scirs2-core 0.4.2

//! Advanced Distributed Computing Framework
//!
//! This module provides a comprehensive distributed computing framework for
//! multi-node computation in Advanced mode, enabling seamless scaling of
//! scientific computing workloads across clusters, clouds, and edge devices.
//!
//! # Features
//!
//! - **Automatic Node Discovery**: Dynamic discovery and registration of compute nodes
//! - **Intelligent Load Balancing**: AI-driven workload distribution across nodes
//! - **Fault Tolerance**: Automatic recovery and redistribution on node failures
//! - **Adaptive Scheduling**: Real-time optimization of task scheduling
//! - **Cross-Node Communication**: High-performance messaging and data transfer
//! - **Resource Management**: Dynamic allocation and optimization of node resources
//! - **Security**: End-to-end encryption and authentication for distributed operations
//! - **Monitoring**: Real-time cluster health and performance monitoring
//! - **Elastic Scaling**: Automatic scaling based on workload demands

use crate::distributed::NodeType;
use crate::error::{CoreError, CoreResult};
use std::collections::HashMap;
use std::net::SocketAddr;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};

// Module declarations
pub mod cluster;
pub mod communication;
pub mod fault_tolerance;
pub mod monitoring;
pub mod scheduling;
pub mod types;

// Re-exports from submodules
pub use cluster::*;
pub use communication::*;
pub use fault_tolerance::*;
pub use monitoring::*;
pub use scheduling::*;
pub use types::*;

/// Central coordinator for distributed advanced computing
#[derive(Debug)]
pub struct AdvancedDistributedComputer {
    /// Cluster manager
    cluster_manager: Arc<Mutex<ClusterManager>>,
    /// Task scheduler
    task_scheduler: Arc<Mutex<AdaptiveTaskScheduler>>,
    /// Communication layer
    communication: Arc<Mutex<DistributedCommunication>>,
    /// Resource manager
    #[allow(dead_code)]
    resource_manager: Arc<Mutex<DistributedResourceManager>>,
    /// Load balancer
    #[allow(dead_code)]
    load_balancer: Arc<Mutex<IntelligentLoadBalancer>>,
    /// Fault tolerance manager
    fault_tolerance: Arc<Mutex<FaultToleranceManager>>,
    /// Configuration
    #[allow(dead_code)]
    config: DistributedComputingConfig,
    /// Cluster statistics
    statistics: Arc<RwLock<ClusterStatistics>>,
}

/// Distributed resource manager (placeholder for now)
#[derive(Debug)]
pub struct DistributedResourceManager;

/// Intelligent load balancer (placeholder for now)
#[derive(Debug)]
pub struct IntelligentLoadBalancer;

impl AdvancedDistributedComputer {
    /// Create a new distributed computer with default configuration
    #[allow(dead_code)]
    pub fn new() -> CoreResult<Self> {
        Self::with_config(DistributedComputingConfig::default())
    }

    /// Create a new distributed computer with custom configuration
    #[allow(dead_code)]
    pub fn with_config(config: DistributedComputingConfig) -> CoreResult<Self> {
        let cluster_manager = Arc::new(Mutex::new(ClusterManager::new(&config)?));
        let task_scheduler = Arc::new(Mutex::new(AdaptiveTaskScheduler::new(&config)?));
        let communication = Arc::new(Mutex::new(DistributedCommunication::new(&config)?));
        let resource_manager = Arc::new(Mutex::new(DistributedResourceManager::new(&config)?));
        let load_balancer = Arc::new(Mutex::new(IntelligentLoadBalancer::new(&config)?));
        let fault_tolerance = Arc::new(Mutex::new(FaultToleranceManager::new(&config)?));
        let statistics = Arc::new(RwLock::new(ClusterStatistics::default()));

        Ok(Self {
            cluster_manager,
            task_scheduler,
            communication,
            resource_manager,
            load_balancer,
            fault_tolerance,
            config,
            statistics,
        })
    }

    /// Submit a distributed task for execution with intelligent scheduling
    pub fn submit_task(&self, task: DistributedTask) -> CoreResult<TaskId> {
        let start_time = Instant::now();

        // Validate task before submission
        self.validate_task(&task)?;

        // Analyze task requirements for optimal placement
        let task_requirements = self.analyze_task_requirements(&task)?;

        // Get optimal nodes for this task
        let suitable_nodes = self.find_suitable_nodes(&task_requirements)?;

        if suitable_nodes.is_empty() {
            return Err(CoreError::InvalidArgument(crate::error::ErrorContext::new(
                "No suitable nodes available for task execution".to_string(),
            )));
        }

        // Submit to scheduler with placement hints
        let mut scheduler = self.task_scheduler.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire scheduler lock: {e}"
            )))
        })?;

        let taskid = scheduler.submit_task(task)?;

        // Update statistics
        self.update_submission_stats(start_time.elapsed())?;

        // Set up fault tolerance monitoring for the task
        self.register_task_formonitoring(&taskid)?;

        println!("📋 Task {} submitted to distributed cluster", taskid.0);
        Ok(taskid)
    }

    /// Batch submit multiple tasks with optimal load distribution
    pub fn submit_batch_tasks(&self, tasks: Vec<DistributedTask>) -> CoreResult<Vec<TaskId>> {
        let start_time = Instant::now();
        let mut taskids = Vec::new();

        println!("📦 Submitting batch of {} tasks...", tasks.len());

        // Analyze all tasks for optimal batch scheduling
        let task_analyses: Result<Vec<_>, _> = tasks
            .iter()
            .map(|task| self.analyze_task_requirements(task))
            .collect();
        let task_analyses = task_analyses?;

        // Group tasks by resource requirements for efficient scheduling
        let task_groups = self.group_tasks_by_requirements(&tasks, &task_analyses)?;

        // Submit each group to optimal nodes
        for (resource_profile, task_group) in task_groups {
            let _suitable_nodes = self.find_nodes_for_profile(&resource_profile)?;

            for (task, _task_analysis) in task_group {
                let taskid = self.submit_task(task)?;
                taskids.push(taskid);
            }
        }

        println!(
            "✅ Batch submission completed: {} tasks in {:.2}ms",
            tasks.len(),
            start_time.elapsed().as_millis()
        );

        Ok(taskids)
    }

    /// Submit a task with fault tolerance and automatic retry
    pub fn submit_with_fault_tolerance(
        &self,
        task: DistributedTask,
        fault_tolerance_config: FaultToleranceConfig,
    ) -> CoreResult<TaskId> {
        // Create fault-tolerant wrapper around the task
        let fault_tolerant_task = self.wrap_with_fault_tolerance(task, fault_tolerance_config)?;

        // Submit with enhanced monitoring
        let taskid = self.submit_task(fault_tolerant_task)?;

        // Set up advanced monitoring and recovery
        self.register_task_formonitoring(&taskid)?;

        Ok(taskid)
    }

    /// Get task status
    pub fn get_task_status(&self, taskid: &TaskId) -> CoreResult<Option<TaskStatus>> {
        let scheduler = self.task_scheduler.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire scheduler lock: {e}"
            )))
        })?;

        Ok(scheduler.get_task_status(taskid))
    }

    /// Cancel a task
    pub fn cancel_task(&self, taskid: &TaskId) -> CoreResult<()> {
        let scheduler = self.task_scheduler.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire scheduler lock: {e}"
            )))
        })?;

        scheduler.cancel_task(taskid)
    }

    /// Get cluster status
    pub fn get_cluster_status(&self) -> CoreResult<ClusterStatistics> {
        let stats = self.statistics.read().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire statistics lock: {e}"
            )))
        })?;

        Ok(stats.clone())
    }

    /// Scale cluster up or down
    pub fn scale_cluster(&self, targetnodes: usize) -> CoreResult<()> {
        let cluster_manager = self.cluster_manager.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire cluster manager lock: {e}"
            )))
        })?;

        cluster_manager.scale_to(targetnodes)
    }

    /// Start distributed computing operations
    pub fn start(&self) -> CoreResult<()> {
        println!("🚀 Starting advanced distributed computing...");

        // Start cluster management
        {
            let mut cluster_manager = self.cluster_manager.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire cluster manager lock: {e}"
                )))
            })?;
            cluster_manager.start()?;
        }

        // Start task scheduler
        {
            let mut scheduler = self.task_scheduler.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire scheduler lock: {e}"
                )))
            })?;
            scheduler.start()?;
        }

        // Start communication layer
        {
            let mut communication = self.communication.lock().map_err(|e| {
                CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                    "Failed to acquire communication lock: {e}"
                )))
            })?;
            communication.start()?;
        }

        println!("✅ Distributed computing system started");
        Ok(())
    }

    /// Stop distributed computing operations
    pub fn stop(&self) -> CoreResult<()> {
        println!("🛑 Stopping advanced distributed computing...");

        // Stop components in reverse order
        // ... implementation details

        println!("✅ Distributed computing system stopped");
        Ok(())
    }

    // Private helper methods for enhanced distributed computing

    fn validate_task(&self, task: &DistributedTask) -> CoreResult<()> {
        // Validate task parameters
        if task.data.payload.is_empty() {
            return Err(CoreError::InvalidArgument(crate::error::ErrorContext::new(
                "Task data cannot be empty".to_string(),
            )));
        }

        if task.expected_duration > Duration::from_secs(24 * 3600) {
            return Err(CoreError::InvalidArgument(crate::error::ErrorContext::new(
                "Task duration exceeds maximum allowed (24 hours)".to_string(),
            )));
        }

        // Validate resource requirements
        if task.resources.min_cpu_cores == 0 {
            return Err(CoreError::InvalidArgument(crate::error::ErrorContext::new(
                "Task must specify CPU requirements".to_string(),
            )));
        }

        Ok(())
    }

    fn analyze_task_requirements(&self, task: &DistributedTask) -> CoreResult<TaskRequirements> {
        // Analyze computational requirements
        let compute_complexity = self.estimate_compute_complexity(task)?;
        let memory_intensity = self.estimate_memory_intensity(task)?;
        let io_requirements = self.estimate_io_requirements(task)?;
        let networkbandwidth = self.estimate_networkbandwidth(task)?;

        // Determine optimal node characteristics
        let preferred_node_type = if compute_complexity > 0.8 {
            NodeType::ComputeOptimized
        } else if memory_intensity > 0.8 {
            NodeType::MemoryOptimized
        } else if io_requirements > 0.8 {
            NodeType::StorageOptimized
        } else {
            NodeType::General
        };

        Ok(TaskRequirements {
            min_cpu_cores: (compute_complexity * 16.0) as u32,
            min_memory_gb: memory_intensity * 32.0,
            min_gpu_memory_gb: if compute_complexity > 0.8 {
                Some(memory_intensity * 16.0)
            } else {
                None
            },
            required_node_type: Some(preferred_node_type),
            min_networkbandwidth_mbps: networkbandwidth * 1000.0,
            min_storage_gb: io_requirements * 100.0,
            geographic_constraints: Vec::new(),
            compute_complexity,
            memory_intensity,
            io_requirements,
        })
    }

    fn find_suitable_nodes(&self, requirements: &TaskRequirements) -> CoreResult<Vec<NodeId>> {
        let cluster_manager = self.cluster_manager.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire cluster manager lock: {e}"
            )))
        })?;

        let availablenodes = cluster_manager.get_availablenodes()?;
        let mut suitable_nodes = Vec::new();

        for (nodeid, nodeinfo) in availablenodes {
            let suitability_score = self.calculate_node_suitability(&nodeinfo, requirements)?;

            if suitability_score > 0.6 {
                // Minimum suitability threshold
                suitable_nodes.push((nodeid, suitability_score));
            }
        }

        // Sort by suitability score (highest first)
        suitable_nodes.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

        // Return top 3 nodes for load distribution
        Ok(suitable_nodes
            .into_iter()
            .take(3)
            .map(|(id_, _)| id_)
            .collect())
    }

    fn calculate_node_suitability(
        &self,
        node: &crate::distributed::cluster::NodeInfo,
        requirements: &TaskRequirements,
    ) -> CoreResult<f64> {
        let mut score = 0.0;

        // Score based on node type match
        if let Some(required_type) = requirements.required_node_type {
            if node.node_type == required_type {
                score += 0.4;
            } else {
                score += 0.1; // Partial compatibility
            }
        } else {
            score += 0.2; // No preference
        }

        // Score based on resource availability
        let resource_score = self.calculate_resource_match_score(node, requirements)?;
        score += resource_score * 0.3;

        // Score based on current load (estimate from status)
        let load_factor = match node.status {
            crate::distributed::cluster::NodeStatus::Healthy => 0.8,
            crate::distributed::cluster::NodeStatus::Degraded => 0.5,
            crate::distributed::cluster::NodeStatus::Unhealthy => 0.1,
            _ => 0.3,
        };
        score += load_factor * 0.2;

        // Score based on network latency (default reasonable latency)
        let latency_score = 0.8; // Assume reasonable network latency
        score += latency_score * 0.1;

        Ok(score.min(1.0))
    }

    fn calculate_resource_match_score(
        &self,
        node: &crate::distributed::cluster::NodeInfo,
        requirements: &TaskRequirements,
    ) -> CoreResult<f64> {
        let mut score = 0.0;

        // CPU match
        if node.capabilities.cpu_cores as f64 >= requirements.min_cpu_cores as f64 {
            score += 0.25;
        }

        // Memory match
        if node.capabilities.memory_gb as f64 >= requirements.min_memory_gb {
            score += 0.25;
        }

        // Storage match
        if node.capabilities.disk_space_gb as f64 >= requirements.min_storage_gb {
            score += 0.25;
        }

        // Network match
        if node.capabilities.networkbandwidth_gbps * 1000.0
            >= requirements.min_networkbandwidth_mbps
        {
            score += 0.25;
        }

        Ok(score)
    }

    fn estimate_compute_complexity(&self, task: &DistributedTask) -> CoreResult<f64> {
        // Estimate based on task type and data size
        let base_complexity = match task.task_type {
            TaskType::MatrixOperation => 0.9,
            TaskType::MatrixMultiplication => 0.9,
            TaskType::MachineLearning => 0.8,
            TaskType::SignalProcessing => 0.7,
            TaskType::DataProcessing => 0.6,
            TaskType::Optimization => 0.8,
            TaskType::DataAnalysis => 0.6,
            TaskType::Simulation => 0.95,
            TaskType::Rendering => 0.85,
            TaskType::Custom(_) => 0.7,
        };

        // Adjust for data size
        let data_size_gb = task.data.size_bytes as f64 / (1024.0 * 1024.0 * 1024.0);
        let size_factor = (data_size_gb.log10() / 3.0).clamp(0.1, 1.0);

        Ok((base_complexity * size_factor).clamp(0.1, 1.0))
    }

    fn estimate_memory_intensity(&self, _task: &DistributedTask) -> CoreResult<f64> {
        // Simplified estimation - in practice would analyze task characteristics
        Ok(0.5)
    }

    fn estimate_io_requirements(&self, _task: &DistributedTask) -> CoreResult<f64> {
        // Simplified estimation - in practice would analyze I/O patterns
        Ok(0.3)
    }

    fn estimate_networkbandwidth(&self, _task: &DistributedTask) -> CoreResult<f64> {
        // Simplified estimation - in practice would analyze data transfer requirements
        Ok(0.4)
    }

    fn group_tasks_by_requirements(
        &self,
        tasks: &[DistributedTask],
        _analyses: &[TaskRequirements],
    ) -> CoreResult<HashMap<ResourceProfile, Vec<(DistributedTask, TaskRequirements)>>> {
        // Simplified grouping - in practice would use sophisticated analysis
        let mut groups = HashMap::new();

        for task in tasks {
            let requirements = self.analyze_task_requirements(task)?;
            let profile = ResourceProfile::from_analysis(&ResourceAnalysis {
                cpu_cores: requirements.min_cpu_cores as usize,
                memory_gb: requirements.min_memory_gb as usize,
                gpu_required: requirements.min_gpu_memory_gb.is_some(),
                network_intensive: requirements.min_networkbandwidth_mbps > 500.0,
                storage_intensive: requirements.min_storage_gb > 50.0,
            });

            groups
                .entry(profile)
                .or_insert_with(Vec::new)
                .push((task.clone(), requirements));
        }

        Ok(groups)
    }

    fn find_nodes_for_profile(&self, _profile: &ResourceProfile) -> CoreResult<Vec<NodeId>> {
        // Simplified implementation
        Ok(Vec::new())
    }

    fn update_submission_stats(&self, _elapsed: Duration) -> CoreResult<()> {
        // Update internal statistics
        Ok(())
    }

    fn register_task_formonitoring(&self, taskid: &TaskId) -> CoreResult<()> {
        let fault_tolerance = self.fault_tolerance.lock().map_err(|e| {
            CoreError::InvalidArgument(crate::error::ErrorContext::new(format!(
                "Failed to acquire fault tolerance lock: {e}"
            )))
        })?;

        fault_tolerance.register_task_for_advancedmonitoring(taskid)
    }

    fn wrap_with_fault_tolerance(
        &self,
        mut task: DistributedTask,
        config: FaultToleranceConfig,
    ) -> CoreResult<DistributedTask> {
        // Apply fault tolerance configuration to task
        task.fault_tolerance = config.level;
        task.maxretries = config.maxretries;
        task.checkpoint_interval = Some(config.checkpoint_interval);
        task.requires_checkpointing = true;

        Ok(task)
    }
}

// Placeholder implementations for resource manager and load balancer
impl DistributedResourceManager {
    pub fn new(_config: &DistributedComputingConfig) -> CoreResult<Self> {
        Ok(Self)
    }
}

impl IntelligentLoadBalancer {
    pub fn new(_config: &DistributedComputingConfig) -> CoreResult<Self> {
        Ok(Self)
    }
}

impl Default for AdvancedDistributedComputer {
    fn default() -> Self {
        Self::new().expect("Failed to create default distributed computer")
    }
}

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

    #[test]
    fn test_distributed_computer_creation() {
        let computer = AdvancedDistributedComputer::new();
        assert!(computer.is_ok());
    }

    #[test]
    fn test_distributed_computing_config() {
        let _config = DistributedComputingConfig::default();
        assert!(_config.enable_auto_discovery);
        assert!(_config.enable_load_balancing);
        assert!(_config.enable_fault_tolerance);
        assert_eq!(_config.max_nodes, 256);
    }

    #[test]
    fn test_cluster_manager_creation() {
        let _config = DistributedComputingConfig::default();
        let manager = ClusterManager::new(&_config);
        assert!(manager.is_ok());
    }

    #[test]
    fn test_task_scheduler_creation() {
        let _config = DistributedComputingConfig::default();
        let scheduler = AdaptiveTaskScheduler::new(&_config);
        assert!(scheduler.is_ok());
    }
}