metrics: FairnessMetrics,
}

/// Priority assignment algorithms
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum PriorityAlgorithm {
    FixedPriority,
    DynamicPriority,
    AgeBasedPriority,
    DeadlineBasedPriority,
    ResourceBasedPriority,
    MLBasedPriority,
}

/// Task priority information
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct TaskPriority {
    /// Base priority
    pub base_priority: u8,
    /// Dynamic adjustment
    pub dynamic_adjustment: i8,
    /// Priority reason
    pub reason: String,
    /// Last adjustment time
    pub last_adjustment: Instant,
}

/// Priority adjustment record
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PriorityAdjustment {
    /// Task ID
    pub task_id: String,
    /// Old priority
    pub old_priority: u8,
    /// New priority
    pub new_priority: u8,
    /// Adjustment reason
    pub reason: String,
    /// Adjustment time
    pub timestamp: Instant,
}

/// Fairness metrics for priority management
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct FairnessMetrics {
    /// Gini coefficient
    pub gini_coefficient: f64,
    /// Jain's fairness index
    pub jains_index: f64,
    /// Average waiting time
    pub avg_waiting_time: Duration,
    /// Starvation incidents
    pub starvation_count: usize,
}

/// Scheduling decision record
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SchedulingDecision {
    /// Task ID
    pub task_id: String,
    /// Scheduled device
    pub device: GpuBackend,
    /// Scheduling time
    pub schedule_time: Instant,
    /// Expected completion time
    pub expected_completion: Instant,
    /// Actual completion time
    pub actual_completion: Option<Instant>,
    /// Performance achieved
    pub performance: Option<PerformanceMetrics>,
}

/// Optimized tensor operation result
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct OptimizedTensorOperation {
    /// Original operation
    pub original_operation: TensorOperation,
    /// Optimized tensor core configuration
    pub optimized_config: TensorCoreConfig,
    /// Optimized kernel parameters
    pub kernel_params: KernelParameters,
    /// Predicted performance
    pub predicted_performance: PerformanceMetrics,
    /// Optimization strategy used
    pub optimization_strategy: String,
    /// Confidence score (0.0 to 1.0)
    pub confidence_score: f64,
}

// Implementation blocks

impl PerformancePredictor {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            prediction_models: HashMap::new(),
            historical_data: Vec::new(),
            prediction_accuracy: HashMap::new(),
            model_selection: ModelSelectionCriteria {
                cv_folds: 5,
                scoring_metrics: vec![ScoringMetric::MeanSquaredError],
                complexity_penalty: 0.01,
                selection_strategy: SelectionStrategy::BestScore,
            },
        })
    }

    pub fn predict_performance(
        &self,
        kernel_params: &KernelParameters,
    ) -> CoreResult<PerformancePrediction> {
        // Advanced performance prediction using machine learning models

        // Extract features from kernel parameters
        let features = self.extract_features_from_kernel(kernel_params)?;

        // Use ensemble of prediction models for robustness
        let mut predictions = Vec::new();

        for (model_name, model) in &self.prediction_models {
            if let Ok(pred) = self.predict_with_model(model, &features) {
                predictions.push(pred);
            }
        }

        // If no models available, use heuristic prediction
        if predictions.is_empty() {
            return self.heuristic_prediction(kernel_params);
        }

        // Ensemble prediction - weighted average based on model accuracy
        let weighted_prediction = self.ensemble_prediction(&predictions)?;

        Ok(weighted_prediction)
    }

    fn extract_features_from_kernel(
        &self,
        kernel_params: &KernelParameters,
    ) -> CoreResult<Vec<f64>> {
        // Extract numerical features from kernel parameters
        let mut features = Vec::new();

        // Basic kernel features - map from actual KernelParameters fields
        features.push(kernel_params.work_group_size[0] as f64); // block_size.0 equivalent
        features.push(kernel_params.work_group_size[1] as f64); // block_size.1 equivalent
        features.push(kernel_params.global_work_size[0] as f64); // grid_size.0 equivalent
        features.push(kernel_params.global_work_size[1] as f64); // grid_size.1 equivalent
        features.push(if kernel_params.local_memory_size > 0 {
            1.0
        } else {
            0.0
        }); // use_shared_memory equivalent
        features.push(1.0); // optimization_level placeholder

        // Advanced features based on kernel parameters
        let compute_density = (kernel_params.work_group_size[0] * kernel_params.work_group_size[1])
            as f64
            / (kernel_params.global_work_size[0] * kernel_params.global_work_size[1]) as f64;
        features.push(compute_density);

        // Memory access pattern features
        let memory_coalescing_factor = if kernel_params.local_memory_size > 0 {
            0.9
        } else {
            0.6
        };
        features.push(memory_coalescing_factor);

        Ok(features)
    }

    fn predict_with_model(
        &self,
        model: &PredictionModel,
        features: &[f64],
    ) -> CoreResult<PerformancePrediction> {
        // Simplified model prediction - in practice this would use actual ML models
        match model.model_type {
            PredictionModelType::LinearRegression => {
                self.linear_regression_predict(model, features)
            }
            PredictionModelType::RandomForestRegressor => {
                self.random_forest_predict(model, features)
            }
            PredictionModelType::NeuralNetworkRegressor => {
                self.neural_network_predict(model, features)
            }
            _ => {
                // Fallback to simple heuristic
                let complexity = features.iter().sum::<f64>() / features.len() as f64;
                Ok(PerformancePrediction {
                    predicted_execution_time: Duration::from_millis((complexity * 10.0) as u64),
                    predicted_throughput: 1000.0 / complexity,
                    predicted_memory_usage: complexity * 100.0,
                    predicted_power_consumption: complexity * 50.0,
                    confidence_interval: (0.8, 1.2),
                    prediction_accuracy: 0.75,
                })
            }
        }
    }

    fn linear_regression_predict(
        &self,
        model: &PredictionModel,
        features: &[f64],
    ) -> CoreResult<PerformancePrediction> {
        // Simple linear regression prediction
        let mut prediction = 0.0;
        for (i, &feature) in features.iter().enumerate() {
            if i < model.parameters.len() {
                prediction += feature * model.parameters[i];
            }
        }

        Ok(PerformancePrediction {
            predicted_execution_time: Duration::from_millis(prediction.max(1.0) as u64),
            predicted_throughput: 1000.0 / prediction.max(1.0),
            predicted_memory_usage: prediction * 50.0,
            predicted_power_consumption: prediction * 25.0,
            confidence_interval: (prediction * 0.9, prediction * 1.1),
            prediction_accuracy: 0.8,
        })
    }

    fn random_forest_predict(
        &self,
        _model: &PredictionModel,
        features: &[f64],
    ) -> CoreResult<PerformancePrediction> {
        // Simplified random forest prediction
        let complexity = features.iter().map(|f| f.abs()).sum::<f64>() / features.len() as f64;
        let base_time = complexity * 5.0;

        Ok(PerformancePrediction {
            predicted_execution_time: Duration::from_millis(base_time as u64),
            predicted_throughput: 2000.0 / base_time,
            predicted_memory_usage: complexity * 75.0,
            predicted_power_consumption: complexity * 30.0,
            confidence_interval: (base_time * 0.85, base_time * 1.15),
            prediction_accuracy: 0.85,
        })
    }

    fn neural_network_predict(
        &self,
        _model: &PredictionModel,
        features: &[f64],
    ) -> CoreResult<PerformancePrediction> {
        // Simplified neural network prediction with non-linear activation
        let mut output = 0.0;
        for &feature in features {
            // Simple sigmoid-like activation
            output += 1.0 / (1.0 + (-feature * 0.1).exp());
        }
        output *= 20.0; // Scale factor

        Ok(PerformancePrediction {
            predicted_execution_time: Duration::from_millis(output as u64),
            predicted_throughput: 3000.0 / output.max(1.0),
            predicted_memory_usage: output * 60.0,
            predicted_power_consumption: output * 35.0,
            confidence_interval: (output * 0.9, output * 1.1),
            prediction_accuracy: 0.88,
        })
    }

    fn ensemble_prediction(
        &self,
        predictions: &[PerformancePrediction],
    ) -> CoreResult<PerformancePrediction> {
        if predictions.is_empty() {
            return Err(CoreError::InvalidInput(crate::error::ErrorContext::new(
                "No predictions available for ensemble",
            )));
        }

        let n = predictions.len() as f64;

        // Weighted average based on prediction accuracy
        let total_weight: f64 = predictions.iter().map(|p| p.prediction_accuracy).sum();

        let mut weighted_time = 0.0;
        let mut weighted_throughput = 0.0;
        let mut weighted_memory = 0.0;
        let mut weighted_power = 0.0;
        let mut avg_accuracy = 0.0;

        for pred in predictions {
            let weight = pred.prediction_accuracy / total_weight;
            weighted_time += pred.predicted_execution_time.as_millis() as f64 * weight;
            weighted_throughput += pred.predicted_throughput * weight;
            weighted_memory += pred.predicted_memory_usage * weight;
            weighted_power += pred.predicted_power_consumption * weight;
            avg_accuracy += pred.prediction_accuracy;
        }

        avg_accuracy /= n;

        Ok(PerformancePrediction {
            predicted_execution_time: Duration::from_millis(weighted_time as u64),
            predicted_throughput: weighted_throughput,
            predicted_memory_usage: weighted_memory,
            predicted_power_consumption: weighted_power,
            confidence_interval: (weighted_throughput * 0.9, weighted_throughput * 1.1),
            prediction_accuracy: avg_accuracy,
        })
    }

    fn heuristic_prediction(
        &self,
        kernel_params: &KernelParameters,
    ) -> CoreResult<PerformancePrediction> {
        // Fallback heuristic when no trained models are available
        let total_threads = (kernel_params.work_group_size[0]
            * kernel_params.work_group_size[1]
            * kernel_params.global_work_size[0]
            * kernel_params.global_work_size[1]) as f64;

        let base_time_ms = (total_threads / 1000.0)
            * if kernel_params.local_memory_size > 0 {
                0.8
            } else {
                1.0
            };
        let estimated_throughput = total_threads * 100.0 / base_time_ms;

        Ok(PerformancePrediction {
            predicted_execution_time: Duration::from_millis(base_time_ms as u64),
            predicted_throughput: estimated_throughput,
            predicted_memory_usage: total_threads * 4.0, // 4 bytes per thread estimate
            predicted_power_consumption: total_threads * 0.01, // 0.01W per thread estimate
            confidence_interval: (estimated_throughput * 0.7, estimated_throughput * 1.3),
            prediction_accuracy: 0.6, // Lower confidence for heuristic
        })
    }

    /// Add historical performance data for model training
    pub fn add_historical_data(&mut self, data: super::PerformanceDataPoint) {
        self.historical_data.push(data);

        // Limit historical data size to prevent memory bloat
        if self.historical_data.len() > 10000 {
            self.historical_data.remove(0);
        }
    }

    /// Update prediction accuracy based on actual results
    pub fn update_accuracy(
        &mut self,
        model_name: &str,
        actual_performance: &PerformanceMetrics,
        predicted: &PerformancePrediction,
    ) {
        let actual_throughput = actual_performance.throughput;
        let predicted_throughput = predicted.predicted_throughput;

        let accuracy =
            1.0 - (actual_throughput - predicted_throughput).abs() / actual_throughput.max(1.0);

        // Update running average of accuracy
        let current_accuracy = self.prediction_accuracy.get(model_name).unwrap_or(&0.5);
        let new_accuracy = (current_accuracy + accuracy) / 2.0;
        self.prediction_accuracy
            .insert(model_name.to_string(), new_accuracy);
    }
}

impl AdaptiveScheduler {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            scheduling_strategies: HashMap::new(),
            resource_allocator: ResourceAllocator::new()?,
            load_balancer: LoadBalancer::new()?,
            priority_manager: PriorityManager::new()?,
            scheduling_history: Vec::new(),
        })
    }

    pub fn update_scheduling_policy(
        &mut self,
        _backend: GpuBackend,
        _kernel: &str,
        _result: &TuningResult,
    ) -> CoreResult<()> {
        // Update scheduling policy based on tuning results
        // This would analyze the performance and adjust scheduling strategies
        Ok(())
    }

    /// Schedule a tensor operation on the best available device
    pub fn schedule_operation(
        &mut self,
        operation: &TensorOperation,
    ) -> CoreResult<SchedulingDecision> {
        // Analyze operation requirements
        let requirements = self.analyze_operation_requirements(operation)?;

        // Find best device based on current load and capabilities
        let best_device = self.find_best_device(&requirements)?;

        // Create scheduling decision
        let decision = SchedulingDecision {
            task_id: format!("task_{}", operation.dimensions.0), // batch_size equivalent
            device: best_device,
            schedule_time: Instant::now(),
            expected_completion: Instant::now() + Duration::from_millis(100),
            actual_completion: None,
            performance: None,
        };

        self.scheduling_history.push(decision.clone());
        Ok(decision)
    }

    fn analyze_operation_requirements(
        &self,
        operation: &TensorOperation,
    ) -> CoreResult<ResourceRequirements> {
        // Analyze tensor operation to determine resource requirements
        let compute_complexity =
            operation.dimensions.0 * operation.dimensions.1 * operation.dimensions.2; // batch_size * sequence_length * hidden_size

        Ok(ResourceRequirements {
            compute_units: (compute_complexity / 1000).max(1),
            memory_gb: (compute_complexity as f64 * 4.0) / (1024.0 * 1024.0 * 1024.0), // 4 bytes per element
            bandwidth_gbps: 100.0,                                                     // Estimate
            energy_budget_joules: compute_complexity as f64 * 0.001,
            max_latency_ms: 100.0,
        })
    }

    fn find_best_device(&self, _requirements: &ResourceRequirements) -> CoreResult<GpuBackend> {
        // Simple device selection - in practice this would consider actual device capabilities
        // and current load from the load balancer
        Ok(GpuBackend::Cuda) // Default to CUDA for simplicity
    }
}

impl ResourceAllocator {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            available_resources: HashMap::new(),
            allocation_strategies: vec![AllocationStrategy::BestFit],
            resource_utilization: ResourceUtilization {
                compute_utilization: HashMap::new(),
                memory_utilization: HashMap::new(),
                bandwidth_utilization: HashMap::new(),
                power_utilization: HashMap::new(),
            },
            allocation_history: Vec::new(),
        })
    }

    /// Allocate resources for a specific request
    pub fn allocate_resources(
        &mut self,
        request_id: String,
        requirements: &ResourceRequirements,
    ) -> CoreResult<AllocationDecision> {
        // Find best device that can satisfy requirements
        let best_device = self.find_suitable_device(requirements)?;

        // Create allocation
        let allocation = AllocatedResources {
            compute_units: requirements.compute_units,
            memory_gb: requirements.memory_gb,
            bandwidth_gbps: requirements.bandwidth_gbps,
            power_watts: requirements.energy_budget_joules, // Simplified mapping
        };

        let decision = AllocationDecision {
            request_id,
            device: best_device,
            allocated_resources: allocation,
            allocation_time: Instant::now(),
            expected_duration: Duration::from_millis(100),
        };

        self.allocation_history.push(decision.clone());
        Ok(decision)
    }

    fn find_suitable_device(&self, _requirements: &ResourceRequirements) -> CoreResult<GpuBackend> {
        // Simple device selection based on available resources
        // In practice, this would check actual resource availability
        Ok(GpuBackend::Cuda)
    }

    /// Update resource utilization
    pub fn update_utilization(&mut self, device: GpuBackend, utilization: ResourceUtilization) {
        self.resource_utilization = utilization;
    }
}

impl LoadBalancer {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            algorithm: LoadBalancingAlgorithm::AdaptiveWeighted,
            device_loads: HashMap::new(),
            balancing_history: Vec::new(),
            balancing_metrics: BalancingMetrics {
                load_variance: 0.1,
                avg_response_time: Duration::from_millis(10),
                throughput: 1000.0,
                balancing_efficiency: 0.9,
            },
        })
    }

    /// Update device load information
    pub fn update_device_load(&mut self, device: GpuBackend, load: DeviceLoad) {
        self.device_loads.insert(device, load);
    }

    /// Make load balancing decision
    pub fn balance_load(&mut self) -> CoreResult<Option<BalancingDecision>> {
        // Check if load balancing is needed
        if !self.needs_balancing()? {
            return Ok(None);
        }

        // Find source and target devices
        let (source, target) = self.find_balancing_pair()?;

        let decision = BalancingDecision {
            source_device: source,
            target_device: target,
            workload_size: 0.3, // Move 30% of workload
            decision_time: Instant::now(),
            reason: "Load imbalance detected".to_string(),
        };

        self.balancing_history.push(decision.clone());
        Ok(Some(decision))
    }

    fn needs_balancing(&self) -> CoreResult<bool> {
        // Check if load variance exceeds threshold
        Ok(self.balancing_metrics.load_variance > 0.2)
    }

    fn find_balancing_pair(&self) -> CoreResult<(GpuBackend, GpuBackend)> {
        // Simple implementation - find highest and lowest loaded devices
        // In practice, this would be more sophisticated
        Ok((GpuBackend::Cuda, GpuBackend::OpenCL))
    }
}

impl PriorityManager {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            priority_algorithms: vec![PriorityAlgorithm::MLBasedPriority],
            task_priorities: HashMap::new(),
            priority_adjustments: Vec::new(),
            fairness_metrics: FairnessMetrics {
                gini_coefficient: 0.3,
                jains_index: 0.8,
                avg_waiting_time: Duration::from_millis(50),
                starvation_count: 0,
            },
        })
    }

    /// Assign priority to a task
    pub fn assign_priority(
        &mut self,
        task_id: String,
        base_priority: u8,
    ) -> CoreResult<TaskPriority> {
        let priority = TaskPriority {
            base_priority,
            dynamic_adjustment: 0,
            reason: "Initial assignment".to_string(),
            last_adjustment: Instant::now(),
        };

        self.task_priorities.insert(task_id, priority.clone());
        Ok(priority)
    }

    /// Adjust task priority based on conditions
    pub fn adjust_priority(
        &mut self,
        task_id: &str,
        adjustment: i8,
        reason: String,
    ) -> CoreResult<()> {
        if let Some(priority) = self.task_priorities.get_mut(task_id) {
            let old_priority = priority.base_priority;
            priority.dynamic_adjustment = adjustment;
            priority.reason = reason.clone();
            priority.last_adjustment = Instant::now();

            let adjustment_record = PriorityAdjustment {
                task_id: task_id.to_string(),
                old_priority,
                new_priority: (old_priority as i16 + adjustment as i16).clamp(0, 255) as u8,
                reason,
                timestamp: Instant::now(),
            };

            self.priority_adjustments.push(adjustment_record);
        }

        Ok(())
    }
}

impl Default for ConfidenceIntervals {
    fn default() -> Self {
        Self {
            lower_bound: 0.0,
            upper_bound: 1.0,
            confidence_level: 0.95,
        }
    }
}

impl Default for ResourceRequirements {
    fn default() -> Self {
        Self {
            compute_units: 1,
            memory_gb: 1.0,
            bandwidth_gbps: 10.0,
            energy_budget_joules: 100.0,
            max_latency_ms: 100.0,
        }
    }
}