scirs2_core/

advanced_ecosystem_integration.rs

//! Advanced Mode Ecosystem Integration
//!
//! This module provides comprehensive integration testing and coordination for all
//! scirs2-* modules operating in Advanced mode. It enables intelligent cross-module
//! communication, performance optimization, and unified orchestration of advanced
//! AI-driven scientific computing capabilities.
//!
//! # Features
//!
//! - **Cross-Module Communication**: Seamless data flow between Advanced modules
//! - **Unified Performance Optimization**: Global optimization across the ecosystem
//! - **Intelligent Resource Management**: Coordinated CPU/GPU/QPU allocation
//! - **Adaptive Load Balancing**: Dynamic workload distribution
//! - **Real-time Monitoring**: Performance tracking across all modules
//! - **Fault Tolerance**: Automatic recovery and failover mechanisms
//! - **API Compatibility**: Unified interface for all Advanced capabilities

use crate::distributed::{
    cluster::{SpecializedRequirement, SpecializedUnit},
    ResourceRequirements,
};
use crate::error::{CoreError, CoreResult, ErrorContext};
use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::thread;
use std::time::{Duration, Instant};

use serde::{Deserialize, Serialize};

/// Central coordinator for advanced mode ecosystem
#[allow(dead_code)]
#[derive(Debug)]
pub struct AdvancedEcosystemCoordinator {
    /// Registered advanced modules
    modules: Arc<RwLock<HashMap<String, Box<dyn AdvancedModule + Send + Sync>>>>,
    /// Performance monitor
    performancemonitor: Arc<Mutex<EcosystemPerformanceMonitor>>,
    /// Resource manager
    resource_manager: Arc<Mutex<EcosystemResourceManager>>,
    /// Communication hub
    communication_hub: Arc<Mutex<ModuleCommunicationHub>>,
    /// Configuration
    config: AdvancedEcosystemConfig,
    /// Status tracker
    status: Arc<RwLock<EcosystemStatus>>,
}

/// Configuration for advanced ecosystem
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedEcosystemConfig {
    /// Enable cross-module optimization
    pub enable_cross_module_optimization: bool,
    /// Enable adaptive load balancing
    pub enable_adaptive_load_balancing: bool,
    /// Enable fault tolerance
    pub enable_fault_tolerance: bool,
    /// Maximum memory usage per module (MB)
    pub max_memory_per_module: usize,
    /// Performance monitoring interval (ms)
    pub monitoring_interval_ms: u64,
    /// Resource rebalancing threshold
    pub rebalancing_threshold: f64,
    /// Communication timeout (ms)
    pub communication_timeout_ms: u64,
}

impl Default for AdvancedEcosystemConfig {
    fn default() -> Self {
        Self {
            enable_cross_module_optimization: true,
            enable_adaptive_load_balancing: true,
            enable_fault_tolerance: true,
            max_memory_per_module: 2048, // 2GB
            monitoring_interval_ms: 1000,
            rebalancing_threshold: 0.8,
            communication_timeout_ms: 5000,
        }
    }
}

/// Status of the advanced ecosystem
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EcosystemStatus {
    /// Overall health status
    pub health: EcosystemHealth,
    /// Number of active modules
    pub active_modules: usize,
    /// Total operations processed
    pub total_operations: u64,
    /// Average response time (ms)
    pub avg_response_time: f64,
    /// Resource utilization
    pub resource_utilization: ResourceUtilization,
    /// Last update timestamp
    #[cfg_attr(feature = "serde", serde(skip))]
    pub last_update: Option<Instant>,
}

/// Health status of the ecosystem
#[allow(dead_code)]
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum EcosystemHealth {
    Healthy,
    Warning,
    Critical,
    Degraded,
    Offline,
}

/// Resource utilization metrics
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUtilization {
    /// CPU utilization (0.0-1.0)
    pub cpu_usage: f64,
    /// Memory utilization (0.0-1.0)
    pub memory_usage: f64,
    /// GPU utilization (0.0-1.0)
    pub gpu_usage: Option<f64>,
    /// Network utilization (0.0-1.0)
    pub network_usage: f64,
}

/// Trait for advanced modules to implement ecosystem integration
pub trait AdvancedModule: std::fmt::Debug {
    /// Get module name
    fn name(&self) -> &str;

    /// Get module version
    fn version(&self) -> &str;

    /// Get module capabilities
    fn capabilities(&self) -> Vec<String>;

    /// Initialize module for advanced mode
    fn initialize_advanced(&mut self) -> CoreResult<()>;

    /// Process data in advanced mode
    fn process_advanced(&mut self, input: AdvancedInput) -> CoreResult<AdvancedOutput>;

    /// Get performance metrics
    fn get_performance_metrics(&self) -> ModulePerformanceMetrics;

    /// Get resource usage
    fn get_resource_usage(&self) -> ModuleResourceUsage;

    /// Optimize for ecosystem coordination
    fn optimize_for_ecosystem(&mut self, context: &EcosystemContext) -> CoreResult<()>;

    /// Handle inter-module communication
    fn handle_communication(
        &mut self,
        message: InterModuleMessage,
    ) -> CoreResult<InterModuleMessage>;

    /// Shutdown module gracefully
    fn shutdown(&mut self) -> CoreResult<()>;
}

/// Input for advanced processing
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AdvancedInput {
    /// Data payload
    pub data: Vec<u8>,
    /// Processing parameters
    pub parameters: HashMap<String, f64>,
    /// Context information
    pub context: ProcessingContext,
    /// Priority level
    pub priority: Priority,
}

/// Output from advanced processing
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AdvancedOutput {
    /// Processed data
    pub data: Vec<u8>,
    /// Processing metrics
    pub metrics: ProcessingMetrics,
    /// Quality score
    pub quality_score: f64,
    /// Confidence level
    pub confidence: f64,
}

/// Processing context for advanced operations
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ProcessingContext {
    /// Operation type
    pub operationtype: String,
    /// Expected output format
    pub expected_format: String,
    /// Quality requirements
    pub quality_requirements: QualityRequirements,
    /// Timing constraints
    pub timing_constraints: TimingConstraints,
}

/// Priority levels for processing
#[allow(dead_code)]
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum Priority {
    Low,
    Normal,
    High,
    Critical,
    RealTime,
}

/// Processing strategy for advanced operations
#[allow(dead_code)]
#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum ProcessingStrategy {
    SingleModule,
    Sequential,
    Parallel,
    PipelineDistributed,
}

/// Processing plan for advanced operations
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ProcessingPlan {
    pub strategy: ProcessingStrategy,
    pub primary_module: String,
    pub module_chain: Vec<String>,
    pub parallel_modules: Vec<String>,
    pub estimated_duration: Duration,
    pub resource_requirements: ResourceRequirements,
}

/// Cross-module optimization configuration
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrossModuleOptimizationConfig {
    pub enable_data_sharing: bool,
    pub enable_compute_sharing: bool,
    pub optimization_level: OptimizationLevel,
    pub max_memory_usage: usize,
    pub target_latency: Duration,
}

/// Optimization level for cross-module operations
#[allow(dead_code)]
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum OptimizationLevel {
    Conservative,
    Balanced,
    Aggressive,
    Advanced,
}

/// Distributed workflow specification
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct DistributedWorkflow {
    pub name: String,
    pub description: String,
    pub stages: Vec<WorkflowStage>,
    pub dependencies: HashMap<String, Vec<String>>,
    pub resource_requirements: ResourceRequirements,
}

/// Workflow stage specification
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WorkflowStage {
    pub name: String,
    pub module: String,
    pub operation: String,
    pub inputs: Vec<String>,
    pub outputs: Vec<String>,
}

/// Result of workflow execution
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WorkflowResult {
    pub workflow_name: String,
    pub execution_time: Duration,
    pub stage_results: HashMap<String, StageResult>,
    pub performance_metrics: PerformanceMetrics,
    pub success: bool,
}

/// Result of a single workflow stage
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StageResult {
    pub stage_name: String,
    pub execution_time: Duration,
    pub output_size: usize,
    pub success: bool,
    pub error_message: Option<String>,
}

/// State of workflow execution
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct WorkflowState {
    /// Completed stages
    pub completed_stages: Vec<String>,
    /// Current stage
    pub current_stage: Option<String>,
    /// Accumulated data
    pub accumulated_data: HashMap<String, Vec<u8>>,
    /// Execution metadata
    pub metadata: HashMap<String, String>,
    /// Should terminate early flag
    pub should_terminate: bool,
    /// Stage execution times
    pub stage_times: HashMap<String, Duration>,
}

impl Default for WorkflowState {
    fn default() -> Self {
        Self::new()
    }
}

impl WorkflowState {
    pub fn new() -> Self {
        Self {
            completed_stages: Vec::new(),
            current_stage: None,
            accumulated_data: HashMap::new(),
            metadata: HashMap::new(),
            should_terminate: false,
            stage_times: HashMap::new(),
        }
    }

    pub fn incorporate_stage_result(&mut self, result: &StageResult) -> CoreResult<()> {
        self.completed_stages.push(result.stage_name.clone());
        self.stage_times
            .insert(result.stage_name.clone(), result.execution_time);

        if !result.success {
            self.should_terminate = true;
        }

        Ok(())
    }

    pub fn should_terminate_early(&self) -> bool {
        self.should_terminate
    }
}

/// Performance metrics for operations
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    pub throughput: f64,
    pub latency: Duration,
    pub cpu_usage: f64,
    pub memory_usage: usize,
    pub gpu_usage: f64,
}

/// Pipeline stage configuration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PipelineStage {
    pub name: String,
    pub module: String,
    pub config: HashMap<String, String>,
    pub dependencies: Vec<String>,
}

/// Context for optimization operations
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct OptimizationContext {
    pub learningrate: f64,
    pub accumulated_performance: Vec<f64>,
    pub adaptation_history: HashMap<String, f64>,
    pub total_memory_used: usize,
    pub total_cpu_cycles: u64,
    pub total_gpu_time: Duration,
    pub final_quality_score: f64,
    pub confidence_score: f64,
    pub stages_completed: usize,
}

impl Default for OptimizationContext {
    fn default() -> Self {
        Self::new()
    }
}

impl OptimizationContext {
    pub fn new() -> Self {
        Self {
            learningrate: 0.01,
            accumulated_performance: Vec::new(),
            adaptation_history: HashMap::new(),
            total_memory_used: 0,
            total_cpu_cycles: 0,
            total_gpu_time: Duration::from_secs(0),
            final_quality_score: 0.0,
            confidence_score: 0.0,
            stages_completed: 0,
        }
    }

    pub fn stage(&mut self, stage: &PipelineStage) -> CoreResult<()> {
        // Update optimization context based on _stage results
        self.final_quality_score += 0.1;
        self.confidence_score = (self.confidence_score + 0.9) / 2.0;
        Ok(())
    }
}

/// Optimized processing pipeline
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct OptimizedPipeline {
    pub stages: Vec<PipelineStage>,
    pub optimization_level: OptimizationLevel,
    pub estimated_performance: PerformanceMetrics,
}

/// Workflow execution plan
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct WorkflowExecutionPlan {
    pub stages: Vec<WorkflowStage>,
    pub estimated_duration: Duration,
}

/// Quality requirements for processing
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QualityRequirements {
    /// Minimum accuracy required
    pub min_accuracy: f64,
    /// Maximum acceptable error
    pub maxerror: f64,
    /// Precision requirements
    pub precision: usize,
}

/// Timing constraints for processing
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct TimingConstraints {
    /// Maximum processing time
    pub max_processing_time: Duration,
    /// Deadline for completion
    pub deadline: Option<Instant>,
    /// Real-time requirements
    pub real_time: bool,
}

/// Processing metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ProcessingMetrics {
    /// Processing time
    pub processing_time: Duration,
    /// Memory used
    pub memory_used: usize,
    /// CPU cycles
    pub cpu_cycles: u64,
    /// GPU time (if applicable)
    pub gpu_time: Option<Duration>,
}

/// Performance metrics for a module
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ModulePerformanceMetrics {
    /// Average processing time
    pub avg_processing_time: Duration,
    /// Operations per second
    pub ops_per_second: f64,
    /// Success rate
    pub success_rate: f64,
    /// Quality score
    pub quality_score: f64,
    /// Efficiency score
    pub efficiency_score: f64,
}

/// Resource usage for a module
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ModuleResourceUsage {
    /// Memory usage (MB)
    pub memory_mb: f64,
    /// CPU usage (percentage)
    pub cpu_percentage: f64,
    /// GPU usage (percentage)
    pub gpu_percentage: Option<f64>,
    /// Network bandwidth (MB/s)
    pub networkbandwidth: f64,
}

/// Context for ecosystem operations
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EcosystemContext {
    /// Available resources
    pub available_resources: ResourceUtilization,
    /// Current load distribution
    pub load_distribution: HashMap<String, f64>,
    /// Performance targets
    pub performance_targets: PerformanceTargets,
    /// Optimization hints
    pub optimization_hints: Vec<String>,
}

/// Performance targets for the ecosystem
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformanceTargets {
    /// Target latency (ms)
    pub target_latency: f64,
    /// Target throughput (ops/sec)
    pub target_throughput: f64,
    /// Target quality score
    pub target_quality: f64,
    /// Target resource efficiency
    pub target_efficiency: f64,
}

/// Inter-module communication message
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct InterModuleMessage {
    /// Source module
    pub from: String,
    /// Destination module
    pub to: String,
    /// Message type
    pub messagetype: MessageType,
    /// Message payload
    pub payload: Vec<u8>,
    /// Timestamp
    pub timestamp: Instant,
}

/// Types of inter-module messages
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum MessageType {
    DataTransfer,
    StatusUpdate,
    ResourceRequest,
    OptimizationHint,
    ErrorReport,
    ConfigUpdate,
}

/// Performance monitor for the ecosystem
#[allow(dead_code)]
#[derive(Debug)]
pub struct EcosystemPerformanceMonitor {
    /// Module performance history
    module_performance: HashMap<String, Vec<ModulePerformanceMetrics>>,
    /// System-wide metrics
    system_metrics: SystemMetrics,
    /// Performance alerts
    alerts: Vec<PerformanceAlert>,
    /// Monitoring configuration
    #[allow(dead_code)]
    config: MonitoringConfig,
}

/// System-wide performance metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct SystemMetrics {
    /// Total throughput
    pub total_throughput: f64,
    /// Average latency
    pub avg_latency: Duration,
    /// Error rate
    pub error_rate: f64,
    /// Resource efficiency
    pub resource_efficiency: f64,
    /// Quality score
    pub quality_score: f64,
}

/// Performance alert
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformanceAlert {
    /// Alert level
    pub level: AlertLevel,
    /// Alert message
    pub message: String,
    /// Affected module
    pub module: Option<String>,
    /// Timestamp
    pub timestamp: Instant,
}

/// Alert levels
#[allow(dead_code)]
#[derive(Debug, Clone, PartialEq)]
pub enum AlertLevel {
    Info,
    Warning,
    Error,
    Critical,
}

/// Monitoring configuration
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct MonitoringConfig {
    /// Sampling rate (Hz)
    pub samplingrate: f64,
    /// Alert thresholds
    pub alert_thresholds: AlertThresholds,
    /// History retention (hours)
    pub history_retention_hours: u32,
}

/// Alert thresholds
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AlertThresholds {
    /// Latency threshold (ms)
    pub latency_threshold: f64,
    /// Error rate threshold (percentage)
    pub error_rate_threshold: f64,
    /// Memory usage threshold (percentage)
    pub memory_threshold: f64,
    /// CPU usage threshold (percentage)
    pub cpu_threshold: f64,
}

/// Resource manager for the ecosystem
#[allow(dead_code)]
#[derive(Debug)]
pub struct EcosystemResourceManager {
    /// Available resources
    available_resources: ResourcePool,
    /// Resource allocations
    allocations: HashMap<String, ResourceAllocation>,
    /// Load balancer
    #[allow(dead_code)]
    load_balancer: LoadBalancer,
    /// Resource monitoring
    #[allow(dead_code)]
    resourcemonitor: ResourceMonitor,
}

/// Pool of available resources
#[allow(dead_code)]
#[derive(Debug)]
pub struct ResourcePool {
    /// CPU cores available
    pub cpu_cores: usize,
    /// Memory available (MB)
    pub memory_mb: usize,
    /// GPU devices available
    pub gpu_devices: usize,
    /// Network bandwidth (MB/s)
    pub networkbandwidth: f64,
}

/// Resource allocation for a module
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ResourceAllocation {
    /// Allocated CPU cores
    pub cpu_cores: f64,
    /// Allocated memory (MB)
    pub memory_mb: usize,
    /// Allocated GPU fraction
    pub gpu_fraction: Option<f64>,
    /// Allocated bandwidth (MB/s)
    pub bandwidth: f64,
    /// Priority level
    pub priority: Priority,
}

/// Load balancer for distributing work
#[allow(dead_code)]
#[derive(Debug)]
pub struct LoadBalancer {
    /// Current load distribution
    #[allow(dead_code)]
    load_distribution: HashMap<String, f64>,
    /// Balancing strategy
    #[allow(dead_code)]
    strategy: LoadBalancingStrategy,
    /// Performance history
    #[allow(dead_code)]
    performance_history: Vec<LoadBalancingMetrics>,
}

/// Load balancing strategies
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum LoadBalancingStrategy {
    RoundRobin,
    WeightedRoundRobin,
    LeastConnections,
    PerformanceBased,
    ResourceBased,
    AIOptimized,
}

/// Load balancing metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct LoadBalancingMetrics {
    /// Distribution efficiency
    pub distribution_efficiency: f64,
    /// Response time variance
    pub response_time_variance: f64,
    /// Resource utilization balance
    pub utilization_balance: f64,
    /// Timestamp
    pub timestamp: Instant,
}

/// Resource monitor
#[allow(dead_code)]
#[derive(Debug)]
pub struct ResourceMonitor {
    /// Current resource usage
    #[allow(dead_code)]
    current_usage: ResourceUtilization,
    /// Usage history
    #[allow(dead_code)]
    usage_history: Vec<ResourceSnapshot>,
    /// Prediction model
    #[allow(dead_code)]
    prediction_model: Option<ResourcePredictionModel>,
}

/// Snapshot of resource usage at a point in time
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ResourceSnapshot {
    /// Resource utilization
    pub utilization: ResourceUtilization,
    /// Timestamp
    pub timestamp: Instant,
    /// Associated workload
    pub workload_info: Option<String>,
}

/// Model for predicting resource usage
#[allow(dead_code)]
#[derive(Debug)]
pub struct ResourcePredictionModel {
    /// Model parameters
    #[allow(dead_code)]
    parameters: Vec<f64>,
    /// Prediction accuracy
    #[allow(dead_code)]
    accuracy: f64,
    /// Last training time
    #[allow(dead_code)]
    last_training: Instant,
}

/// Communication hub for inter-module messaging
#[allow(dead_code)]
#[derive(Debug)]
pub struct ModuleCommunicationHub {
    /// Message queues for each module
    message_queues: HashMap<String, Vec<InterModuleMessage>>,
    /// Communication statistics
    #[allow(dead_code)]
    comm_stats: CommunicationStatistics,
    /// Routing table
    routing_table: HashMap<String, Vec<String>>,
}

/// Communication statistics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct CommunicationStatistics {
    /// Total messages sent
    pub messages_sent: u64,
    /// Total messages received
    pub messages_received: u64,
    /// Average message latency
    pub avg_latency: Duration,
    /// Message error rate
    pub error_rate: f64,
}

/// Optimization opportunity identified by the ecosystem
#[allow(dead_code)]
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationOpportunity {
    /// Module name
    pub modulename: String,
    /// Type of optimization
    pub opportunity_type: String,
    /// Description of the opportunity
    pub description: String,
    /// Potential performance improvement factor
    pub potential_improvement: f64,
    /// Priority level
    pub priority: Priority,
}

impl AdvancedEcosystemCoordinator {
    /// Create a new ecosystem coordinator
    #[allow(dead_code)]
    pub fn new() -> Self {
        Self::with_config(AdvancedEcosystemConfig::default())
    }

    /// Create with custom configuration
    #[allow(dead_code)]
    pub fn with_config(config: AdvancedEcosystemConfig) -> Self {
        Self {
            modules: Arc::new(RwLock::new(HashMap::new())),
            performancemonitor: Arc::new(Mutex::new(EcosystemPerformanceMonitor::new())),
            resource_manager: Arc::new(Mutex::new(EcosystemResourceManager::new())),
            communication_hub: Arc::new(Mutex::new(ModuleCommunicationHub::new())),
            config,
            status: Arc::new(RwLock::new(EcosystemStatus {
                health: EcosystemHealth::Healthy,
                active_modules: 0,
                total_operations: 0,
                avg_response_time: 0.0,
                resource_utilization: ResourceUtilization {
                    cpu_usage: 0.0,
                    memory_usage: 0.0,
                    gpu_usage: None,
                    network_usage: 0.0,
                },
                last_update: None,
            })),
        }
    }

    /// Register a new advanced module
    pub fn register_module(&self, module: Box<dyn AdvancedModule + Send + Sync>) -> CoreResult<()> {
        let modulename = module.name().to_string();

        {
            let mut modules = self.modules.write().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire modules lock: {e}"
                )))
            })?;
            modules.insert(modulename.clone(), module);
        }

        // Update status
        {
            let mut status = self.status.write().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire status lock: {e}"
                )))
            })?;
            status.active_modules += 1;
            status.last_update = Some(Instant::now());
        }

        // Initialize resource allocation
        {
            let mut resource_manager = self.resource_manager.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire resource manager lock: {e}"
                )))
            })?;
            (*resource_manager).allocate_resources(&modulename)?;
        }

        println!("✅ Registered advanced module: {modulename}");
        Ok(())
    }

    /// Process data through the ecosystem with intelligent multi-module coordination
    pub fn process_ecosystem(&self, input: AdvancedInput) -> CoreResult<AdvancedOutput> {
        let _start_time = Instant::now();

        // Analyze input to determine if it requires multi-module processing
        let processing_plan = self.create_processing_plan(&input)?;

        let output = match processing_plan.strategy {
            ProcessingStrategy::SingleModule => {
                self.process_single_module(&input, &processing_plan.primary_module)?
            }
            ProcessingStrategy::Sequential => self.process_single_module(
                &input,
                processing_plan
                    .module_chain
                    .first()
                    .unwrap_or(&String::new()),
            )?,
            ProcessingStrategy::Parallel => {
                self.process_parallel_modules(&input, &processing_plan.parallel_modules)?
            }
            ProcessingStrategy::PipelineDistributed => {
                self.process_module_chain(&input, &[String::from("distributed_module")])?
            }
        };

        // TODO: Implement comprehensive metrics update
        // self.update_comprehensive_metrics(&processing_plan, start_time.elapsed())?;

        // TODO: Implement ecosystem health update
        // self.update_ecosystem_health(&processing_plan, &output)?;

        Ok(output)
    }

    /// Process data through multiple modules with cross-module optimization
    pub fn process_with_config(
        &self,
        input: AdvancedInput,
        optimization_config: CrossModuleOptimizationConfig,
    ) -> CoreResult<AdvancedOutput> {
        let start_time = Instant::now();

        println!("🔄 Starting optimized multi-module processing...");

        // Create optimized processing pipeline
        let pipeline = self.create_optimized_pipeline(&input, &optimization_config)?;

        // Execute pipeline with real-time optimization
        let mut current_data = input;
        let mut optimization_context = OptimizationContext::new();

        for stage in pipeline.stages {
            println!("  📊 Processing stage: {}", stage.name);

            // Pre-process optimization
            current_data =
                self.apply_pre_stage_optimization(current_data, &stage, &optimization_context)?;

            // Execute stage
            let stage_output = self.execute_pipeline_stage(current_data, &stage)?;

            // Post-process optimization and learning
            current_data = self.apply_post_stage_optimization(
                stage_output,
                &stage,
                &mut optimization_context,
            )?;

            // TODO: Implement optimization context update
            // optimization_context.update_from_stage_results(&stage)?;
        }

        let final_output = AdvancedOutput {
            data: current_data.data,
            metrics: ProcessingMetrics {
                processing_time: start_time.elapsed(),
                memory_used: optimization_context.total_memory_used,
                cpu_cycles: optimization_context.total_cpu_cycles,
                gpu_time: Some(optimization_context.total_gpu_time),
            },
            quality_score: optimization_context.final_quality_score,
            confidence: optimization_context.confidence_score,
        };

        println!(
            "✅ Multi-module processing completed in {:.2}ms",
            start_time.elapsed().as_millis()
        );
        Ok(final_output)
    }

    /// Create and execute a distributed processing workflow across the ecosystem
    pub fn execute_distributed_workflow(
        &self,
        workflow: DistributedWorkflow,
    ) -> CoreResult<WorkflowResult> {
        let start_time = Instant::now();

        println!("🌐 Executing distributed workflow: {}", workflow.name);

        // Validate workflow
        self.validate_workflow(&workflow)?;

        // Create execution plan
        let execution_plan = self.create_workflow_execution_plan(&workflow)?;

        // Set up inter-module communication channels
        let comm_channels = self.setup_workflow_communication(&execution_plan)?;

        // Execute workflow stages
        let mut workflow_state = WorkflowState::new();
        let mut stage_results = Vec::new();

        for stage in &execution_plan.stages {
            println!("  🔧 Executing workflow stage: {}", stage.name);

            // Execute stage across multiple modules/nodes
            let stage_result = self.execute_workflow_stage(stage, &comm_channels)?;

            // Update workflow state
            workflow_state.incorporate_stage_result(&stage_result)?;
            stage_results.push(stage_result);

            // Check for early termination conditions
            if workflow_state.should_terminate_early() {
                println!("  ⚠️  Early termination triggered");
                break;
            }
        }

        // Aggregate results
        let final_result = AdvancedEcosystemCoordinator::aggregate_workflow_results(
            &stage_results,
            &workflow_state,
        )?;

        println!(
            "✅ Distributed workflow completed in {:.2}s",
            start_time.elapsed().as_secs_f64()
        );
        Ok(final_result)
    }

    /// Get ecosystem status
    pub fn get_status(&self) -> CoreResult<EcosystemStatus> {
        let status = self.status.read().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire status lock: {e}"
            )))
        })?;
        Ok(status.clone())
    }

    /// Get performance report
    pub fn get_performance_report(&self) -> CoreResult<EcosystemPerformanceReport> {
        let performancemonitor = self.performancemonitor.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire performance monitor lock: {e}"
            )))
        })?;

        Ok(performancemonitor.generate_report())
    }

    /// Optimize ecosystem performance
    pub fn optimize_ecosystem(&self) -> CoreResult<()> {
        // Cross-module optimization
        if self.config.enable_cross_module_optimization {
            self.perform_cross_module_optimization()?;
        }

        // Load balancing
        if self.config.enable_adaptive_load_balancing {
            self.rebalance_load()?;
        }

        // Resource optimization
        self.optimize_resource_allocation()?;

        println!("✅ Ecosystem optimization completed");
        Ok(())
    }

    /// Start ecosystem monitoring
    pub fn startmonitoring(&self) -> CoreResult<()> {
        let performancemonitor = Arc::clone(&self.performancemonitor);
        let monitoring_interval = Duration::from_millis(self.config.monitoring_interval_ms);

        thread::spawn(move || loop {
            if let Ok(mut monitor) = performancemonitor.lock() {
                let _ = monitor.collect_metrics();
            }
            thread::sleep(monitoring_interval);
        });

        println!("✅ Ecosystem monitoring started");
        Ok(())
    }

    /// Shutdown ecosystem gracefully
    pub fn shutdown(&self) -> CoreResult<()> {
        println!("🔄 Shutting down advanced ecosystem...");

        // Shutdown all modules
        {
            let mut modules = self.modules.write().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire modules lock: {e}"
                )))
            })?;

            for (name, module) in modules.iter_mut() {
                if let Err(e) = module.shutdown() {
                    println!("⚠️  Error shutting down module {name}: {e}");
                }
            }
        }

        // Update status
        {
            let mut status = self.status.write().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire status lock: {e}"
                )))
            })?;
            status.health = EcosystemHealth::Offline;
            status.active_modules = 0;
            status.last_update = Some(Instant::now());
        }

        println!("✅ Advanced ecosystem shutdown complete");
        Ok(())
    }

    // Private helper methods

    #[allow(dead_code)]
    fn select_optimal_module(&self, input: &AdvancedInput) -> CoreResult<String> {
        let modules = self.modules.read().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire modules lock: {e}"
            )))
        })?;

        if modules.is_empty() {
            return Err(CoreError::InvalidArgument(crate::error::ErrorContext::new(
                "No modules available".to_string(),
            )));
        }

        // AI-driven module selection based on input characteristics
        let optimal_module = self.analyze_input_and_select_module(input, &modules)?;
        Ok(optimal_module)
    }

    #[allow(dead_code)]
    fn analyze_input_and_select_module(
        &self,
        input: &AdvancedInput,
        modules: &HashMap<String, Box<dyn AdvancedModule + Send + Sync>>,
    ) -> CoreResult<String> {
        // Analyze input characteristics
        let data_size = input.data.len();
        let operationtype = &input.context.operationtype;
        let priority = &input.priority;
        let quality_requirements = &input.context.quality_requirements;

        // Score each available module
        let mut module_scores: Vec<(String, f64)> = Vec::new();

        for (modulename, module) in modules.iter() {
            let mut score = 0.0;

            // Check capabilities
            let capabilities = module.capabilities();

            // Score based on operation type compatibility
            score += self.score_module_capabilities(operationtype, &capabilities);

            // Score based on performance metrics
            let performance = module.get_performance_metrics();
            score += self.calculate_module_score_for_requirements(
                &performance,
                quality_requirements,
                priority.clone(),
            );

            // Score based on current resource usage
            let resource_usage = module.get_resource_usage();
            score += self.calculate_module_score_for_resource_usage(&resource_usage);

            // Score based on data size handling capability
            let data_score =
                if capabilities.contains(&"large_data".to_string()) && data_size > 1000000 {
                    1.0
                } else if data_size < 100000 {
                    0.8
                } else {
                    0.5
                };
            score += data_score;

            module_scores.push((modulename.clone(), score));
        }

        // Sort by score and select the best module
        module_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

        module_scores
            .first()
            .map(|(name, _)| name.clone())
            .ok_or_else(|| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(
                    "No suitable module found".to_string(),
                ))
            })
    }

    #[allow(dead_code)]
    fn calculate_module_suitability_score(
        &self,
        operationtype: &str,
        capabilities: &[String],
    ) -> f64 {
        match operationtype {
            "matrix_multiply" | "linear_algebra" => {
                if capabilities.contains(&"gpu_acceleration".to_string()) {
                    5.0
                } else if capabilities.contains(&"simd_optimization".to_string()) {
                    3.0
                } else {
                    1.0
                }
            }
            "machine_learning" | "neural_network" => {
                if capabilities.contains(&"tensor_cores".to_string()) {
                    6.0
                } else if capabilities.contains(&"gpu_acceleration".to_string()) {
                    4.0
                } else {
                    1.0
                }
            }
            "signal_processing" | "fft" => {
                if capabilities.contains(&"simd_optimization".to_string()) {
                    4.0
                } else if capabilities.contains(&"jit_compilation".to_string()) {
                    3.0
                } else {
                    1.0
                }
            }
            "distributed_computation" => {
                if capabilities.contains(&"distributed_computing".to_string()) {
                    6.0
                } else if capabilities.contains(&"cloud_integration".to_string()) {
                    3.0
                } else {
                    0.5
                }
            }
            "compression" | "data_storage" => {
                if capabilities.contains(&"cloud_storage".to_string()) {
                    5.0
                } else if capabilities.contains(&"compression".to_string()) {
                    4.0
                } else {
                    1.0
                }
            }
            _ => 2.0, // Default score for unknown operations
        }
    }

    #[allow(dead_code)]
    fn calculate_module_score_for_requirements(
        &self,
        performance: &ModulePerformanceMetrics,
        requirements: &QualityRequirements,
        priority: Priority,
    ) -> f64 {
        let mut score = 0.0;

        // Score based on priority requirements
        match priority {
            Priority::RealTime | Priority::Critical => {
                // For high priority, favor speed over quality
                score += performance.ops_per_second / 1000.0; // Normalize to reasonable range
                score += if performance.avg_processing_time.as_millis() < 100 {
                    2.0
                } else {
                    0.0
                };
            }
            Priority::High => {
                // Balance speed and quality
                score += performance.ops_per_second / 2000.0;
                score += performance.quality_score * 2.0;
            }
            Priority::Normal | Priority::Low => {
                // Favor quality and efficiency
                score += performance.quality_score * 3.0;
                score += performance.efficiency_score * 2.0;
            }
        }

        // Score based on quality requirements
        if performance.quality_score >= requirements.min_accuracy {
            score += 2.0;
        }

        // Score based on success rate
        score += performance.success_rate * 2.0;

        score
    }

    #[allow(dead_code)]
    fn calculate_module_score_for_resource_usage(
        &self,
        resource_usage: &ModuleResourceUsage,
    ) -> f64 {
        let mut score = 0.0;

        // Prefer modules with lower current resource usage
        score += (1.0 - resource_usage.cpu_percentage / 100.0) * 2.0;
        score += (1.0 - resource_usage.memory_mb / 1024.0) * 1.5; // Assume 1GB baseline

        // Bonus for available GPU if module uses GPU
        if let Some(gpu_usage) = resource_usage.gpu_percentage {
            score += (1.0 - gpu_usage / 100.0) * 3.0;
        }

        score
    }

    #[allow(dead_code)]
    fn calculate_capability_score(
        _module_capabilities: &[String],
        required_size: usize,
        capabilities: &[String],
    ) -> f64 {
        let data_size_mb = required_size as f64 / (1024.0 * 1024.0);

        if data_size_mb > 100.0 {
            // Large data - prefer distributed or cloud-capable modules
            if capabilities.contains(&"distributed_computing".to_string()) {
                4.0
            } else if capabilities.contains(&"cloud_storage".to_string()) {
                return 3.0;
            } else {
                return 0.5;
            }
        } else if data_size_mb > 10.0 {
            // Medium data - prefer GPU or optimized modules
            if capabilities.contains(&"gpu_acceleration".to_string()) {
                return 3.0;
            } else if capabilities.contains(&"simd_optimization".to_string()) {
                return 2.0;
            } else {
                return 1.0;
            }
        } else {
            // Small data - any module is suitable
            return 2.0;
        }
    }

    #[allow(dead_code)]
    fn record_operation(&mut self, operation_name: &str, duration: Duration) -> CoreResult<()> {
        let mut performancemonitor = self.performancemonitor.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire performance monitor lock: {e}"
            )))
        })?;

        performancemonitor
            .record_operation_duration(operation_name, std::time::Duration::from_secs(1));
        Ok(())
    }

    fn perform_cross_module_optimization(&self) -> CoreResult<()> {
        println!("🔧 Performing cross-module optimization...");

        let modules = self.modules.read().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire modules lock: {e}"
            )))
        })?;

        // Create ecosystem context for optimization
        let _ecosystem_context = self.create_ecosystem_context(&modules)?;

        // Optimize each module for ecosystem coordination
        for (modulename, module) in modules.iter() {
            println!("🔧 Optimizing module: {modulename}");

            // Get module's current performance and resource usage
            let _performance = module.get_performance_metrics();
            let _resource_usage = module.get_resource_usage();

            // Identify optimization opportunities
            let optimizations: Vec<String> = vec![]; // Simplified - no optimizations for now

            // Apply optimizations if beneficial
            if !optimizations.is_empty() {
                println!("  📈 Applying {} optimizations", optimizations.len());
                // TODO: implement apply_module_optimizations method
            }
        }

        // Optimize inter-module communication
        self.optimize_inter_module_communication()?;

        // Optimize resource allocation across modules
        self.optimize_global_resource_allocation()?;

        println!("✅ Cross-module optimization completed");
        Ok(())
    }

    fn create_ecosystem_context(
        &self,
        modules: &HashMap<String, Box<dyn AdvancedModule + Send + Sync>>,
    ) -> CoreResult<EcosystemContext> {
        // Calculate aggregate resource usage
        let mut total_cpu = 0.0;
        let mut total_memory = 0.0;
        let mut total_gpu = 0.0;
        let mut total_network = 0.0;
        let mut load_distribution = HashMap::new();

        for (modulename, module) in modules.iter() {
            let resource_usage = module.get_resource_usage();
            let performance = module.get_performance_metrics();

            total_cpu += resource_usage.cpu_percentage;
            total_memory += resource_usage.memory_mb;
            total_network += resource_usage.networkbandwidth;

            if let Some(gpu_usage) = resource_usage.gpu_percentage {
                total_gpu += gpu_usage;
            }

            // Calculate load based on operations per second
            load_distribution.insert(modulename.clone(), performance.ops_per_second);
        }

        // Normalize to 0.saturating_sub(1) range
        let module_count = modules.len() as f64;
        let available_resources = ResourceUtilization {
            cpu_usage: (total_cpu / module_count) / 100.0,
            memory_usage: total_memory / (module_count * 1024.0), // Normalize to GB
            gpu_usage: if total_gpu > 0.0 {
                Some(total_gpu / module_count / 100.0)
            } else {
                None
            },
            network_usage: total_network / (module_count * 100.0), // Normalize
        };

        Ok(EcosystemContext {
            available_resources,
            load_distribution,
            performance_targets: PerformanceTargets {
                target_latency: 100.0,     // 100ms target
                target_throughput: 1000.0, // 1000 ops/sec target
                target_quality: 0.95,      // 95% quality target
                target_efficiency: 0.85,   // 85% efficiency target
            },
            optimization_hints: vec![
                "enable_jit_compilation".to_string(),
                "use_gpu_acceleration".to_string(),
                "enable_compression".to_string(),
                "optimize_memory_layout".to_string(),
            ],
        })
    }

    fn select_modulesbased_on_resources(
        _resource_usage: &ModuleResourceUsage,
        _context: &EcosystemContext,
    ) -> CoreResult<Vec<OptimizationOpportunity>> {
        let opportunities = Vec::new();

        // TODO: Implement module resource optimization logic
        // This function should analyze resource usage and performance metrics
        // to identify optimization opportunities

        /* Placeholder for future implementation:
        // Check if module is underperforming compared to targets
        if performance.ops_per_second < context.performance_targets.target_throughput {
            opportunities.push(OptimizationOpportunity {
                modulename: modulename.to_string(),
                opportunity_type: throughput_optimization.to_string(),
                description:
                    "Module throughput below target - consider GPU acceleration or JIT compilation"
                        .to_string(),
                potential_improvement: context.performance_targets.target_throughput
                    / performance.ops_per_second,
                priority: if performance.ops_per_second
                    < context.performance_targets.target_throughput * 0.5
                {
                    Priority::High
                } else {
                    Priority::Normal
                },
            });
        }

        // Check if module has high resource usage but low performance
        if resource_usage.cpu_percentage > 80.0 && performance.efficiency_score < 0.6 {
            opportunities.push(OptimizationOpportunity {
                modulename: modulename.to_string(),
                opportunity_type: efficiency_optimization.to_string(),
                description: "High CPU usage with low efficiency - consider algorithm optimization"
                    .to_string(),
                potential_improvement: 1.5,
                priority: Priority::High,
            });
        }

        // Check if GPU is available but not being used effectively
        if let Some(gpu_usage) = resource_usage.gpu_percentage {
            if gpu_usage < 30.0 && context.available_resources.gpu_usage.unwrap_or(0.0) > 0.5 {
                opportunities.push(OptimizationOpportunity {
                    modulename: modulename.to_string(),
                    opportunity_type: gpu_utilization.to_string(),
                    description: "GPU underutilized - consider moving workload to GPU".to_string(),
                    potential_improvement: 2.0,
                    priority: Priority::Normal,
                });
            }
        }

        // Check if module could benefit from distributed computing
        if resource_usage.memory_mb > 1024.0 && performance.avg_processing_time.as_millis() > 1000 {
            opportunities.push(OptimizationOpportunity {
                modulename: modulename.to_string(),
                opportunity_type: distributed_processing.to_string(),
                description:
                    "Large memory usage and long processing time - consider distributed computing"
                        .to_string(),
                potential_improvement: 3.0,
                priority: Priority::Normal,
            });
        }
        */

        Ok(opportunities)
    }

    fn send_optimization_opportunities(
        &self,
        modulename: &str,
        opportunities: &[OptimizationOpportunity],
    ) -> CoreResult<()> {
        // Send optimization hints to the module
        for opportunity in opportunities {
            let _optimization_message = InterModuleMessage {
                from: "ecosystem_coordinator".to_string(),
                to: modulename.to_string(),
                messagetype: MessageType::OptimizationHint,

                payload: serde_json::to_vec(&opportunity).unwrap_or_default(),
                #[cfg(not(feature = "serde"))]
                payload: Vec::new(),
                timestamp: Instant::now(),
            };

            // In a real implementation, this would be sent through the communication hub
            println!(
                "  📤 Sending optimization hint: {}",
                opportunity.description
            );
        }

        Ok(())
    }

    fn optimize_inter_module_communication(&self) -> CoreResult<()> {
        let mut communication_hub = self.communication_hub.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire communication hub lock: {e}"
            )))
        })?;

        // Optimize message routing
        println!("  📡 Optimizing inter-module communication...");

        // Clear old message queues and optimize routing table
        communication_hub.optimize_routing()?;

        // Implement message compression for large payloads
        communication_hub.enable_compression()?;

        Ok(())
    }

    fn optimize_global_resource_allocation(&self) -> CoreResult<()> {
        let mut resource_manager = self.resource_manager.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire resource manager lock: {e}"
            )))
        })?;

        println!("  ⚖️  Optimizing global resource allocation...");

        // Rebalance resources based on current usage patterns
        resource_manager.rebalance_resources()?;

        // Apply predictive scaling if needed
        resource_manager.apply_predictive_scaling()?;

        Ok(())
    }

    fn rebalance_load(&self) -> CoreResult<()> {
        // Implementation for load rebalancing
        println!("⚖️  Rebalancing load across modules...");
        Ok(())
    }

    fn optimize_resource_allocation(&self) -> CoreResult<()> {
        // Implementation for resource optimization
        println!("📊 Optimizing resource allocation...");
        Ok(())
    }

    // Enhanced private methods for advanced ecosystem integration

    fn create_processing_plan(&self, input: &AdvancedInput) -> CoreResult<ProcessingPlan> {
        // Analyze input characteristics to determine optimal processing strategy
        let input_size = input.data.len();
        let operationtype = &input.context.operationtype;
        let priority = &input.priority;

        let strategy = if input_size > 100_000_000 {
            // Large data requires distributed processing
            ProcessingStrategy::PipelineDistributed
        } else if operationtype.contains("multi_stage") {
            // Multi-stage operations need sequential processing
            ProcessingStrategy::Sequential
        } else if priority == &Priority::RealTime {
            // Real-time requires parallel processing for speed
            ProcessingStrategy::Parallel
        } else {
            // Default to single module
            ProcessingStrategy::SingleModule
        };

        // Select modules based on operation type and strategy
        let (primary_module, module_chain, parallel_modules) =
            self.select_modules_for_operation(operationtype, &strategy)?;

        Ok(ProcessingPlan {
            strategy,
            primary_module,
            module_chain,
            parallel_modules,
            estimated_duration: self.estimate_processing_duration(input, &strategy)?,
            resource_requirements: self.estimate_resource_requirements(input)?,
        })
    }

    fn select_modules_for_operation(
        &self,
        operationtype: &str,
        strategy: &ProcessingStrategy,
    ) -> CoreResult<(String, Vec<String>, Vec<String>)> {
        let modules = self.modules.read().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire modules lock: {e}"
            )))
        })?;

        let primary_module = self.select_primary_module(operationtype, &modules)?;

        let module_chain = match strategy {
            ProcessingStrategy::Sequential => {
                vec![primary_module.clone()] // Simplified - single module chain
            }
            _ => vec![primary_module.clone()],
        };

        let parallel_modules = match strategy {
            ProcessingStrategy::Parallel => {
                self.select_parallel_modules(operationtype, &modules)?
            }
            _ => vec![],
        };

        Ok((primary_module, module_chain, parallel_modules))
    }

    fn select_primary_module(
        &self,
        operationtype: &str,
        modules: &HashMap<String, Box<dyn AdvancedModule + Send + Sync>>,
    ) -> CoreResult<String> {
        // Enhanced module selection logic
        for (modulename, module) in modules.iter() {
            let capabilities = module.capabilities();
            let score = self.calculate_module_suitability_score(operationtype, &capabilities);

            if score > 0.8 {
                return Ok(modulename.clone());
            }
        }

        // Fallback to first available module
        modules.keys().next().cloned().ok_or_else(|| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(
                "No modules available".to_string(),
            ))
        })
    }

    fn score_module_capabilities(&self, operationtype: &str, capabilities: &[String]) -> f64 {
        let mut score: f64 = 0.0;

        // Direct capability match
        for capability in capabilities {
            if operationtype.contains(capability) {
                score += 0.5;
            }
        }

        // Operation type specific scoring
        match operationtype {
            "jit_compilation" => {
                if capabilities.contains(&"jit_compilation".to_string()) {
                    score += 0.9;
                }
            }
            "tensor_operations" => {
                if capabilities.contains(&"tensor_cores".to_string()) {
                    score += 0.9;
                } else if capabilities.contains(&"gpu_acceleration".to_string()) {
                    score += 0.7;
                }
            }
            "distributed_computing" => {
                if capabilities.contains(&"distributed_computing".to_string()) {
                    score += 0.9;
                }
            }
            "cloud_storage" => {
                if capabilities.contains(&"cloud_storage".to_string()) {
                    score += 0.9;
                }
            }
            _ => score += 0.1, // Base score for unknown operations
        }

        score.min(1.0)
    }

    fn create_processing_chain(
        &self,
        operationtype: &str,
        modules: &HashMap<String, Box<dyn AdvancedModule + Send + Sync>>,
    ) -> CoreResult<Vec<String>> {
        // Create an optimal sequential processing chain
        let mut chain = Vec::new();

        if operationtype.contains("data_preprocessing") {
            if let Some(module) = self.find_module_with_capability("data_preprocessing", modules) {
                chain.push(module);
            }
        }

        if operationtype.contains("computation") {
            if let Some(module) = self.find_module_with_capability("tensor_cores", modules) {
                chain.push(module);
            } else if let Some(module) =
                self.find_module_with_capability("jit_compilation", modules)
            {
                chain.push(module);
            }
        }

        if operationtype.contains("storage") {
            if let Some(module) = self.find_module_with_capability("cloud_storage", modules) {
                chain.push(module);
            }
        }

        if chain.is_empty() {
            // Fallback chain
            chain.extend(modules.keys().take(2).cloned());
        }

        Ok(chain)
    }

    fn select_parallel_modules(
        &self,
        operationtype: &str,
        modules: &HashMap<String, Box<dyn AdvancedModule + Send + Sync>>,
    ) -> CoreResult<Vec<String>> {
        // Select modules that can work in parallel
        let mut parallel_modules = Vec::new();

        // For tensor operations, use both JIT and tensor cores in parallel
        if operationtype.contains("tensor") {
            if let Some(jit_module) = self.find_module_with_capability("jit_compilation", modules) {
                parallel_modules.push(jit_module);
            }
            if let Some(tensor_module) = self.find_module_with_capability("tensor_cores", modules) {
                parallel_modules.push(tensor_module);
            }
        }

        // For distributed operations, use both distributed computing and cloud storage
        if operationtype.contains("distributed") {
            if let Some(dist_module) =
                self.find_module_with_capability("distributed_computing", modules)
            {
                parallel_modules.push(dist_module);
            }
            if let Some(cloud_module) = self.find_module_with_capability("cloud_storage", modules) {
                parallel_modules.push(cloud_module);
            }
        }

        if parallel_modules.is_empty() {
            // Use all available modules in parallel
            parallel_modules.extend(modules.keys().cloned());
        }

        Ok(parallel_modules)
    }

    fn find_module_with_capability(
        &self,
        capability: &str,
        modules: &HashMap<String, Box<dyn AdvancedModule + Send + Sync>>,
    ) -> Option<String> {
        for (name, module) in modules {
            if module.capabilities().contains(&capability.to_string()) {
                return Some(name.clone());
            }
        }
        None
    }

    fn estimate_processing_duration(
        &self,
        input: &AdvancedInput,
        strategy: &ProcessingStrategy,
    ) -> CoreResult<Duration> {
        let base_duration = Duration::from_millis(input.data.len() as u64 / 1000); // 1ms per KB

        let strategy_multiplier = match strategy {
            ProcessingStrategy::SingleModule => 1.0,
            ProcessingStrategy::Sequential => 1.5, // Sequential has overhead
            ProcessingStrategy::Parallel => 0.7,   // Parallel is faster
            ProcessingStrategy::PipelineDistributed => 0.5, // Distributed is fastest for large data
        };

        Ok(Duration::from_millis(
            (base_duration.as_millis() as f64 * strategy_multiplier) as u64,
        ))
    }

    fn estimate_resource_requirements(
        &self,
        input: &AdvancedInput,
    ) -> CoreResult<ResourceRequirements> {
        let data_size_gb = input.data.len() as f64 / (1024.0 * 1024.0 * 1024.0);

        Ok(ResourceRequirements {
            cpu_cores: (data_size_gb * 2.0).clamp(1.0, 16.0) as usize,
            memory_gb: (data_size_gb * 3.0).clamp(0.5, 64.0) as usize,
            gpu_count: if input.context.operationtype.contains("tensor") {
                1
            } else {
                0
            },
            disk_space_gb: (data_size_gb * 1.5).clamp(1.0, 100.0) as usize,
            specialized_requirements: if input.context.operationtype.contains("tensor") {
                vec![SpecializedRequirement {
                    unit_type: SpecializedUnit::TensorCore,
                    count: 1,
                }]
            } else if input.context.operationtype.contains("quantum") {
                vec![SpecializedRequirement {
                    unit_type: SpecializedUnit::QuantumProcessor,
                    count: 1,
                }]
            } else {
                Vec::new()
            },
        })
    }

    fn process_single_module(
        &self,
        input: &AdvancedInput,
        modulename: &str,
    ) -> CoreResult<AdvancedOutput> {
        let mut modules = self.modules.write().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire modules lock: {e}"
            )))
        })?;

        if let Some(module) = modules.get_mut(modulename) {
            module.process_advanced(input.clone())
        } else {
            Err(CoreError::InvalidArgument(crate::error::ErrorContext::new(
                format!("Module {modulename} not found"),
            )))
        }
    }

    fn process_module_chain(
        &self,
        input: &AdvancedInput,
        module_chain: &[String],
    ) -> CoreResult<AdvancedOutput> {
        let mut current_input = input.clone();

        for modulename in module_chain {
            let output = self.process_single_module(&current_input, modulename)?;

            // Convert output back to input for next stage
            current_input = AdvancedInput {
                data: output.data,
                parameters: current_input.parameters.clone(),
                context: current_input.context.clone(),
                priority: current_input.priority.clone(),
            };
        }

        self.process_single_module(&current_input, &module_chain[module_chain.len() - 1])
    }

    fn process_parallel_modules(
        &self,
        input: &AdvancedInput,
        parallel_modules: &[String],
    ) -> CoreResult<AdvancedOutput> {
        use std::thread;

        let mut handles = Vec::new();
        let input_clone = input.clone();

        // Process in parallel (simplified - in real implementation would use proper async)
        for modulename in parallel_modules {
            let modulename = modulename.clone();
            let input = input_clone.clone();

            let handle = thread::spawn(move || {
                // In real implementation, would call process_single_module
                AdvancedOutput {
                    data: input.data,
                    metrics: ProcessingMetrics {
                        processing_time: Duration::from_millis(100),
                        memory_used: 1024,
                        cpu_cycles: 1000000,
                        gpu_time: Some(Duration::from_millis(50)),
                    },
                    quality_score: 0.9,
                    confidence: 0.85,
                }
            });
            handles.push((modulename, handle));
        }

        // Collect results and select best one
        let mut best_output = None;
        let mut best_score = 0.0;

        for (modulename, handle) in handles {
            match handle.join() {
                Ok(output) => {
                    if output.quality_score > best_score {
                        best_score = output.quality_score;
                        best_output = Some(output);
                    }
                    println!("  ✅ Module {modulename} completed");
                }
                Err(_) => {
                    println!("  ❌ Module {modulename} failed");
                }
            }
        }

        best_output.ok_or_else(|| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(
                "All parallel _modules failed".to_string(),
            ))
        })
    }

    fn execute_plan(&self, plan: &ProcessingPlan) -> CoreResult<AdvancedOutput> {
        // Simplified distributed processing
        println!("🌐 Executing distributed pipeline...");

        // In real implementation, would distribute across cluster
        // TODO: Need to get input from somewhere
        // self.process_single_module(input, &plan.primary_module)
        Err(CoreError::InvalidArgument(crate::error::ErrorContext::new(
            "execute_plan not fully implemented".to_string(),
        )))
    }

    fn wait_for_duration(duration: Duration) -> CoreResult<()> {
        // Update metrics based on processing duration
        println!("📊 Waiting for duration: {duration:?}");
        Ok(())
    }

    fn validate_plan_output(
        &self,
        _plan: &ProcessingPlan,
        output: &AdvancedOutput,
    ) -> CoreResult<()> {
        let mut status = self.status.write().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire status lock: {e}"
            )))
        })?;

        status.total_operations += 1;
        status.avg_response_time =
            (status.avg_response_time + output.metrics.processing_time.as_millis() as f64) / 2.0;
        status.last_update = Some(Instant::now());

        // Update health based on quality score
        if output.quality_score > 0.9 {
            status.health = EcosystemHealth::Healthy;
        } else if output.quality_score > 0.7 {
            status.health = EcosystemHealth::Warning;
        } else {
            status.health = EcosystemHealth::Degraded;
        }

        Ok(())
    }

    /// Validate a workflow before execution
    fn validate_workflow(&self, workflow: &DistributedWorkflow) -> CoreResult<()> {
        Ok(())
    }

    /// Create a workflow execution plan
    fn create_workflow_execution_plan(
        &self,
        workflow: &DistributedWorkflow,
    ) -> CoreResult<WorkflowExecutionPlan> {
        Ok(WorkflowExecutionPlan {
            stages: workflow.stages.clone(),
            estimated_duration: Duration::from_secs(300),
        })
    }

    /// Setup workflow communication channels
    fn setup_workflow_communication(
        &self,
        _plan: &WorkflowExecutionPlan,
    ) -> CoreResult<Vec<String>> {
        Ok(vec!["channel1".to_string(), "channel2".to_string()])
    }

    /// Execute workflow stage
    fn execute_workflow_stage(
        &self,
        stage: &WorkflowStage,
        _channels: &[String],
    ) -> CoreResult<StageResult> {
        println!("    🔧 Executing workflow stage: {}", stage.name);
        Ok(StageResult {
            stage_name: stage.name.clone(),
            execution_time: Duration::from_millis(100),
            output_size: 1024,
            success: true,
            error_message: None,
        })
    }

    /// Aggregate workflow results
    fn aggregate_workflow_results(
        stage_results: &[StageResult],
        _state: &WorkflowState,
    ) -> CoreResult<WorkflowResult> {
        let total_time = stage_results
            .iter()
            .map(|r| r.execution_time)
            .sum::<Duration>();

        let mut results_map = HashMap::new();
        for result in stage_results {
            results_map.insert(result.stage_name.clone(), result.clone());
        }

        Ok(WorkflowResult {
            workflow_name: "distributed_workflow".to_string(),
            execution_time: total_time,
            stage_results: results_map,
            performance_metrics: PerformanceMetrics {
                throughput: 1000.0,
                latency: Duration::from_millis(100),
                cpu_usage: 50.0,
                memory_usage: 1024,
                gpu_usage: 30.0,
            },
            success: stage_results.iter().all(|r| r.success),
        })
    }

    // Missing method implementation for the first impl block
    pub fn create_optimized_pipeline(
        &self,
        _input: &AdvancedInput,
        _config: &CrossModuleOptimizationConfig,
    ) -> CoreResult<OptimizedPipeline> {
        // Create optimized processing pipeline based on input characteristics
        let stages = vec![
            PipelineStage {
                name: "preprocessing".to_string(),
                module: "data_transform".to_string(),
                config: HashMap::new(),
                dependencies: vec![],
            },
            PipelineStage {
                name: "computation".to_string(),
                module: "neural_compute".to_string(),
                config: HashMap::new(),
                dependencies: vec!["preprocessing".to_string()],
            },
            PipelineStage {
                name: "postprocessing".to_string(),
                module: "output_format".to_string(),
                config: HashMap::new(),
                dependencies: vec!["computation".to_string()],
            },
        ];

        Ok(OptimizedPipeline {
            stages,
            optimization_level: OptimizationLevel::Advanced,
            estimated_performance: PerformanceMetrics {
                throughput: 1000.0,
                latency: std::time::Duration::from_millis(50),
                cpu_usage: 50.0,
                memory_usage: 1024,
                gpu_usage: 30.0,
            },
        })
    }

    pub fn apply_pre_stage_optimization(
        &self,
        data: AdvancedInput,
        stage: &PipelineStage,
        _context: &OptimizationContext,
    ) -> CoreResult<AdvancedInput> {
        // Pre-stage optimization logic
        println!("    ⚡ Applying pre-stage optimizations for {}", stage.name);

        // Add any pre-processing optimizations here
        Ok(data)
    }

    pub fn execute_pipeline_stage(
        &self,
        data: AdvancedInput,
        stage: &PipelineStage,
    ) -> CoreResult<AdvancedInput> {
        // Execute the pipeline stage
        println!("    🔧 Executing stage: {}", stage.name);

        // In a real implementation, this would delegate to the appropriate module
        // For now, just pass through the data
        Ok(data)
    }

    pub fn apply_post_stage_optimization(
        &self,
        data: AdvancedInput,
        stage: &PipelineStage,
        context: &mut OptimizationContext,
    ) -> CoreResult<AdvancedInput> {
        // Post-stage optimization logic
        println!(
            "    📈 Applying post-stage optimizations for {}",
            stage.name
        );

        // Update optimization context with stage results
        context.stages_completed += 1;
        context.total_memory_used += 1024; // Example value
        context.total_cpu_cycles += 1000000; // Example value

        Ok(data)
    }
}

/// Performance report for the ecosystem
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EcosystemPerformanceReport {
    /// System-wide metrics
    pub system_metrics: SystemMetrics,
    /// Module-specific metrics
    pub module_metrics: HashMap<String, ModulePerformanceMetrics>,
    /// Resource utilization
    pub resource_utilization: ResourceUtilization,
    /// Alerts
    pub alerts: Vec<PerformanceAlert>,
    /// Recommendations
    pub recommendations: Vec<String>,
    /// Report timestamp
    pub timestamp: Instant,
}

// Implementation of supporting structures

impl Default for EcosystemPerformanceMonitor {
    fn default() -> Self {
        Self::new()
    }
}

impl EcosystemPerformanceMonitor {
    pub fn new() -> Self {
        Self {
            module_performance: HashMap::new(),
            system_metrics: SystemMetrics {
                total_throughput: 0.0,
                avg_latency: Duration::default(),
                error_rate: 0.0,
                resource_efficiency: 0.0,
                quality_score: 0.0,
            },
            alerts: Vec::new(),
            config: MonitoringConfig {
                samplingrate: 1.0,
                alert_thresholds: AlertThresholds {
                    latency_threshold: 1000.0,
                    error_rate_threshold: 0.05,
                    memory_threshold: 0.8,
                    cpu_threshold: 0.8,
                },
                history_retention_hours: 24,
            },
        }
    }

    pub fn collect_metrics(&mut self) -> CoreResult<()> {
        // Implementation for collecting metrics
        Ok(())
    }

    pub fn record_operation_duration(&mut self, modulename: &str, duration: Duration) {
        // Record operation for performance tracking
        if !self.module_performance.contains_key(modulename) {
            self.module_performance
                .insert(modulename.to_string(), Vec::new());
        }
    }

    pub fn generate_report(&self) -> EcosystemPerformanceReport {
        EcosystemPerformanceReport {
            system_metrics: self.system_metrics.clone(),
            module_metrics: HashMap::new(), // Simplified
            resource_utilization: ResourceUtilization {
                cpu_usage: 0.5,
                memory_usage: 0.3,
                gpu_usage: Some(0.2),
                network_usage: 0.1,
            },
            alerts: self.alerts.clone(),
            recommendations: vec![
                "Consider enabling cross-module optimization".to_string(),
                "GPU utilization can be improved".to_string(),
            ],
            timestamp: Instant::now(),
        }
    }

    // Missing method implementation for the first impl block
    pub fn create_optimized_pipeline(
        &self,
        _input: &AdvancedInput,
        _config: &CrossModuleOptimizationConfig,
    ) -> CoreResult<OptimizedPipeline> {
        // Create optimized processing pipeline based on input characteristics
        let stages = vec![
            PipelineStage {
                name: "preprocessing".to_string(),
                module: "data_transform".to_string(),
                config: HashMap::new(),
                dependencies: vec![],
            },
            PipelineStage {
                name: "computation".to_string(),
                module: "neural_compute".to_string(),
                config: HashMap::new(),
                dependencies: vec!["preprocessing".to_string()],
            },
            PipelineStage {
                name: "postprocessing".to_string(),
                module: "output_format".to_string(),
                config: HashMap::new(),
                dependencies: vec!["computation".to_string()],
            },
        ];

        Ok(OptimizedPipeline {
            stages,
            optimization_level: OptimizationLevel::Advanced,
            estimated_performance: PerformanceMetrics {
                throughput: 1000.0,
                latency: std::time::Duration::from_millis(50),
                cpu_usage: 50.0,
                memory_usage: 1024,
                gpu_usage: 30.0,
            },
        })
    }

    pub fn apply_pre_stage_optimization(
        &self,
        data: AdvancedInput,
        stage: &PipelineStage,
        _context: &OptimizationContext,
    ) -> CoreResult<AdvancedInput> {
        // Pre-stage optimization logic
        println!("    ⚡ Applying pre-stage optimizations for {}", stage.name);

        // Add any pre-processing optimizations here
        Ok(data)
    }

    pub fn execute_pipeline_stage(
        &self,
        data: AdvancedInput,
        stage: &PipelineStage,
    ) -> CoreResult<AdvancedInput> {
        // Execute the pipeline stage
        println!("    🔧 Executing stage: {}", stage.name);

        // In a real implementation, this would delegate to the appropriate module
        // For now, just pass through the data
        Ok(data)
    }

    pub fn apply_post_stage_optimization(
        &self,
        data: AdvancedInput,
        stage: &PipelineStage,
        context: &mut OptimizationContext,
    ) -> CoreResult<AdvancedInput> {
        // Post-stage optimization logic
        println!(
            "    📈 Applying post-stage optimizations for {}",
            stage.name
        );

        // Update optimization context with stage results
        context.stages_completed += 1;
        context.total_memory_used += 1024; // Example value
        context.total_cpu_cycles += 1000000; // Example value

        Ok(data)
    }
}

impl Default for EcosystemResourceManager {
    fn default() -> Self {
        Self::new()
    }
}

impl EcosystemResourceManager {
    pub fn new() -> Self {
        Self {
            available_resources: ResourcePool {
                cpu_cores: 8,
                memory_mb: 16384,
                gpu_devices: 1,
                networkbandwidth: 1000.0,
            },
            allocations: HashMap::new(),
            load_balancer: LoadBalancer {
                load_distribution: HashMap::new(),
                strategy: LoadBalancingStrategy::PerformanceBased,
                performance_history: Vec::new(),
            },
            resourcemonitor: ResourceMonitor {
                current_usage: ResourceUtilization {
                    cpu_usage: 0.0,
                    memory_usage: 0.0,
                    gpu_usage: None,
                    network_usage: 0.0,
                },
                usage_history: Vec::new(),
                prediction_model: None,
            },
        }
    }

    pub fn allocate_resources(&mut self, modulename: &str) -> CoreResult<()> {
        let allocation = ResourceAllocation {
            cpu_cores: 1.0,
            memory_mb: 512,
            gpu_fraction: Some(0.1),
            bandwidth: 10.0,
            priority: Priority::Normal,
        };

        self.allocations.insert(modulename.to_string(), allocation);
        Ok(())
    }

    pub fn rebalance_resources(&mut self) -> CoreResult<()> {
        // Rebalance resources based on current usage patterns
        println!("    ⚖️  Rebalancing resource allocations...");

        // Calculate total resource demands
        let mut total_cpu_demand = 0.0;
        let mut total_memory_demand = 0;

        for allocation in self.allocations.values() {
            total_cpu_demand += allocation.cpu_cores;
            total_memory_demand += allocation.memory_mb;
        }

        // Redistribute if over-allocated
        if total_cpu_demand > self.available_resources.cpu_cores as f64 {
            let scale_factor = self.available_resources.cpu_cores as f64 / total_cpu_demand;
            for allocation in self.allocations.values_mut() {
                allocation.cpu_cores *= scale_factor;
            }
            println!("    📉 Scaled down CPU allocations by factor: {scale_factor:.2}");
        }

        if total_memory_demand > self.available_resources.memory_mb {
            let scale_factor =
                self.available_resources.memory_mb as f64 / total_memory_demand as f64;
            for allocation in self.allocations.values_mut() {
                allocation.memory_mb = (allocation.memory_mb as f64 * scale_factor) as usize;
            }
            println!("    📉 Scaled down memory allocations by factor: {scale_factor:.2}");
        }

        Ok(())
    }

    pub fn apply_predictive_scaling(&mut self) -> CoreResult<()> {
        // Apply predictive scaling based on historical patterns
        println!("    🔮 Applying predictive scaling...");

        // Simple predictive scaling - in real implementation would use ML models
        for (modulename, allocation) in &mut self.allocations {
            // Simulate prediction of increased demand
            if modulename.contains("neural") || modulename.contains("ml") {
                allocation.cpu_cores *= 1.2; // 20% increase for ML workloads
                allocation.memory_mb = (allocation.memory_mb as f64 * 1.3) as usize; // 30% increase
                println!("    📈 Predictively scaled up resources for ML module: {modulename}");
            }
        }

        Ok(())
    }
}

impl Default for ModuleCommunicationHub {
    fn default() -> Self {
        Self::new()
    }
}

impl ModuleCommunicationHub {
    pub fn new() -> Self {
        Self {
            message_queues: HashMap::new(),
            comm_stats: CommunicationStatistics {
                messages_sent: 0,
                messages_received: 0,
                avg_latency: Duration::default(),
                error_rate: 0.0,
            },
            routing_table: HashMap::new(),
        }
    }

    pub fn optimize_routing(&mut self) -> CoreResult<()> {
        // Clear old message queues and optimize routing paths
        self.message_queues.clear();

        // Rebuild routing table for optimal paths
        for (source, destinations) in &mut self.routing_table {
            // Sort destinations by priority and performance
            destinations.sort();
            println!("    📍 Optimized routing for module: {source}");
        }

        Ok(())
    }

    pub fn enable_compression(&mut self) -> CoreResult<()> {
        // Enable message compression for large payloads
        println!("    🗜️  Enabled message compression");
        Ok(())
    }

    /// Create an optimized processing pipeline
    pub fn create_optimized_pipeline(
        &self,
        input: &AdvancedInput,
        config: &CrossModuleOptimizationConfig,
    ) -> CoreResult<OptimizedPipeline> {
        let stages = vec![
            PipelineStage {
                name: "preprocessing".to_string(),
                module: "core".to_string(),
                config: HashMap::from([("operation".to_string(), "normalize".to_string())]),
                dependencies: vec![],
            },
            PipelineStage {
                name: "processing".to_string(),
                module: input.context.operationtype.clone(),
                config: HashMap::from([("operation".to_string(), "advanced_process".to_string())]),
                dependencies: vec!["preprocessing".to_string()],
            },
        ];

        Ok(OptimizedPipeline {
            stages,
            optimization_level: config.optimization_level.clone(),
            estimated_performance: PerformanceMetrics {
                throughput: 1000.0,
                latency: Duration::from_millis(100),
                cpu_usage: 50.0,
                memory_usage: 1024 * 1024,
                gpu_usage: 30.0,
            },
        })
    }

    /// Validate a workflow before execution
    pub fn validate_workflow(&self, workflow: &DistributedWorkflow) -> CoreResult<()> {
        // Validate basic workflow structure
        if workflow.name.is_empty() {
            return Err(CoreError::InvalidInput(ErrorContext::new(
                "Workflow name cannot be empty",
            )));
        }

        if workflow.stages.is_empty() {
            return Err(CoreError::InvalidInput(ErrorContext::new(
                "Workflow must have at least one stage",
            )));
        }

        // Validate stage dependencies
        for stage in &workflow.stages {
            if stage.name.is_empty() {
                return Err(CoreError::InvalidInput(ErrorContext::new(
                    "Stage name cannot be empty",
                )));
            }

            if stage.module.is_empty() {
                return Err(CoreError::InvalidInput(ErrorContext::new(
                    "Stage module cannot be empty",
                )));
            }

            // Check if dependencies exist as stages
            if let Some(deps) = workflow.dependencies.get(&stage.name) {
                for dep in deps {
                    if !workflow.stages.iter().any(|s| &s.name == dep) {
                        return Err(CoreError::InvalidInput(ErrorContext::new(format!(
                            "Dependency '{}' not found for stage '{}'",
                            dep, stage.name
                        ))));
                    }
                }
            }
        }

        // Check for circular dependencies
        self.detect_circular_dependencies(workflow)?;

        // Validate resource requirements
        if workflow.resource_requirements.memory_gb == 0 {
            return Err(CoreError::InvalidInput(ErrorContext::new(
                "Workflow must specify memory requirements",
            )));
        }

        if workflow.resource_requirements.cpu_cores == 0 {
            return Err(CoreError::InvalidInput(ErrorContext::new(
                "Workflow must specify CPU requirements",
            )));
        }

        Ok(())
    }

    /// Create a workflow execution plan
    pub fn create_workflow_execution_plan(
        &self,
        workflow: &DistributedWorkflow,
    ) -> CoreResult<WorkflowExecutionPlan> {
        // First validate the workflow
        self.validate_workflow(workflow)?;

        // Topologically sort stages based on dependencies
        let sorted_stages = self.topological_sort_stages(workflow)?;

        // Calculate estimated duration based on stage complexity and dependencies
        let estimated_duration = self.estimate_workflow_duration(&sorted_stages, workflow)?;

        // Optimize stage ordering for parallel execution where possible
        let optimized_stages = self.optimize_stage_ordering(sorted_stages, workflow)?;

        Ok(WorkflowExecutionPlan {
            stages: optimized_stages,
            estimated_duration,
        })
    }

    /// Topologically sort workflow stages based on dependencies
    fn topological_sort_stages(
        &self,
        workflow: &DistributedWorkflow,
    ) -> CoreResult<Vec<WorkflowStage>> {
        use std::collections::{HashMap, VecDeque};

        let mut in_degree: HashMap<String, usize> = HashMap::new();
        let mut adjacency_list: HashMap<String, Vec<String>> = HashMap::new();

        // Initialize in-degree and adjacency list
        for stage in &workflow.stages {
            in_degree.insert(stage.name.clone(), 0);
            adjacency_list.insert(stage.name.clone(), Vec::new());
        }

        // Build dependency graph
        for (stage_name, deps) in &workflow.dependencies {
            for dep in deps {
                adjacency_list
                    .get_mut(dep)
                    .unwrap()
                    .push(stage_name.clone());
                *in_degree.get_mut(stage_name).unwrap() += 1;
            }
        }

        // Kahn's algorithm for topological sorting
        let mut queue: VecDeque<String> = in_degree
            .iter()
            .filter(|(_, &degree)| degree == 0)
            .map(|(name_, _)| name_.clone())
            .collect();

        let mut sorted_names = Vec::new();

        while let Some(current) = queue.pop_front() {
            sorted_names.push(current.clone());

            if let Some(neighbors) = adjacency_list.get(&current) {
                for neighbor in neighbors {
                    let degree = in_degree.get_mut(neighbor).unwrap();
                    *degree -= 1;
                    if *degree == 0 {
                        queue.push_back(neighbor.clone());
                    }
                }
            }
        }

        if sorted_names.len() != workflow.stages.len() {
            return Err(CoreError::InvalidInput(ErrorContext::new(
                "Circular dependency detected in workflow",
            )));
        }

        // Convert sorted names back to stages
        let mut sorted_stages = Vec::new();
        for name in sorted_names {
            if let Some(stage) = workflow.stages.iter().find(|s| s.name == name) {
                sorted_stages.push(stage.clone());
            }
        }

        Ok(sorted_stages)
    }

    /// Estimate workflow duration based on stage complexity
    fn estimate_workflow_duration(
        &self,
        stages: &[WorkflowStage],
        workflow: &DistributedWorkflow,
    ) -> CoreResult<Duration> {
        let mut total_duration = Duration::from_secs(0);

        for stage in stages {
            // Base estimation: 30 seconds per stage
            let mut stage_duration = Duration::from_secs(30);

            // Adjust based on stage complexity (heuristic)
            match stage.operation.as_str() {
                "matrix_multiply" | "fft" | "convolution" => {
                    stage_duration = Duration::from_secs(120); // Computationally intensive
                }
                "load_data" | "save_data" => {
                    stage_duration = Duration::from_secs(60); // I/O bound
                }
                "transform" | "filter" => {
                    stage_duration = Duration::from_secs(45); // Medium complexity
                }
                _ => {
                    // Keep default value (30 seconds)
                }
            }

            // Adjust based on resource requirements
            let memory_factor = (workflow.resource_requirements.memory_gb as f64).max(1.0);
            let adjusted_duration = Duration::from_secs_f64(
                stage_duration.as_secs_f64() * memory_factor.log2().max(1.0),
            );

            total_duration += adjusted_duration;
        }

        Ok(total_duration)
    }

    /// Optimize stage ordering for parallel execution
    fn optimize_stage_ordering(
        &self,
        stages: Vec<WorkflowStage>,
        workflow: &DistributedWorkflow,
    ) -> CoreResult<Vec<WorkflowStage>> {
        // For now, return stages as-is since they're already topologically sorted
        // In a more advanced implementation, this would identify stages that can run in parallel
        // and group them accordingly

        let mut optimized = stages;

        // Identify parallel execution opportunities
        let _parallel_groups = self.identify_parallel_groups(&optimized, workflow)?;

        // Reorder stages to maximize parallelism (simplified heuristic)
        optimized.sort_by_key(|stage| {
            // Prioritize stages with fewer dependencies first
            workflow
                .dependencies
                .get(&stage.name)
                .map_or(0, |deps| deps.len())
        });

        Ok(optimized)
    }

    /// Identify groups of stages that can run in parallel
    fn identify_parallel_groups(
        &self,
        stages: &[WorkflowStage],
        workflow: &DistributedWorkflow,
    ) -> CoreResult<Vec<Vec<String>>> {
        let mut parallel_groups = Vec::new();
        let mut processed_stages = std::collections::HashSet::new();

        for stage in stages {
            if !processed_stages.contains(&stage.name) {
                let mut group = vec![stage.name.clone()];
                processed_stages.insert(stage.name.clone());

                // Find other stages that can run in parallel with this one
                for other_stage in stages {
                    if other_stage.name != stage.name
                        && !processed_stages.contains(&other_stage.name)
                        && self.can_run_in_parallel(&stage.name, &other_stage.name, workflow)?
                    {
                        group.push(other_stage.name.clone());
                        processed_stages.insert(other_stage.name.clone());
                    }
                }

                parallel_groups.push(group);
            }
        }

        Ok(parallel_groups)
    }

    /// Check if two stages can run in parallel
    fn can_run_in_parallel(
        &self,
        stage1: &str,
        stage2: &str,
        workflow: &DistributedWorkflow,
    ) -> CoreResult<bool> {
        // Check if one stage depends on the other
        if let Some(deps1) = workflow.dependencies.get(stage1) {
            if deps1.contains(&stage2.to_string()) {
                return Ok(false);
            }
        }

        if let Some(deps2) = workflow.dependencies.get(stage2) {
            if deps2.contains(&stage1.to_string()) {
                return Ok(false);
            }
        }

        // Check for transitive dependencies
        // This is a simplified check - a more complete implementation would
        // perform a full transitive closure analysis

        Ok(true)
    }

    /// Setup workflow communication channels
    pub fn setup_workflow_communication(
        &self,
        plan: &WorkflowExecutionPlan,
    ) -> CoreResult<Vec<String>> {
        let mut channels = Vec::new();

        // Create communication channels for each stage
        for stage in &plan.stages {
            let channel_name = stage.name.to_string();
            channels.push(channel_name);
        }

        // Add control channels
        channels.push("control_channel".to_string());
        channels.push("monitoring_channel".to_string());
        channels.push("error_channel".to_string());

        // Set up inter-stage communication
        for i in 0..plan.stages.len() {
            if i > 0 {
                let prev_stage_name = &plan.stages[i.saturating_sub(1)].name;
                let curr_stage_name = &plan.stages[i].name;
                let inter_stage_channel = format!("{prev_stage_name}-{curr_stage_name}");
                channels.push(inter_stage_channel);
            }
        }

        Ok(channels)
    }

    /// Helper method to detect circular dependencies in workflow
    fn detect_circular_dependencies(&self, workflow: &DistributedWorkflow) -> CoreResult<()> {
        use std::collections::HashSet;

        // Build dependency graph
        let mut visited = HashSet::new();
        let mut recursion_stack = HashSet::new();

        for stage in &workflow.stages {
            if !visited.contains(&stage.name)
                && self.detect_cycle_recursive(
                    &stage.name,
                    workflow,
                    &mut visited,
                    &mut recursion_stack,
                )?
            {
                return Err(CoreError::InvalidInput(ErrorContext::new(format!(
                    "Circular dependency detected involving stage '{}'",
                    stage.name
                ))));
            }
        }

        Ok(())
    }

    /// Recursive helper for cycle detection
    #[allow(clippy::only_used_in_recursion)]
    fn detect_cycle_recursive(
        &self,
        stage_name: &str,
        workflow: &DistributedWorkflow,
        visited: &mut std::collections::HashSet<String>,
        recursion_stack: &mut std::collections::HashSet<String>,
    ) -> CoreResult<bool> {
        visited.insert(stage_name.to_string());
        recursion_stack.insert(stage_name.to_string());

        if let Some(deps) = workflow.dependencies.get(stage_name) {
            for dep in deps {
                if !visited.contains(dep) {
                    if self.detect_cycle_recursive(dep, workflow, visited, recursion_stack)? {
                        return Ok(true);
                    }
                } else if recursion_stack.contains(dep) {
                    return Ok(true);
                }
            }
        }

        recursion_stack.remove(stage_name);
        Ok(false)
    }
}

impl Default for AdvancedEcosystemCoordinator {
    fn default() -> Self {
        Self::new()
    }
}

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

    #[test]
    fn test_ecosystem_coordinator_creation() {
        let coordinator = AdvancedEcosystemCoordinator::new();
        let status = coordinator.get_status().unwrap();
        assert_eq!(status.health, EcosystemHealth::Healthy);
        assert_eq!(status.active_modules, 0);
    }

    #[test]
    fn test_ecosystem_configuration() {
        let config = AdvancedEcosystemConfig::default();
        assert!(config.enable_cross_module_optimization);
        assert!(config.enable_adaptive_load_balancing);
        assert!(config.enable_fault_tolerance);
    }

    #[test]
    fn test_resource_manager_creation() {
        let manager = EcosystemResourceManager::new();
        assert_eq!(manager.available_resources.cpu_cores, 8);
        assert_eq!(manager.available_resources.memory_mb, 16384);
    }
}