voirs-sdk 0.1.0-rc.1

use super::*;
use std::collections::{HashMap, VecDeque};
use std::sync::atomic::{AtomicU32, AtomicU64, Ordering};
use std::time::Duration;
use tokio::sync::{RwLock, Semaphore};
use tokio::time::{interval, timeout, Instant};
use uuid::Uuid;

/// Distributed processing manager for VoiRS synthesis workloads
pub struct VoirsDistributedProcessing {
    config: ProcessingConfig,
    job_scheduler: Arc<JobScheduler>,
    worker_manager: Arc<WorkerManager>,
    load_balancer: Arc<LoadBalancer>,
    fault_manager: Arc<FaultManager>,
    cost_optimizer: Arc<CostOptimizer>,
}

struct JobScheduler {
    job_queue: Arc<RwLock<JobQueue>>,
    running_jobs: Arc<RwLock<HashMap<String, RunningJob>>>,
    job_history: Arc<RwLock<VecDeque<CompletedJob>>>,
    scheduler_stats: Arc<RwLock<SchedulerStats>>,
}

struct JobQueue {
    high_priority: VecDeque<ProcessingJob>,
    normal_priority: VecDeque<ProcessingJob>,
    low_priority: VecDeque<ProcessingJob>,
    critical_priority: VecDeque<ProcessingJob>,
}

struct RunningJob {
    job: ProcessingJob,
    worker_id: String,
    started_at: DateTime<Utc>,
    progress: f32,
    estimated_completion: Option<DateTime<Utc>>,
}

struct CompletedJob {
    job_id: String,
    status: JobStatus,
    started_at: DateTime<Utc>,
    completed_at: DateTime<Utc>,
    worker_id: String,
    cost: f64,
    resource_usage: ResourceUsage,
}

struct ResourceUsage {
    cpu_seconds: f64,
    memory_mb_seconds: f64,
    gpu_seconds: Option<f64>,
    network_mb: f64,
}

struct SchedulerStats {
    total_jobs: AtomicU64,
    completed_jobs: AtomicU64,
    failed_jobs: AtomicU64,
    average_wait_time: f64,
    average_execution_time: f64,
    throughput_per_hour: f64,
}

struct WorkerManager {
    workers: Arc<RwLock<HashMap<String, Worker>>>,
    worker_pools: Arc<RwLock<HashMap<String, WorkerPool>>>,
    auto_scaling: Arc<AutoScaler>,
}

#[derive(Clone)]
struct Worker {
    id: String,
    status: WorkerStatus,
    capabilities: WorkerCapabilities,
    current_job: Option<String>,
    performance_metrics: WorkerPerformanceMetrics,
    health_status: WorkerHealth,
    last_heartbeat: DateTime<Utc>,
    cost_per_hour: f64,
}

#[derive(Debug, Clone)]
struct WorkerCapabilities {
    cpu_cores: u32,
    memory_mb: u32,
    gpu_available: bool,
    gpu_memory_mb: Option<u32>,
    supported_job_types: Vec<JobType>,
    max_concurrent_jobs: u32,
}

#[derive(Debug)]
struct WorkerPerformanceMetrics {
    jobs_completed: AtomicU32,
    jobs_failed: AtomicU32,
    total_processing_time: Duration,
    average_job_time: Duration,
    efficiency_score: f32,
    reliability_score: f32,
}

impl Clone for WorkerPerformanceMetrics {
    fn clone(&self) -> Self {
        Self {
            jobs_completed: AtomicU32::new(self.jobs_completed.load(Ordering::Relaxed)),
            jobs_failed: AtomicU32::new(self.jobs_failed.load(Ordering::Relaxed)),
            total_processing_time: self.total_processing_time,
            average_job_time: self.average_job_time,
            efficiency_score: self.efficiency_score,
            reliability_score: self.reliability_score,
        }
    }
}

#[derive(Debug, Clone)]
struct WorkerHealth {
    cpu_usage: f32,
    memory_usage: f32,
    gpu_usage: Option<f32>,
    disk_usage: f32,
    network_latency: Duration,
    error_rate: f32,
}

struct WorkerPool {
    name: String,
    workers: Vec<String>,
    min_size: u32,
    max_size: u32,
    current_size: u32,
    target_size: u32,
    pool_type: PoolType,
}

#[derive(Debug, Clone)]
enum PoolType {
    OnDemand,
    Spot,
    Reserved,
    Preemptible,
}

struct AutoScaler {
    scaling_policies: Vec<ScalingPolicy>,
    scaling_history: VecDeque<ScalingEvent>,
    cooldown_period: Duration,
    last_scaling_action: Option<DateTime<Utc>>,
}

struct ScalingPolicy {
    name: String,
    condition: ScalingCondition,
    action: ScalingAction,
    enabled: bool,
}

#[derive(Debug, Clone)]
enum ScalingCondition {
    QueueLength(u32),
    CpuUtilization(f32),
    MemoryUtilization(f32),
    WaitTime(Duration),
    Custom(String),
}

#[derive(Debug, Clone)]
enum ScalingAction {
    Up(u32),
    Down(u32),
    ToTarget(u32),
}

struct ScalingEvent {
    timestamp: DateTime<Utc>,
    action: ScalingAction,
    reason: String,
    old_size: u32,
    new_size: u32,
}

struct LoadBalancer {
    strategy: LoadBalancingStrategy,
    worker_weights: HashMap<String, f32>,
    routing_rules: Vec<RoutingRule>,
    balancer_stats: LoadBalancerStats,
}

struct RoutingRule {
    condition: RoutingCondition,
    target_workers: Vec<String>,
    weight: f32,
}

#[derive(Debug, Clone)]
enum RoutingCondition {
    JobType(JobType),
    JobSize(u64),
    RequiredCapabilities(WorkerCapabilities),
    Custom(String),
}

struct LoadBalancerStats {
    total_requests: AtomicU64,
    successful_routings: AtomicU64,
    failed_routings: AtomicU64,
    average_response_time: f64,
    worker_utilization: HashMap<String, f32>,
}

struct FaultManager {
    failure_detectors: Vec<Box<dyn FailureDetector>>,
    recovery_strategies: HashMap<FailureType, RecoveryStrategy>,
    circuit_breakers: HashMap<String, CircuitBreaker>,
    fault_history: VecDeque<FaultEvent>,
}

trait FailureDetector: Send + Sync {
    fn detect_failures(&self, workers: &HashMap<String, Worker>) -> Vec<DetectedFailure>;
    fn get_detector_name(&self) -> &str;
}

#[derive(Debug, Clone, Eq, Hash, PartialEq)]
enum FailureType {
    WorkerUnresponsive,
    JobTimeout,
    ResourceExhaustion,
    NetworkPartition,
    SystemError,
}

#[derive(Debug, Clone)]
enum RecoveryStrategy {
    Restart,
    Migrate,
    Retry,
    Abort,
    Fallback(String),
}

#[derive(Debug)]
struct DetectedFailure {
    failure_type: FailureType,
    affected_worker: String,
    severity: FailureSeverity,
    detected_at: DateTime<Utc>,
    details: String,
}

#[derive(Debug, Clone)]
enum FailureSeverity {
    Low,
    Medium,
    High,
    Critical,
}

struct CircuitBreaker {
    state: CircuitBreakerState,
    failure_count: AtomicU32,
    success_count: AtomicU32,
    last_failure: Option<DateTime<Utc>>,
    failure_threshold: u32,
    recovery_timeout: Duration,
}

#[derive(Debug, Clone)]
enum CircuitBreakerState {
    Closed,
    Open,
    HalfOpen,
}

struct FaultEvent {
    id: String,
    failure_type: FailureType,
    affected_components: Vec<String>,
    recovery_action: RecoveryStrategy,
    timestamp: DateTime<Utc>,
    resolved: bool,
    resolution_time: Option<DateTime<Utc>>,
}

struct CostOptimizer {
    pricing_models: HashMap<String, PricingModel>,
    cost_history: VecDeque<CostEvent>,
    optimization_rules: Vec<OptimizationRule>,
    budget_constraints: BudgetConstraints,
}

struct PricingModel {
    provider: String,
    instance_type: String,
    cpu_cost_per_hour: f64,
    memory_cost_per_gb_hour: f64,
    gpu_cost_per_hour: Option<f64>,
    storage_cost_per_gb_month: f64,
    network_cost_per_gb: f64,
    spot_discount: Option<f32>,
}

struct CostEvent {
    timestamp: DateTime<Utc>,
    job_id: String,
    worker_id: String,
    cost: f64,
    resource_usage: ResourceUsage,
}

struct OptimizationRule {
    name: String,
    condition: CostCondition,
    action: CostAction,
    enabled: bool,
    priority: u32,
}

#[derive(Debug, Clone)]
enum CostCondition {
    HourlyCostExceeds(f64),
    UtilizationBelow(f32),
    QueueEmpty,
    SpotPriceBelow(f64),
}

#[derive(Debug, Clone)]
enum CostAction {
    SwitchToSpot,
    DownscaleWorkers(u32),
    UpgradeInstance(String),
    DowngradeInstance(String),
    ScheduleShutdown(Duration),
}

struct BudgetConstraints {
    daily_budget: Option<f64>,
    monthly_budget: Option<f64>,
    per_job_budget: Option<f64>,
    alert_threshold: f32,
}

impl VoirsDistributedProcessing {
    pub async fn new(config: ProcessingConfig) -> Result<Self> {
        let job_scheduler = Arc::new(JobScheduler::new());
        let worker_manager = Arc::new(WorkerManager::new().await?);
        let load_balancer = Arc::new(LoadBalancer::new(config.load_balancing.clone()));
        let fault_manager = Arc::new(FaultManager::new());
        let cost_optimizer = Arc::new(CostOptimizer::new());

        let processing = Self {
            config,
            job_scheduler,
            worker_manager,
            load_balancer,
            fault_manager,
            cost_optimizer,
        };

        // Start background services
        processing.start_scheduler().await?;
        processing.start_health_monitor().await?;
        processing.start_auto_scaler().await?;

        Ok(processing)
    }

    async fn start_scheduler(&self) -> Result<()> {
        let job_scheduler = self.job_scheduler.clone();
        let worker_manager = self.worker_manager.clone();
        let load_balancer = self.load_balancer.clone();

        tokio::spawn(async move {
            let mut scheduler_interval = interval(Duration::from_secs(1));

            loop {
                scheduler_interval.tick().await;
                let _ = Self::run_scheduler_cycle(
                    job_scheduler.clone(),
                    worker_manager.clone(),
                    load_balancer.clone(),
                )
                .await;
            }
        });

        Ok(())
    }

    async fn run_scheduler_cycle(
        job_scheduler: Arc<JobScheduler>,
        worker_manager: Arc<WorkerManager>,
        load_balancer: Arc<LoadBalancer>,
    ) -> Result<()> {
        let next_job = job_scheduler.get_next_job().await;
        if let Some(job) = next_job {
            let workers = worker_manager.get_available_workers().await;
            if let Some(worker_id) = load_balancer.select_worker(&job, &workers).await? {
                let _ = worker_manager.assign_job(&worker_id, job).await;
            }
        }
        Ok(())
    }

    async fn start_health_monitor(&self) -> Result<()> {
        let worker_manager = self.worker_manager.clone();
        let fault_manager = self.fault_manager.clone();

        tokio::spawn(async move {
            let mut health_interval = interval(Duration::from_secs(30));

            loop {
                health_interval.tick().await;
                let _ = Self::run_health_check(worker_manager.clone(), fault_manager.clone()).await;
            }
        });

        Ok(())
    }

    async fn run_health_check(
        worker_manager: Arc<WorkerManager>,
        fault_manager: Arc<FaultManager>,
    ) -> Result<()> {
        let workers = worker_manager.get_all_workers().await;
        let failures = fault_manager.detect_failures(&workers).await;

        for failure in failures {
            let _ = fault_manager.handle_failure(failure).await;
        }

        Ok(())
    }

    async fn start_auto_scaler(&self) -> Result<()> {
        let worker_manager = self.worker_manager.clone();
        let job_scheduler = self.job_scheduler.clone();

        tokio::spawn(async move {
            let mut scaling_interval = interval(Duration::from_secs(60));

            loop {
                scaling_interval.tick().await;
                let _ = Self::run_auto_scaling(worker_manager.clone(), job_scheduler.clone()).await;
            }
        });

        Ok(())
    }

    async fn run_auto_scaling(
        worker_manager: Arc<WorkerManager>,
        job_scheduler: Arc<JobScheduler>,
    ) -> Result<()> {
        let queue_length = job_scheduler.get_queue_length().await;
        let worker_utilization = worker_manager.get_average_utilization().await;

        // Simple scaling logic - scale up if queue is long or utilization is high
        if queue_length > 10 || worker_utilization > 0.8 {
            let _ = worker_manager.scale_workers_up(1).await;
        } else if queue_length == 0 && worker_utilization < 0.2 {
            let _ = worker_manager.scale_workers_down(1).await;
        }

        Ok(())
    }

    pub async fn get_cluster_stats(&self) -> Result<ClusterStats> {
        let workers = self.worker_manager.get_all_workers().await;
        let queue_stats = self.job_scheduler.get_queue_stats().await;
        let cost_stats = self.cost_optimizer.get_cost_stats().await;

        Ok(ClusterStats {
            total_workers: workers.len() as u32,
            active_workers: workers
                .values()
                .filter(|w| matches!(w.status, WorkerStatus::Busy))
                .count() as u32,
            total_jobs_queued: queue_stats.total_queued,
            total_jobs_running: queue_stats.total_running,
            average_queue_wait_time: queue_stats.average_wait_time,
            cluster_utilization: self.worker_manager.get_average_utilization().await,
            hourly_cost: cost_stats.current_hourly_cost,
            efficiency_score: self.calculate_efficiency_score().await,
        })
    }

    async fn calculate_efficiency_score(&self) -> f32 {
        // Comprehensive efficiency calculation based on multiple factors
        let utilization = self.worker_manager.get_average_utilization().await;
        let cost_stats = self.cost_optimizer.get_cost_stats().await;

        // Calculate cost efficiency: jobs completed per dollar spent
        let cost_efficiency = if cost_stats.current_hourly_cost > 0.0 {
            let jobs_per_hour = self.job_scheduler.get_throughput_per_hour().await;
            (jobs_per_hour / cost_stats.current_hourly_cost).min(1.0) as f32
        } else {
            1.0 // Perfect efficiency if no cost
        };

        // Calculate resource efficiency: actual usage vs capacity
        let resource_efficiency = utilization;

        // Calculate reliability factor based on failure rates
        let reliability_factor = self.calculate_reliability_factor().await;

        // Weighted average of different efficiency metrics
        let weights = (0.4, 0.3, 0.3); // (utilization, cost, reliability)
        resource_efficiency * weights.0
            + cost_efficiency * weights.1
            + reliability_factor * weights.2
    }

    async fn calculate_reliability_factor(&self) -> f32 {
        let scheduler_stats = self.job_scheduler.scheduler_stats.read().await;
        let total_jobs = scheduler_stats.total_jobs.load(Ordering::Relaxed);
        let failed_jobs = scheduler_stats.failed_jobs.load(Ordering::Relaxed);

        if total_jobs == 0 {
            return 1.0; // Perfect reliability if no jobs processed
        }

        let success_rate = 1.0 - (failed_jobs as f32 / total_jobs as f32);
        success_rate.max(0.0)
    }
}

#[derive(Debug, Clone)]
struct QueueStats {
    total_queued: u32,
    total_running: u32,
    average_wait_time: Duration,
}

#[derive(Debug, Clone)]
struct CostStats {
    current_hourly_cost: f64,
    daily_cost: f64,
    monthly_cost: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClusterStats {
    pub total_workers: u32,
    pub active_workers: u32,
    pub total_jobs_queued: u32,
    pub total_jobs_running: u32,
    pub average_queue_wait_time: Duration,
    pub cluster_utilization: f32,
    pub hourly_cost: f64,
    pub efficiency_score: f32,
}

impl JobScheduler {
    fn new() -> Self {
        Self {
            job_queue: Arc::new(RwLock::new(JobQueue::new())),
            running_jobs: Arc::new(RwLock::new(HashMap::new())),
            job_history: Arc::new(RwLock::new(VecDeque::new())),
            scheduler_stats: Arc::new(RwLock::new(SchedulerStats::new())),
        }
    }

    async fn get_next_job(&self) -> Option<ProcessingJob> {
        let mut queue = self.job_queue.write().await;

        // Priority order: Critical > High > Normal > Low
        if let Some(job) = queue.critical_priority.pop_front() {
            return Some(job);
        }
        if let Some(job) = queue.high_priority.pop_front() {
            return Some(job);
        }
        if let Some(job) = queue.normal_priority.pop_front() {
            return Some(job);
        }
        queue.low_priority.pop_front()
    }

    async fn get_queue_length(&self) -> u32 {
        let queue = self.job_queue.read().await;
        (queue.critical_priority.len()
            + queue.high_priority.len()
            + queue.normal_priority.len()
            + queue.low_priority.len()) as u32
    }

    async fn get_queue_stats(&self) -> QueueStats {
        let queue = self.job_queue.read().await;
        let running = self.running_jobs.read().await;
        let stats = self.scheduler_stats.read().await;

        QueueStats {
            total_queued: (queue.critical_priority.len()
                + queue.high_priority.len()
                + queue.normal_priority.len()
                + queue.low_priority.len()) as u32,
            total_running: running.len() as u32,
            average_wait_time: Duration::from_secs_f64(stats.average_wait_time),
        }
    }

    async fn get_throughput_per_hour(&self) -> f64 {
        let stats = self.scheduler_stats.read().await;
        stats.throughput_per_hour
    }
}

impl JobQueue {
    fn new() -> Self {
        Self {
            high_priority: VecDeque::new(),
            normal_priority: VecDeque::new(),
            low_priority: VecDeque::new(),
            critical_priority: VecDeque::new(),
        }
    }
}

impl SchedulerStats {
    fn new() -> Self {
        Self {
            total_jobs: AtomicU64::new(0),
            completed_jobs: AtomicU64::new(0),
            failed_jobs: AtomicU64::new(0),
            average_wait_time: 0.0,
            average_execution_time: 0.0,
            throughput_per_hour: 0.0,
        }
    }
}

impl WorkerManager {
    async fn new() -> Result<Self> {
        Ok(Self {
            workers: Arc::new(RwLock::new(HashMap::new())),
            worker_pools: Arc::new(RwLock::new(HashMap::new())),
            auto_scaling: Arc::new(AutoScaler::new()),
        })
    }

    async fn get_available_workers(&self) -> Vec<Worker> {
        let workers = self.workers.read().await;
        workers
            .values()
            .filter(|w| matches!(w.status, WorkerStatus::Idle))
            .cloned()
            .collect()
    }

    async fn get_all_workers(&self) -> HashMap<String, Worker> {
        self.workers.read().await.clone()
    }

    async fn assign_job(&self, worker_id: &str, job: ProcessingJob) -> Result<()> {
        let mut workers = self.workers.write().await;
        if let Some(worker) = workers.get_mut(worker_id) {
            worker.status = WorkerStatus::Busy;
            worker.current_job = Some(job.id.clone());
        }
        Ok(())
    }

    async fn get_average_utilization(&self) -> f32 {
        let workers = self.workers.read().await;
        if workers.is_empty() {
            return 0.0;
        }

        let busy_count = workers
            .values()
            .filter(|w| matches!(w.status, WorkerStatus::Busy))
            .count();

        busy_count as f32 / workers.len() as f32
    }

    async fn scale_workers_up(&self, count: u32) -> Result<()> {
        tracing::info!("Scaling up {} workers", count);

        let auto_scaler = &self.auto_scaling;
        let pools = self.worker_pools.read().await;

        for _ in 0..count {
            // Find the best pool to provision new worker
            let pool_name = self.select_optimal_pool_for_scaling(&pools).await;

            let worker_id = Uuid::new_v4().to_string();
            let worker = Worker {
                id: worker_id.clone(),
                status: WorkerStatus::Provisioning,
                capabilities: WorkerCapabilities {
                    cpu_cores: 4,
                    memory_mb: 8192,
                    gpu_available: false,
                    gpu_memory_mb: None,
                    supported_job_types: vec![JobType::Synthesis, JobType::Recognition],
                    max_concurrent_jobs: 2,
                },
                current_job: None,
                performance_metrics: WorkerPerformanceMetrics {
                    jobs_completed: AtomicU32::new(0),
                    jobs_failed: AtomicU32::new(0),
                    total_processing_time: Duration::from_secs(0),
                    average_job_time: Duration::from_secs(0),
                    efficiency_score: 0.8,
                    reliability_score: 0.9,
                },
                health_status: WorkerHealth {
                    cpu_usage: 0.0,
                    memory_usage: 0.0,
                    gpu_usage: None,
                    disk_usage: 0.1,
                    network_latency: Duration::from_millis(10),
                    error_rate: 0.0,
                },
                last_heartbeat: chrono::Utc::now(),
                cost_per_hour: 0.50, // $0.50/hour default
            };

            // Add worker to pool and worker list
            let mut workers = self.workers.write().await;
            workers.insert(worker_id.clone(), worker.clone());

            // Simulate provisioning process
            tokio::spawn(async move {
                tokio::time::sleep(Duration::from_secs(30)).await; // Simulate provisioning time
                tracing::info!("Worker {} provisioned successfully", worker_id);
            });

            // Record scaling event
            let scaling_event = ScalingEvent {
                timestamp: chrono::Utc::now(),
                action: ScalingAction::Up(1),
                reason: "Manual scale up requested".to_string(),
                old_size: workers.len() as u32 - 1,
                new_size: workers.len() as u32,
            };

            // Note: In a real implementation, this would:
            // 1. Call cloud provider APIs (AWS EC2, GCP Compute Engine, etc.)
            // 2. Configure networking and security groups
            // 3. Install and configure VoiRS worker software
            // 4. Register worker with load balancer
            // 5. Update monitoring and logging
        }

        Ok(())
    }

    async fn scale_workers_down(&self, count: u32) -> Result<()> {
        tracing::info!("Scaling down {} workers", count);

        let mut workers = self.workers.write().await;
        let mut removed_count = 0;

        // Select workers to remove (prefer idle workers with lowest efficiency)
        let mut candidates: Vec<_> = workers
            .values()
            .filter(|w| matches!(w.status, WorkerStatus::Idle))
            .cloned()
            .collect();

        // Sort by efficiency score (ascending) to remove least efficient workers first
        candidates.sort_by(|a, b| {
            a.performance_metrics
                .efficiency_score
                .partial_cmp(&b.performance_metrics.efficiency_score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        for worker in candidates.iter().take(count as usize) {
            // Gracefully drain the worker
            let worker_id = worker.id.clone();

            // Mark worker as draining
            if let Some(worker_mut) = workers.get_mut(&worker_id) {
                worker_mut.status = WorkerStatus::Draining;
            }

            // Wait for current jobs to complete (in a real implementation)
            // For now, we'll just simulate the process
            let worker_id_clone = worker_id.clone();
            tokio::spawn(async move {
                // Simulate graceful shutdown time
                tokio::time::sleep(Duration::from_secs(60)).await;
                tracing::info!("Worker {} drained and terminated", worker_id_clone);
            });

            removed_count += 1;

            // Record scaling event
            let scaling_event = ScalingEvent {
                timestamp: chrono::Utc::now(),
                action: ScalingAction::Down(1),
                reason: "Manual scale down requested".to_string(),
                old_size: workers.len() as u32,
                new_size: workers.len() as u32 - 1,
            };

            // Note: In a real implementation, this would:
            // 1. Drain existing jobs to other workers
            // 2. Update load balancer to stop routing to this worker
            // 3. Terminate cloud instances gracefully
            // 4. Clean up associated resources (storage, networking)
            // 5. Update billing and cost tracking
        }

        // Remove the workers from the active pool
        for worker in candidates.iter().take(count as usize) {
            workers.remove(&worker.id);
        }

        if removed_count < count {
            tracing::warn!(
                "Only removed {} workers out of {} requested (not enough idle workers)",
                removed_count,
                count
            );
        }

        Ok(())
    }

    async fn increment_worker_failed_jobs(&self, worker_id: &str) -> Result<()> {
        let workers = self.workers.read().await;
        if let Some(worker) = workers.get(worker_id) {
            worker
                .performance_metrics
                .jobs_failed
                .fetch_add(1, Ordering::Relaxed);
            tracing::debug!("Incremented failed job count for worker: {}", worker_id);
        }
        Ok(())
    }

    async fn signal_job_cancellation(&self, worker_id: &str, job_id: &str) -> Result<()> {
        tracing::info!(
            "Signaling job cancellation to worker {} for job {}",
            worker_id,
            job_id
        );

        // In a real implementation, this would:
        // 1. Send a cancellation signal through a worker communication channel
        // 2. Use message queues (Redis, RabbitMQ) or HTTP API calls to notify workers
        // 3. Set cancellation flags in shared storage that workers check periodically
        // 4. Use gRPC/WebSocket connections to send real-time cancellation requests
        // 5. Update job status to "Cancelling" to prevent further processing

        // For now, simulate the cancellation process
        let mut workers = self.workers.write().await;
        if let Some(worker) = workers.get_mut(worker_id) {
            // Clear the current job assignment
            worker.current_job = None;
            worker.status = WorkerStatus::Idle;

            tracing::info!(
                "Worker {} status updated to Idle after job {} cancellation",
                worker_id,
                job_id
            );
        } else {
            tracing::warn!("Worker {} not found for job cancellation signal", worker_id);
        }

        // Simulate network communication delay
        tokio::time::sleep(Duration::from_millis(10)).await;

        tracing::info!(
            "Job cancellation signal sent successfully to worker {}",
            worker_id
        );
        Ok(())
    }

    async fn select_optimal_pool_for_scaling(&self, pools: &HashMap<String, WorkerPool>) -> String {
        // Select pool with lowest utilization and available capacity
        let mut best_pool = "default".to_string();
        let mut lowest_utilization = f32::INFINITY;

        for (pool_name, pool) in pools {
            if pool.current_size < pool.max_size {
                let utilization = pool.current_size as f32 / pool.max_size as f32;
                if utilization < lowest_utilization {
                    lowest_utilization = utilization;
                    best_pool = pool_name.clone();
                }
            }
        }

        best_pool
    }
}

impl AutoScaler {
    fn new() -> Self {
        Self {
            scaling_policies: Vec::new(),
            scaling_history: VecDeque::new(),
            cooldown_period: Duration::from_secs(300), // 5 minutes
            last_scaling_action: None,
        }
    }
}

impl LoadBalancer {
    fn new(strategy: LoadBalancingStrategy) -> Self {
        Self {
            strategy,
            worker_weights: HashMap::new(),
            routing_rules: Vec::new(),
            balancer_stats: LoadBalancerStats::new(),
        }
    }

    async fn select_worker(
        &self,
        _job: &ProcessingJob,
        workers: &[Worker],
    ) -> Result<Option<String>> {
        if workers.is_empty() {
            return Ok(None);
        }

        match self.strategy {
            LoadBalancingStrategy::RoundRobin => {
                // Simple round-robin selection
                Ok(Some(workers[0].id.clone()))
            }
            LoadBalancingStrategy::LeastConnections => {
                // Select worker with least current jobs
                let worker = workers
                    .iter()
                    .min_by_key(|w| w.performance_metrics.jobs_completed.load(Ordering::Relaxed));
                Ok(worker.map(|w| w.id.clone()))
            }
            LoadBalancingStrategy::LatencyBased => {
                // Select worker with lowest latency
                let worker = workers
                    .iter()
                    .min_by_key(|w| w.health_status.network_latency);
                Ok(worker.map(|w| w.id.clone()))
            }
            LoadBalancingStrategy::ResourceBased => {
                // Select worker with most available resources
                let worker = workers.iter().min_by(|a, b| {
                    let a_load = a.health_status.cpu_usage + a.health_status.memory_usage;
                    let b_load = b.health_status.cpu_usage + b.health_status.memory_usage;
                    a_load
                        .partial_cmp(&b_load)
                        .unwrap_or(std::cmp::Ordering::Equal)
                });
                Ok(worker.map(|w| w.id.clone()))
            }
        }
    }
}

impl LoadBalancerStats {
    fn new() -> Self {
        Self {
            total_requests: AtomicU64::new(0),
            successful_routings: AtomicU64::new(0),
            failed_routings: AtomicU64::new(0),
            average_response_time: 0.0,
            worker_utilization: HashMap::new(),
        }
    }
}

impl FaultManager {
    fn new() -> Self {
        Self {
            failure_detectors: Vec::new(),
            recovery_strategies: HashMap::new(),
            circuit_breakers: HashMap::new(),
            fault_history: VecDeque::new(),
        }
    }

    async fn detect_failures(&self, workers: &HashMap<String, Worker>) -> Vec<DetectedFailure> {
        let mut failures = Vec::new();

        for detector in &self.failure_detectors {
            failures.extend(detector.detect_failures(workers));
        }

        failures
    }

    async fn handle_failure(&self, failure: DetectedFailure) -> Result<()> {
        tracing::warn!("Handling failure: {:?}", failure);

        if let Some(strategy) = self.recovery_strategies.get(&failure.failure_type) {
            match strategy {
                RecoveryStrategy::Restart => {
                    tracing::info!("Restarting worker: {}", failure.affected_worker);
                    self.execute_worker_restart(&failure.affected_worker)
                        .await?;
                }
                RecoveryStrategy::Migrate => {
                    tracing::info!("Migrating jobs from worker: {}", failure.affected_worker);
                    self.execute_job_migration(&failure.affected_worker).await?;
                }
                RecoveryStrategy::Retry => {
                    tracing::info!("Retrying failed operation");
                    self.execute_retry_logic(&failure).await?;
                }
                RecoveryStrategy::Abort => {
                    tracing::info!("Aborting failed operation");
                    self.execute_abort_logic(&failure).await?;
                }
                RecoveryStrategy::Fallback(fallback) => {
                    tracing::info!("Using fallback strategy: {}", fallback);
                    self.execute_fallback_logic(&failure, fallback).await?;
                }
            }
        } else {
            tracing::warn!(
                "No recovery strategy configured for failure type: {:?}",
                failure.failure_type
            );
            // Default to abort for unhandled failure types
            self.execute_abort_logic(&failure).await?;
        }

        Ok(())
    }

    /// Execute worker restart recovery strategy
    async fn execute_worker_restart(&self, worker_id: &str) -> Result<()> {
        tracing::info!("Executing worker restart for worker: {}", worker_id);

        // In a real implementation, this would:
        // 1. Stop the current worker process/container
        // 2. Clear the worker's state and resources
        // 3. Start a new worker instance with the same configuration
        // 4. Update the worker status and metrics
        // 5. Redistribute any pending jobs from the restarted worker

        // Simulate the restart process
        tokio::time::sleep(Duration::from_millis(100)).await;

        tracing::info!("Worker restart completed for: {}", worker_id);
        Ok(())
    }

    /// Execute job migration recovery strategy
    async fn execute_job_migration(&self, worker_id: &str) -> Result<()> {
        tracing::info!("Executing job migration from worker: {}", worker_id);

        // In a real implementation, this would:
        // 1. Identify all jobs currently running on the failed worker
        // 2. Find suitable alternative workers with sufficient capacity
        // 3. Transfer job state and data to the new workers
        // 4. Update job routing and tracking information
        // 5. Resume job execution on the new workers

        // Simulate the migration process
        tokio::time::sleep(Duration::from_millis(50)).await;

        tracing::info!("Job migration completed from worker: {}", worker_id);
        Ok(())
    }

    /// Execute retry logic recovery strategy
    async fn execute_retry_logic(&self, failure: &DetectedFailure) -> Result<()> {
        tracing::info!(
            "Executing retry logic for failure: {:?}",
            failure.failure_type
        );

        // In a real implementation, this would:
        // 1. Analyze the failure to determine if retry is appropriate
        // 2. Implement exponential backoff for retry attempts
        // 3. Track retry count to prevent infinite retry loops
        // 4. Consider circuit breaker patterns for repeated failures
        // 5. Retry the failed operation with potentially modified parameters

        // Simulate retry logic with exponential backoff
        let retry_delay = Duration::from_millis(200);
        tokio::time::sleep(retry_delay).await;

        tracing::info!(
            "Retry logic executed for failure: {:?}",
            failure.failure_type
        );
        Ok(())
    }

    /// Execute abort logic recovery strategy
    async fn execute_abort_logic(&self, failure: &DetectedFailure) -> Result<()> {
        tracing::info!(
            "Executing abort logic for failure: {:?}",
            failure.failure_type
        );

        // In a real implementation, this would:
        // 1. Cancel all related operations and jobs
        // 2. Clean up allocated resources
        // 3. Notify clients about the operation failure
        // 4. Log detailed failure information for analysis
        // 5. Update system metrics and monitoring dashboards

        // Simulate abort logic
        tokio::time::sleep(Duration::from_millis(10)).await;

        tracing::info!(
            "Abort logic executed for failure on worker: {}",
            failure.affected_worker
        );
        Ok(())
    }

    /// Execute fallback logic recovery strategy
    async fn execute_fallback_logic(
        &self,
        failure: &DetectedFailure,
        fallback_strategy: &str,
    ) -> Result<()> {
        tracing::info!(
            "Executing fallback logic '{}' for failure: {:?}",
            fallback_strategy,
            failure.failure_type
        );

        // In a real implementation, this would:
        // 1. Parse the fallback strategy configuration
        // 2. Switch to alternative processing methods or services
        // 3. Adjust quality or performance parameters as needed
        // 4. Route traffic to backup systems or degraded service modes
        // 5. Monitor fallback system performance and capacity

        match fallback_strategy {
            "degraded_quality" => {
                tracing::info!("Switching to degraded quality processing mode");
                // Lower quality settings to reduce resource requirements
            }
            "backup_service" => {
                tracing::info!("Routing to backup service");
                // Redirect traffic to backup processing service
            }
            "local_processing" => {
                tracing::info!("Falling back to local processing");
                // Process locally instead of distributed processing
            }
            _ => {
                tracing::warn!("Unknown fallback strategy: {}", fallback_strategy);
                // Default fallback behavior
            }
        }

        // Simulate fallback execution
        tokio::time::sleep(Duration::from_millis(75)).await;

        tracing::info!(
            "Fallback logic '{}' executed successfully",
            fallback_strategy
        );
        Ok(())
    }
}

impl CostOptimizer {
    fn new() -> Self {
        Self {
            pricing_models: HashMap::new(),
            cost_history: VecDeque::new(),
            optimization_rules: Vec::new(),
            budget_constraints: BudgetConstraints {
                daily_budget: None,
                monthly_budget: None,
                per_job_budget: None,
                alert_threshold: 0.8,
            },
        }
    }

    async fn get_cost_stats(&self) -> CostStats {
        let mut hourly_cost = 0.0;

        // Calculate costs based on pricing models and current usage
        for (provider, pricing) in &self.pricing_models {
            // For demonstration, assume some baseline usage
            // In a real implementation, this would get actual resource usage from workers
            let cpu_hours = 1.0; // Base CPU usage
            let memory_gb_hours = 4.0; // Base memory usage
            let storage_gb = 10.0; // Base storage usage
            let network_gb = 1.0; // Base network usage

            let provider_hourly_cost = cpu_hours * pricing.cpu_cost_per_hour
                + memory_gb_hours * pricing.memory_cost_per_gb_hour
                + network_gb * pricing.network_cost_per_gb
                + pricing.gpu_cost_per_hour.unwrap_or(0.0);

            hourly_cost += provider_hourly_cost;
        }

        // Apply spot instance discounts if available
        for pricing in self.pricing_models.values() {
            if let Some(discount) = pricing.spot_discount {
                hourly_cost *= 1.0 - discount as f64;
            }
        }

        let mut daily_cost = hourly_cost * 24.0;
        let mut monthly_cost = daily_cost * 30.0; // Approximate month

        // Add historical cost data
        let recent_costs: f64 = self
            .cost_history
            .iter()
            .take(24) // Last 24 hours
            .map(|event| event.cost)
            .sum();

        if recent_costs > 0.0 {
            // Use actual recent cost data if available
            hourly_cost = recent_costs / 24.0;
            daily_cost = recent_costs;
            monthly_cost = daily_cost * 30.0;
        }

        CostStats {
            current_hourly_cost: hourly_cost,
            daily_cost,
            monthly_cost,
        }
    }
}

#[async_trait::async_trait]
impl DistributedProcessing for VoirsDistributedProcessing {
    async fn submit_job(&self, mut job: ProcessingJob) -> Result<JobHandle> {
        job.id = Uuid::new_v4().to_string();

        let handle = JobHandle {
            id: job.id.clone(),
            status: JobStatus::Queued,
            created_at: Utc::now(),
            estimated_completion: None,
        };

        // Add job to appropriate priority queue
        let mut queue = self.job_scheduler.job_queue.write().await;
        match job.priority {
            JobPriority::Critical => queue.critical_priority.push_back(job),
            JobPriority::High => queue.high_priority.push_back(job),
            JobPriority::Normal => queue.normal_priority.push_back(job),
            JobPriority::Low => queue.low_priority.push_back(job),
        }

        Ok(handle)
    }

    async fn get_job_status(&self, job_id: &str) -> Result<JobStatus> {
        let running_jobs = self.job_scheduler.running_jobs.read().await;
        if let Some(running_job) = running_jobs.get(job_id) {
            return Ok(JobStatus::Running);
        }

        // Check completed jobs
        let history = self.job_scheduler.job_history.read().await;
        if let Some(completed_job) = history.iter().find(|j| j.job_id == job_id) {
            return Ok(completed_job.status.clone());
        }

        // Check queued jobs
        let queue = self.job_scheduler.job_queue.read().await;
        let in_queue = queue.critical_priority.iter().any(|j| j.id == job_id)
            || queue.high_priority.iter().any(|j| j.id == job_id)
            || queue.normal_priority.iter().any(|j| j.id == job_id)
            || queue.low_priority.iter().any(|j| j.id == job_id);

        if in_queue {
            Ok(JobStatus::Queued)
        } else {
            Err(VoirsError::config_error(format!(
                "Job {} not found",
                job_id
            )))
        }
    }

    async fn cancel_job(&self, job_id: &str) -> Result<()> {
        // Remove from queue if queued
        let mut queue = self.job_scheduler.job_queue.write().await;
        queue.critical_priority.retain(|j| j.id != job_id);
        queue.high_priority.retain(|j| j.id != job_id);
        queue.normal_priority.retain(|j| j.id != job_id);
        queue.low_priority.retain(|j| j.id != job_id);

        // Cancel if running
        let mut running_jobs = self.job_scheduler.running_jobs.write().await;
        if let Some(running_job) = running_jobs.remove(job_id) {
            // Signal worker to cancel the job
            let _ = self
                .worker_manager
                .signal_job_cancellation(&running_job.worker_id, job_id)
                .await;

            tracing::info!(
                "Cancelled running job {} on worker {}",
                job_id,
                running_job.worker_id
            );
        }

        Ok(())
    }

    async fn get_worker_stats(&self) -> Result<Vec<WorkerStats>> {
        let workers = self.worker_manager.get_all_workers().await;

        Ok(workers
            .values()
            .map(|worker| WorkerStats {
                id: worker.id.clone(),
                status: worker.status.clone(),
                current_jobs: if worker.current_job.is_some() { 1 } else { 0 },
                completed_jobs: worker
                    .performance_metrics
                    .jobs_completed
                    .load(Ordering::Relaxed),
                failed_jobs: worker
                    .performance_metrics
                    .jobs_failed
                    .load(Ordering::Relaxed),
                cpu_usage: worker.health_status.cpu_usage,
                memory_usage: worker.health_status.memory_usage,
                gpu_usage: worker.health_status.gpu_usage,
                last_heartbeat: worker.last_heartbeat,
            })
            .collect())
    }

    async fn scale_workers(&self, target_count: u32) -> Result<()> {
        let current_count = self.worker_manager.get_all_workers().await.len() as u32;

        if target_count > current_count {
            self.worker_manager
                .scale_workers_up(target_count - current_count)
                .await?;
        } else if target_count < current_count {
            self.worker_manager
                .scale_workers_down(current_count - target_count)
                .await?;
        }

        Ok(())
    }
}

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

    #[tokio::test]
    async fn test_distributed_processing_creation() {
        let config = ProcessingConfig {
            max_concurrent_jobs: 10,
            timeout_seconds: 3600,
            retry_count: 3,
            load_balancing: LoadBalancingStrategy::LeastConnections,
        };

        let processing = VoirsDistributedProcessing::new(config).await;
        assert!(processing.is_ok());
    }

    #[tokio::test]
    async fn test_job_submission() {
        let config = ProcessingConfig {
            max_concurrent_jobs: 10,
            timeout_seconds: 3600,
            retry_count: 3,
            load_balancing: LoadBalancingStrategy::RoundRobin,
        };

        let processing = VoirsDistributedProcessing::new(config).await.unwrap();

        let job = ProcessingJob {
            id: "test".to_string(),
            job_type: JobType::Synthesis,
            input_data: vec![1, 2, 3],
            parameters: HashMap::new(),
            priority: JobPriority::Normal,
            requirements: ResourceRequirements {
                cpu_cores: 2,
                memory_mb: 1024,
                gpu_required: false,
                gpu_memory_mb: None,
                max_execution_time: chrono::Duration::seconds(300),
            },
        };

        let handle = processing.submit_job(job).await.unwrap();
        assert!(!handle.id.is_empty());
        assert!(matches!(handle.status, JobStatus::Queued));
    }

    #[tokio::test]
    async fn test_job_status_query() {
        let config = ProcessingConfig {
            max_concurrent_jobs: 10,
            timeout_seconds: 3600,
            retry_count: 3,
            load_balancing: LoadBalancingStrategy::RoundRobin,
        };

        let processing = VoirsDistributedProcessing::new(config).await.unwrap();

        let job = ProcessingJob {
            id: "test".to_string(),
            job_type: JobType::Synthesis,
            input_data: vec![1, 2, 3],
            parameters: HashMap::new(),
            priority: JobPriority::Normal,
            requirements: ResourceRequirements {
                cpu_cores: 2,
                memory_mb: 1024,
                gpu_required: false,
                gpu_memory_mb: None,
                max_execution_time: chrono::Duration::seconds(300),
            },
        };

        let handle = processing.submit_job(job).await.unwrap();
        let status = processing.get_job_status(&handle.id).await.unwrap();
        assert!(matches!(status, JobStatus::Queued));
    }

    #[test]
    fn test_load_balancing_strategies() {
        let strategies = vec![
            LoadBalancingStrategy::RoundRobin,
            LoadBalancingStrategy::LeastConnections,
            LoadBalancingStrategy::LatencyBased,
            LoadBalancingStrategy::ResourceBased,
        ];

        assert_eq!(strategies.len(), 4);
    }

    #[test]
    fn test_job_queue_priority() {
        let queue = JobQueue::new();
        assert_eq!(queue.critical_priority.len(), 0);
        assert_eq!(queue.high_priority.len(), 0);
        assert_eq!(queue.normal_priority.len(), 0);
        assert_eq!(queue.low_priority.len(), 0);
    }
}