// Enterprise Go scheduler optimization framework
package scheduler

import (
    "context"
    "runtime"
    "sync"
    "sync/atomic"
    "time"
    "unsafe"
)

// EnterpriseSchedulerManager provides comprehensive scheduler optimization
type EnterpriseSchedulerManager struct {
    // Core optimization components
    loadBalancer        *AdvancedLoadBalancer
    workStealingEngine  *WorkStealingEngine
    affinityManager     *ProcessorAffinityManager
    
    // Performance optimization
    profileManager      *SchedulerProfileManager
    tuningEngine        *RuntimeTuningEngine
    latencyOptimizer    *LatencyOptimizer
    
    // Monitoring and analytics
    performanceMonitor  *ConcurrencyMonitor
    bottleneckDetector  *BottleneckDetector
    metricsCollector    *SchedulerMetrics
    
    // Advanced features
    numaOptimizer      *NUMAOptimizer
    lockOptimizer      *LockOptimizer
    cacheOptimizer     *CacheOptimizer
    
    // Configuration
    config             *SchedulerConfig
    
    // Thread safety
    mu                 sync.RWMutex
}

type SchedulerConfig struct {
    // Processor configuration
    MaxProcs               int
    ProcessorAffinity      ProcessorAffinityMode
    NUMAAwareness         bool
    HyperThreadingMode    HyperThreadingMode
    
    // Load balancing
    WorkStealingEnabled   bool
    LoadBalancingStrategy LoadBalancingStrategy
    MigrationThreshold    float64
    StealingLatency       time.Duration
    
    // Performance optimization
    LatencyOptimization   LatencyOptimizationMode
    ThroughputPriority    float64
    EnergyEfficiency      bool
    CacheOptimization     bool
    
    // Monitoring
    EnableProfiling       bool
    EnableMetrics        bool
    MonitoringInterval   time.Duration
    AlertThresholds      *AlertThresholds
}

type ProcessorAffinityMode int

const (
    AffinityModeDefault ProcessorAffinityMode = iota
    AffinityModeManual
    AffinityModeAutomatic
    AffinityModeNUMAAware
    AffinityModeLatencyOptimized
)

// AdvancedLoadBalancer provides sophisticated work distribution
type AdvancedLoadBalancer struct {
    strategy           LoadBalancingStrategy
    processors         []*ProcessorState
    globalQueue        *GlobalWorkQueue
    
    // Load analysis
    loadAnalyzer       *LoadAnalyzer
    patternDetector    *WorkloadPatternDetector
    predictor          *LoadPredictor
    
    // Optimization engines
    migrationEngine    *GoroutineMigrationEngine
    placementEngine    *WorkPlacementEngine
    scalingEngine      *DynamicScalingEngine
    
    // Performance tracking
    balancingMetrics   *LoadBalancingMetrics
    performanceTracker *BalancingPerformanceTracker
    
    // Configuration
    config            *LoadBalancerConfig
}

type LoadBalancingStrategy int

const (
    StrategyRoundRobin LoadBalancingStrategy = iota
    StrategyLeastLoaded
    StrategyWorkStealing
    StrategyAdaptive
    StrategyLatencyOptimized
    StrategyThroughputOptimized
    StrategyMLBased
)

// OptimizeScheduler performs comprehensive scheduler optimization
func (esm *EnterpriseSchedulerManager) OptimizeScheduler(ctx context.Context) error {
    esm.mu.Lock()
    defer esm.mu.Unlock()
    
    // Analyze current performance
    currentProfile, err := esm.profileManager.GenerateProfile(ctx)
    if err != nil {
        return fmt.Errorf("failed to generate performance profile: %w", err)
    }
    
    // Detect bottlenecks
    bottlenecks, err := esm.bottleneckDetector.DetectBottlenecks(currentProfile)
    if err != nil {
        return fmt.Errorf("bottleneck detection failed: %w", err)
    }
    
    // Optimize based on detected issues
    optimizations := esm.generateOptimizations(bottlenecks)
    
    // Apply optimizations
    for _, optimization := range optimizations {
        if err := esm.applyOptimization(ctx, optimization); err != nil {
            return fmt.Errorf("optimization application failed: %w", err)
        }
    }
    
    // Validate optimization effectiveness
    return esm.validateOptimizations(ctx, optimizations)
}

// applyOptimization applies a specific optimization
func (esm *EnterpriseSchedulerManager) applyOptimization(
    ctx context.Context, 
    optimization *SchedulerOptimization,
) error {
    
    switch optimization.Type {
    case OptimizationTypeProcessorAffinity:
        return esm.affinityManager.OptimizeAffinity(optimization.Parameters)
        
    case OptimizationTypeLoadBalancing:
        return esm.loadBalancer.ReconfigureStrategy(optimization.Parameters)
        
    case OptimizationTypeLatency:
        return esm.latencyOptimizer.ApplyOptimization(optimization.Parameters)
        
    case OptimizationTypeNUMA:
        return esm.numaOptimizer.OptimizeNUMAPlacement(optimization.Parameters)
        
    case OptimizationTypeCaching:
        return esm.cacheOptimizer.OptimizeCacheUsage(optimization.Parameters)
        
    default:
        return fmt.Errorf("unknown optimization type: %v", optimization.Type)
    }
}

// ProcessorAffinityManager manages CPU affinity optimization
type ProcessorAffinityManager struct {
    topology          *CPUTopology
    affinityMaps      map[int]*AffinityConfiguration
    numaNodes         []*NUMANode
    
    // Dynamic optimization
    adaptiveEngine    *AdaptiveAffinityEngine
    migrationTracker  *MigrationTracker
    performanceAnalyzer *AffinityPerformanceAnalyzer
    
    // Configuration
    config           *AffinityConfig
}

type CPUTopology struct {
    LogicalCores     int
    PhysicalCores    int
    SocketCount      int
    NUMANodes        int
    CacheHierarchy   *CacheHierarchy
    HyperThreading   bool
    
    // Performance characteristics
    CoreFrequencies  []float64
    CacheLatencies   map[CacheLevel]time.Duration
    MemoryBandwidth  map[int]float64  // Per NUMA node
}

// OptimizeAffinity optimizes processor affinity for workload
func (pam *ProcessorAffinityManager) OptimizeAffinity(
    parameters *OptimizationParameters,
) error {
    
    // Analyze current workload characteristics
    workloadProfile := pam.analyzeWorkload(parameters)
    
    // Determine optimal affinity configuration
    optimalConfig, err := pam.calculateOptimalAffinity(workloadProfile)
    if err != nil {
        return fmt.Errorf("affinity calculation failed: %w", err)
    }
    
    // Apply affinity configuration
    return pam.applyAffinityConfiguration(optimalConfig)
}

// analyzeWorkload analyzes workload characteristics for affinity optimization
func (pam *ProcessorAffinityManager) analyzeWorkload(
    parameters *OptimizationParameters,
) *WorkloadProfile {
    
    profile := &WorkloadProfile{
        GoroutineCount:     runtime.NumGoroutine(),
        ConcurrencyLevel:   pam.calculateConcurrencyLevel(),
        MemoryAccessPattern: pam.analyzeMemoryAccess(),
        CacheUtilization:   pam.analyzeCacheUsage(),
        NUMAActivity:       pam.analyzeNUMAActivity(),
    }
    
    // Analyze CPU utilization patterns
    profile.CPUUtilization = pam.analyzeCPUUtilization()
    
    // Analyze communication patterns
    profile.CommunicationPattern = pam.analyzeCommunicationPatterns()
    
    // Analyze workload type
    profile.WorkloadType = pam.classifyWorkloadType(profile)
    
    return profile
}

// WorkStealingEngine implements advanced work stealing algorithms
type WorkStealingEngine struct {
    processors        []*ProcessorWorkQueue
    stealingPolicy    StealingPolicy
    
    // Advanced algorithms
    victimSelector    *VictimSelector
    stealingStrategies map[WorkloadType]*StealingStrategy
    adaptiveThreshold *AdaptiveThresholdManager
    
    // Performance optimization
    stealingLatency   *LatencyTracker
    efficiencyMetrics *StealingEfficiencyMetrics
    fairnessTracker   *FairnessTracker
    
    // Configuration
    config           *WorkStealingConfig
}

type StealingPolicy int

const (
    StealingPolicyRandom StealingPolicy = iota
    StealingPolicyRoundRobin
    StealingPolicyLoadBased
    StealingPolicyLatencyOptimized
    StealingPolicyAdaptive
)

// StealWork implements advanced work stealing with multiple strategies
func (wse *WorkStealingEngine) StealWork(
    thiefProcessor int,
    workloadHint WorkloadType,
) (*Work, error) {
    
    // Select stealing strategy based on workload
    strategy := wse.selectStealingStrategy(workloadHint)
    
    // Find victim processor
    victim, err := wse.victimSelector.SelectVictim(
        thiefProcessor,
        strategy.VictimSelectionCriteria,
    )
    if err != nil {
        return nil, fmt.Errorf("victim selection failed: %w", err)
    }
    
    // Attempt to steal work
    work, stolen := wse.attemptSteal(thiefProcessor, victim, strategy)
    
    // Update metrics
    wse.efficiencyMetrics.RecordStealingAttempt(thiefProcessor, victim, stolen)
    
    if !stolen {
        return nil, ErrNoWorkAvailable
    }
    
    return work, nil
}

// selectStealingStrategy selects optimal stealing strategy
func (wse *WorkStealingEngine) selectStealingStrategy(
    workloadType WorkloadType,
) *StealingStrategy {
    
    if strategy, exists := wse.stealingStrategies[workloadType]; exists {
        return strategy
    }
    
    // Use adaptive strategy for unknown workload types
    return wse.stealingStrategies[WorkloadTypeAdaptive]
}

// LatencyOptimizer optimizes for ultra-low latency requirements
type LatencyOptimizer struct {
    targetLatency     time.Duration
    currentLatency    *LatencyTracker
    
    // Optimization techniques
    schedulingPolicy  *LatencyAwareScheduling
    cachePinning     *CachePinningManager
    processorBinding *ProcessorBindingManager
    
    // Real-time features
    priorityManager  *PriorityManager
    preemptionEngine *PreemptionEngine
    deadlineScheduler *DeadlineScheduler
    
    // Monitoring
    latencyMonitor   *RealTimeLatencyMonitor
    jitterAnalyzer   *JitterAnalyzer
    outlierDetector  *LatencyOutlierDetector
}

// ApplyOptimization applies latency-focused optimizations
func (lo *LatencyOptimizer) ApplyOptimization(
    parameters *OptimizationParameters,
) error {
    
    // Analyze current latency characteristics
    latencyProfile := lo.analyzeLatencyProfile()
    
    // Identify latency bottlenecks
    bottlenecks := lo.identifyLatencyBottlenecks(latencyProfile)
    
    // Apply targeted optimizations
    for _, bottleneck := range bottlenecks {
        if err := lo.optimizeBottleneck(bottleneck); err != nil {
            return fmt.Errorf("bottleneck optimization failed: %w", err)
        }
    }
    
    // Validate latency improvements
    return lo.validateLatencyImprovements()
}

// optimizeBottleneck applies specific optimization for identified bottleneck
func (lo *LatencyOptimizer) optimizeBottleneck(bottleneck *LatencyBottleneck) error {
    switch bottleneck.Type {
    case BottleneckTypeSchedulingOverhead:
        return lo.optimizeSchedulingOverhead(bottleneck)
        
    case BottleneckTypeContextSwitching:
        return lo.optimizeContextSwitching(bottleneck)
        
    case BottleneckTypeCacheContention:
        return lo.optimizeCacheContention(bottleneck)
        
    case BottleneckTypeMemoryLatency:
        return lo.optimizeMemoryLatency(bottleneck)
        
    case BottleneckTypeLockContention:
        return lo.optimizeLockContention(bottleneck)
        
    default:
        return fmt.Errorf("unknown bottleneck type: %v", bottleneck.Type)
    }
}

// NUMAOptimizer optimizes for NUMA topology awareness
type NUMAOptimizer struct {
    topology         *NUMATopology
    placementEngine  *NUMAPlacementEngine
    migrationEngine  *NUMAMigrationEngine
    
    // Performance optimization
    localityOptimizer *MemoryLocalityOptimizer
    bandwidthManager *MemoryBandwidthManager
    balancingEngine  *NUMABalancingEngine
    
    // Monitoring
    topologyMonitor  *NUMATopologyMonitor
    performanceTracker *NUMAPerformanceTracker
    
    // Configuration
    config          *NUMAConfig
}

type NUMATopology struct {
    Nodes           []*NUMANode
    NodeCount       int
    DistanceMatrix  [][]int
    
    // Bandwidth characteristics
    LocalBandwidth  map[int]float64
    RemoteBandwidth map[int]map[int]float64
    
    // Latency characteristics
    LocalLatency    map[int]time.Duration
    RemoteLatency   map[int]map[int]time.Duration
}

// OptimizeNUMAPlacement optimizes goroutine placement for NUMA efficiency
func (no *NUMAOptimizer) OptimizeNUMAPlacement(
    parameters *OptimizationParameters,
) error {
    
    // Analyze current NUMA utilization
    utilizationProfile := no.analyzeNUMAUtilization()
    
    // Identify placement opportunities
    opportunities := no.identifyPlacementOpportunities(utilizationProfile)
    
    // Apply placement optimizations
    for _, opportunity := range opportunities {
        if err := no.applyPlacementOptimization(opportunity); err != nil {
            return fmt.Errorf("placement optimization failed: %w", err)
        }
    }
    
    // Monitor and adjust
    return no.startContinuousOptimization()
}

// ConcurrencyMonitor provides comprehensive concurrency monitoring
type ConcurrencyMonitor struct {
    goroutineTracker    *GoroutineTracker
    schedulerMetrics    *SchedulerMetrics
    performanceAnalyzer *ConcurrencyPerformanceAnalyzer
    
    // Real-time monitoring
    realTimeMonitor    *RealTimeConcurrencyMonitor
    alertManager       *ConcurrencyAlertManager
    dashboardManager   *ConcurrencyDashboardManager
    
    // Analysis engines
    bottleneckAnalyzer *ConcurrencyBottleneckAnalyzer
    patternDetector    *ConcurrencyPatternDetector
    anomalyDetector    *ConcurrencyAnomalyDetector
    
    // Historical analysis
    trendAnalyzer      *ConcurrencyTrendAnalyzer
    forecastEngine     *ConcurrencyForecastEngine
    
    // Configuration
    config            *MonitoringConfig
}

// MonitorConcurrency starts comprehensive concurrency monitoring
func (cm *ConcurrencyMonitor) MonitorConcurrency(ctx context.Context) error {
    // Start goroutine tracking
    if err := cm.goroutineTracker.StartTracking(ctx); err != nil {
        return fmt.Errorf("goroutine tracking start failed: %w", err)
    }
    
    // Start performance monitoring
    if err := cm.performanceAnalyzer.StartMonitoring(ctx); err != nil {
        return fmt.Errorf("performance monitoring start failed: %w", err)
    }
    
    // Start real-time monitoring
    if err := cm.realTimeMonitor.StartRealtimeMonitoring(ctx); err != nil {
        return fmt.Errorf("real-time monitoring start failed: %w", err)
    }
    
    // Start anomaly detection
    if err := cm.anomalyDetector.StartDetection(ctx); err != nil {
        return fmt.Errorf("anomaly detection start failed: %w", err)
    }
    
    return nil
}

// GoroutineTracker provides advanced goroutine lifecycle tracking
type GoroutineTracker struct {
    activeGoroutines   map[uint64]*GoroutineInfo
    lifecycleEvents    *GoroutineLifecycleEvents
    stateTransitions   *StateTransitionTracker
    
    // Memory tracking
    memoryTracker     *GoroutineMemoryTracker
    stackTracker      *StackUsageTracker
    
    // Performance tracking
    executionTracker  *ExecutionTimeTracker
    blockingTracker   *BlockingTimeTracker
    
    // Configuration
    config           *GoroutineTrackingConfig
    
    // Thread safety
    mu               sync.RWMutex
}

type GoroutineInfo struct {
    ID               uint64
    StartTime        time.Time
    State            GoroutineState
    StackSize        int64
    
    // Execution statistics
    CPUTime          time.Duration
    WallTime         time.Duration
    BlockingTime     time.Duration
    WaitingTime      time.Duration
    
    // Memory statistics
    AllocatedMemory  int64
    StackMemory      int64
    
    // Location information
    CreationLocation *LocationInfo
    CurrentLocation  *LocationInfo
    
    // Performance characteristics
    Priority         int
    ProcessorAffinity int
    NUMANode         int
}

// StartTracking begins comprehensive goroutine tracking
func (gt *GoroutineTracker) StartTracking(ctx context.Context) error {
    gt.mu.Lock()
    defer gt.mu.Unlock()
    
    // Initialize tracking data structures
    gt.activeGoroutines = make(map[uint64]*GoroutineInfo)
    
    // Start lifecycle event monitoring
    if err := gt.lifecycleEvents.StartMonitoring(ctx); err != nil {
        return fmt.Errorf("lifecycle monitoring start failed: %w", err)
    }
    
    // Start state transition tracking
    if err := gt.stateTransitions.StartTracking(ctx); err != nil {
        return fmt.Errorf("state transition tracking start failed: %w", err)
    }
    
    // Start memory tracking
    if err := gt.memoryTracker.StartTracking(ctx); err != nil {
        return fmt.Errorf("memory tracking start failed: %w", err)
    }
    
    return nil
}

// RuntimeTuningEngine provides dynamic runtime optimization
type RuntimeTuningEngine struct {
    currentConfig    *RuntimeConfiguration
    tuningStrategies map[TuningObjective]*TuningStrategy
    
    // Performance feedback
    feedbackLoop     *PerformanceFeedbackLoop
    adaptationEngine *AdaptationEngine
    
    // Machine learning
    mlOptimizer      *MLBasedOptimizer
    patternLearner   *PerformancePatternLearner
    
    // Safety mechanisms
    rollbackManager  *ConfigurationRollbackManager
    safetyValidator  *TuningSafetyValidator
    
    // Configuration
    config          *TuningConfig
}

type TuningObjective int

const (
    ObjectiveLatency TuningObjective = iota
    ObjectiveThroughput
    ObjectiveMemoryEfficiency
    ObjectiveEnergyEfficiency
    ObjectiveBalanced
)

// TuneRuntime performs dynamic runtime optimization
func (rte *RuntimeTuningEngine) TuneRuntime(
    ctx context.Context,
    objective TuningObjective,
) error {
    
    // Get current performance baseline
    baseline, err := rte.collectPerformanceBaseline(ctx)
    if err != nil {
        return fmt.Errorf("baseline collection failed: %w", err)
    }
    
    // Select tuning strategy
    strategy := rte.tuningStrategies[objective]
    if strategy == nil {
        return fmt.Errorf("no strategy available for objective: %v", objective)
    }
    
    // Generate tuning recommendations
    recommendations, err := strategy.GenerateRecommendations(baseline)
    if err != nil {
        return fmt.Errorf("recommendation generation failed: %w", err)
    }
    
    // Validate safety of recommendations
    if err := rte.safetyValidator.ValidateRecommendations(recommendations); err != nil {
        return fmt.Errorf("safety validation failed: %w", err)
    }
    
    // Apply recommendations with rollback capability
    return rte.applyRecommendationsWithRollback(ctx, recommendations)
}

// Enterprise concurrency patterns and optimization
package concurrency

import (
    "context"
    "sync"
    "sync/atomic"
    "time"
)

// ConcurrencyPatternManager manages advanced concurrency patterns
type ConcurrencyPatternManager struct {
    // Pattern implementations
    workerPoolManager    *WorkerPoolManager
    pipelineManager     *PipelineManager
    fanOutManager       *FanOutManager
    circuitBreakerManager *CircuitBreakerManager
    
    // Advanced patterns
    actorSystemManager  *ActorSystemManager
    cspManager         *CSPManager
    stmManager         *STMManager
    
    // Optimization engines
    patternOptimizer   *PatternOptimizer
    loadBalancer       *ConcurrencyLoadBalancer
    resourceManager    *ConcurrencyResourceManager
    
    // Monitoring
    patternMonitor     *PatternMonitor
    performanceTracker *ConcurrencyPerformanceTracker
    
    // Configuration
    config            *ConcurrencyConfig
}

// WorkerPoolManager provides enterprise-grade worker pool management
type WorkerPoolManager struct {
    pools              map[string]*WorkerPool
    poolFactory        *WorkerPoolFactory
    scalingEngine      *DynamicScalingEngine
    
    // Load balancing
    loadBalancer       *WorkerPoolLoadBalancer
    workDistributor    *WorkDistributor
    
    // Performance optimization
    performanceOptimizer *WorkerPoolOptimizer
    resourceMonitor     *ResourceMonitor
    
    // Configuration
    config             *WorkerPoolConfig
    
    // Thread safety
    mu                 sync.RWMutex
}

type WorkerPool struct {
    name               string
    workers            []*Worker
    workQueue          chan *Work
    resultQueue        chan *WorkResult
    
    // Dynamic scaling
    minWorkers         int
    maxWorkers         int
    currentWorkers     int64
    scalingPolicy      *ScalingPolicy
    
    // Performance tracking
    throughputTracker  *ThroughputTracker
    latencyTracker     *LatencyTracker
    utilizationTracker *UtilizationTracker
    
    // Health monitoring
    healthChecker      *WorkerHealthChecker
    errorTracker       *ErrorTracker
    
    // Configuration
    config            *WorkerConfig
    
    // Lifecycle management
    ctx               context.Context
    cancel            context.CancelFunc
    wg                sync.WaitGroup
    
    // Thread safety
    mu                sync.RWMutex
}

// CreateOptimizedWorkerPool creates a highly optimized worker pool
func (wpm *WorkerPoolManager) CreateOptimizedWorkerPool(
    name string,
    config *WorkerPoolConfig,
) (*WorkerPool, error) {
    
    wpm.mu.Lock()
    defer wpm.mu.Unlock()
    
    // Check if pool already exists
    if _, exists := wpm.pools[name]; exists {
        return nil, fmt.Errorf("worker pool %s already exists", name)
    }
    
    // Create optimized worker pool
    pool, err := wpm.poolFactory.CreatePool(name, config)
    if err != nil {
        return nil, fmt.Errorf("pool creation failed: %w", err)
    }
    
    // Apply performance optimizations
    if err := wpm.performanceOptimizer.OptimizePool(pool); err != nil {
        return nil, fmt.Errorf("pool optimization failed: %w", err)
    }
    
    // Start monitoring
    if err := wpm.startPoolMonitoring(pool); err != nil {
        return nil, fmt.Errorf("monitoring start failed: %w", err)
    }
    
    // Register pool
    wpm.pools[name] = pool
    
    return pool, nil
}

// Start starts the worker pool with advanced lifecycle management
func (wp *WorkerPool) Start(ctx context.Context) error {
    wp.mu.Lock()
    defer wp.mu.Unlock()
    
    // Create pool context
    wp.ctx, wp.cancel = context.WithCancel(ctx)
    
    // Initialize workers
    for i := 0; i < wp.config.InitialWorkers; i++ {
        worker, err := wp.createWorker(i)
        if err != nil {
            return fmt.Errorf("worker creation failed: %w", err)
        }
        
        wp.workers = append(wp.workers, worker)
        
        // Start worker
        wp.wg.Add(1)
        go wp.runWorker(worker)
    }
    
    // Update current worker count
    atomic.StoreInt64(&wp.currentWorkers, int64(len(wp.workers)))
    
    // Start scaling engine
    if wp.scalingPolicy.Enabled {
        wp.wg.Add(1)
        go wp.runScalingEngine()
    }
    
    // Start health monitoring
    wp.wg.Add(1)
    go wp.runHealthMonitoring()
    
    return nil
}

// runWorker executes the main worker loop with optimization
func (wp *WorkerPool) runWorker(worker *Worker) {
    defer wp.wg.Done()
    
    // Worker-specific optimization
    wp.optimizeWorkerPerformance(worker)
    
    for {
        select {
        case <-wp.ctx.Done():
            return
            
        case work := <-wp.workQueue:
            // Process work with performance tracking
            result := wp.processWorkWithTracking(worker, work)
            
            // Send result
            select {
            case wp.resultQueue <- result:
            case <-wp.ctx.Done():
                return
            }
        }
    }
}

// optimizeWorkerPerformance applies worker-specific optimizations
func (wp *WorkerPool) optimizeWorkerPerformance(worker *Worker) {
    // Set processor affinity if configured
    if wp.config.ProcessorAffinity.Enabled {
        wp.setWorkerAffinity(worker)
    }
    
    // Configure memory allocation preferences
    if wp.config.MemoryOptimization.Enabled {
        wp.optimizeWorkerMemory(worker)
    }
    
    // Set priority if configured
    if wp.config.Priority != 0 {
        wp.setWorkerPriority(worker, wp.config.Priority)
    }
}

// PipelineManager manages high-performance processing pipelines
type PipelineManager struct {
    pipelines          map[string]*Pipeline
    stageFactory       *StageFactory
    
    // Optimization
    flowOptimizer      *PipelineFlowOptimizer
    bufferOptimizer    *BufferOptimizer
    backpressureManager *BackpressureManager
    
    // Monitoring
    flowMonitor        *PipelineFlowMonitor
    bottleneckDetector *PipelineBottleneckDetector
    
    // Configuration
    config            *PipelineConfig
    
    // Thread safety
    mu                sync.RWMutex
}

type Pipeline struct {
    name              string
    stages            []*PipelineStage
    
    // Flow control
    bufferSizes       []int
    backpressurePolicy *BackpressurePolicy
    
    // Performance optimization
    parallelismLevels []int
    affinityConfig    *AffinityConfiguration
    
    // Monitoring
    throughputMonitor *ThroughputMonitor
    latencyMonitor    *LatencyMonitor
    bottleneckTracker *BottleneckTracker
    
    // Configuration
    config           *PipelineStageConfig
    
    // Lifecycle
    ctx              context.Context
    cancel           context.CancelFunc
    wg               sync.WaitGroup
}

// CreateOptimizedPipeline creates a high-performance processing pipeline
func (pm *PipelineManager) CreateOptimizedPipeline(
    name string,
    stageConfigs []*PipelineStageConfig,
) (*Pipeline, error) {
    
    pm.mu.Lock()
    defer pm.mu.Unlock()
    
    // Create pipeline structure
    pipeline := &Pipeline{
        name:   name,
        stages: make([]*PipelineStage, 0, len(stageConfigs)),
    }
    
    // Create and optimize stages
    for i, stageConfig := range stageConfigs {
        stage, err := pm.stageFactory.CreateStage(i, stageConfig)
        if err != nil {
            return nil, fmt.Errorf("stage %d creation failed: %w", i, err)
        }
        
        // Apply stage-specific optimizations
        if err := pm.optimizeStage(stage, stageConfig); err != nil {
            return nil, fmt.Errorf("stage %d optimization failed: %w", i, err)
        }
        
        pipeline.stages = append(pipeline.stages, stage)
    }
    
    // Optimize pipeline flow
    if err := pm.flowOptimizer.OptimizePipeline(pipeline); err != nil {
        return nil, fmt.Errorf("pipeline optimization failed: %w", err)
    }
    
    // Register pipeline
    pm.pipelines[name] = pipeline
    
    return pipeline, nil
}

// ActorSystemManager implements enterprise actor model patterns
type ActorSystemManager struct {
    actorRegistry     *ActorRegistry
    messageRouter     *MessageRouter
    supervisorTree    *SupervisorTree
    
    // Scalability
    clusterManager    *ActorClusterManager
    distributionEngine *ActorDistributionEngine
    
    // Performance
    messageOptimizer  *MessageOptimizer
    routingOptimizer  *RoutingOptimizer
    
    // Fault tolerance
    faultDetector     *ActorFaultDetector
    recoveryManager   *ActorRecoveryManager
    
    // Configuration
    config           *ActorSystemConfig
}

type Actor struct {
    id               ActorID
    mailbox          chan *Message
    behavior         ActorBehavior
    
    // State management
    state            interface{}
    stateManager     *ActorStateManager
    
    // Performance optimization
    messageProcessor *OptimizedMessageProcessor
    batchProcessor   *MessageBatchProcessor
    
    // Fault tolerance
    supervisor       *ActorSupervisor
    recoveryPolicy   *RecoveryPolicy
    
    // Lifecycle
    ctx             context.Context
    cancel          context.CancelFunc
    
    // Thread safety
    mu              sync.RWMutex
}

// CreateActor creates an optimized actor with enterprise features
func (asm *ActorSystemManager) CreateActor(
    id ActorID,
    behavior ActorBehavior,
    config *ActorConfig,
) (*Actor, error) {
    
    // Create actor structure
    actor := &Actor{
        id:       id,
        mailbox:  make(chan *Message, config.MailboxSize),
        behavior: behavior,
    }
    
    // Initialize state manager
    actor.stateManager = NewActorStateManager(config.StateConfig)
    
    // Create optimized message processor
    actor.messageProcessor = NewOptimizedMessageProcessor(config.ProcessingConfig)
    
    // Setup batch processing if enabled
    if config.BatchProcessing.Enabled {
        actor.batchProcessor = NewMessageBatchProcessor(config.BatchProcessing)
    }
    
    // Register with supervisor
    supervisor, err := asm.supervisorTree.GetSupervisor(id)
    if err != nil {
        return nil, fmt.Errorf("supervisor assignment failed: %w", err)
    }
    actor.supervisor = supervisor
    
    // Register actor
    if err := asm.actorRegistry.RegisterActor(actor); err != nil {
        return nil, fmt.Errorf("actor registration failed: %w", err)
    }
    
    return actor, nil
}

// LockOptimizer optimizes lock usage and contention
type LockOptimizer struct {
    lockAnalyzer      *LockContentionAnalyzer
    lockProfiler      *LockProfiler
    
    // Optimization strategies
    lockFreeConverter *LockFreeConverter
    lockGranularityOptimizer *LockGranularityOptimizer
    rwLockOptimizer   *RWLockOptimizer
    
    // Alternative implementations
    atomicOptimizer   *AtomicOptimizer
    channelOptimizer  *ChannelBasedOptimizer
    
    // Monitoring
    contentionMonitor *ContentionMonitor
    deadlockDetector  *DeadlockDetector
    
    // Configuration
    config           *LockOptimizationConfig
}

// OptimizeLockUsage analyzes and optimizes lock usage patterns
func (lo *LockOptimizer) OptimizeLockUsage(
    ctx context.Context,
    codebase *CodebaseAnalysis,
) (*LockOptimizationReport, error) {
    
    // Analyze current lock usage
    lockUsage, err := lo.lockAnalyzer.AnalyzeLockUsage(codebase)
    if err != nil {
        return nil, fmt.Errorf("lock usage analysis failed: %w", err)
    }
    
    // Identify optimization opportunities
    opportunities := lo.identifyOptimizationOpportunities(lockUsage)
    
    // Generate optimization recommendations
    recommendations := make([]*LockOptimizationRecommendation, 0)
    
    for _, opportunity := range opportunities {
        recommendation := lo.generateRecommendation(opportunity)
        recommendations = append(recommendations, recommendation)
    }
    
    // Create optimization report
    report := &LockOptimizationReport{
        CurrentUsage:      lockUsage,
        Opportunities:     opportunities,
        Recommendations:  recommendations,
        EstimatedImprovement: lo.estimatePerformanceImprovement(recommendations),
    }
    
    return report, nil
}

// CacheOptimizer optimizes cache usage and memory access patterns
type CacheOptimizer struct {
    cacheAnalyzer     *CacheUsageAnalyzer
    accessPatternAnalyzer *MemoryAccessPatternAnalyzer
    
    // Optimization techniques
    localityOptimizer *DataLocalityOptimizer
    prefetchOptimizer *PrefetchOptimizer
    alignmentOptimizer *DataAlignmentOptimizer
    
    // Cache-specific optimizations
    l1Optimizer       *L1CacheOptimizer
    l2Optimizer       *L2CacheOptimizer
    l3Optimizer       *L3CacheOptimizer
    
    // Monitoring
    cacheMonitor      *CachePerformanceMonitor
    missAnalyzer      *CacheMissAnalyzer
    
    // Configuration
    config           *CacheOptimizationConfig
}

// OptimizeCacheUsage optimizes memory access patterns for cache efficiency
func (co *CacheOptimizer) OptimizeCacheUsage(
    parameters *OptimizationParameters,
) error {
    
    // Analyze current cache usage
    cacheProfile, err := co.cacheAnalyzer.AnalyzeCacheUsage()
    if err != nil {
        return fmt.Errorf("cache analysis failed: %w", err)
    }
    
    // Analyze memory access patterns
    accessPatterns, err := co.accessPatternAnalyzer.AnalyzeAccessPatterns()
    if err != nil {
        return fmt.Errorf("access pattern analysis failed: %w", err)
    }
    
    // Optimize data locality
    if err := co.localityOptimizer.OptimizeDataLocality(accessPatterns); err != nil {
        return fmt.Errorf("data locality optimization failed: %w", err)
    }
    
    // Optimize prefetching
    if err := co.prefetchOptimizer.OptimizePrefetching(accessPatterns); err != nil {
        return fmt.Errorf("prefetch optimization failed: %w", err)
    }
    
    // Optimize data alignment
    if err := co.alignmentOptimizer.OptimizeAlignment(cacheProfile); err != nil {
        return fmt.Errorf("alignment optimization failed: %w", err)
    }
    
    return nil
}

#!/bin/bash
# Enterprise Go scheduler and concurrency monitoring framework

set -euo pipefail

# Configuration
SCHEDULER_CONFIG_DIR="/etc/go-scheduler"
MONITORING_OUTPUT_DIR="/var/lib/scheduler-monitoring"
PROFILING_DATA_DIR="/var/lib/scheduler-profiling"
OPTIMIZATION_RESULTS_DIR="/var/lib/optimization-results"

# Setup comprehensive scheduler monitoring framework
setup_scheduler_monitoring() {
    local application_name="$1"
    local monitoring_profile="${2:-enterprise}"
    
    log_scheduler_event "INFO" "scheduler_monitoring" "setup" "started" "App: $application_name, Profile: $monitoring_profile"
    
    # Setup runtime monitoring
    setup_runtime_monitoring "$application_name" "$monitoring_profile"
    
    # Configure scheduler profiling
    configure_scheduler_profiling "$application_name" "$monitoring_profile"
    
    # Deploy performance analysis
    deploy_performance_analysis "$application_name"
    
    # Setup automated optimization
    setup_automated_optimization "$application_name"
    
    # Configure alerting and dashboards
    configure_scheduler_alerting "$application_name"
    
    log_scheduler_event "INFO" "scheduler_monitoring" "setup" "completed" "App: $application_name"
}

# Setup comprehensive runtime monitoring
setup_runtime_monitoring() {
    local application_name="$1"
    local monitoring_profile="$2"
    
    # Deploy runtime monitor
    kubectl apply -f - <<EOF
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: go-runtime-monitor
  namespace: monitoring
spec:
  selector:
    matchLabels:
      app: go-runtime-monitor
  template:
    metadata:
      labels:
        app: go-runtime-monitor
    spec:
      hostPID: true
      hostNetwork: true
      containers:
      - name: monitor
        image: registry.company.com/monitoring/go-runtime-monitor:latest
        ports:
        - containerPort: 8080
          name: http
        - containerPort: 6060
          name: pprof
        env:
        - name: TARGET_APPLICATION
          value: "$application_name"
        - name: MONITORING_PROFILE
          value: "$monitoring_profile"
        - name: GOMAXPROCS_MONITORING
          value: "true"
        - name: SCHEDULER_MONITORING
          value: "true"
        - name: NUMA_MONITORING
          value: "true"
        volumeMounts:
        - name: proc
          mountPath: /host/proc
          readOnly: true
        - name: sys
          mountPath: /host/sys
          readOnly: true
        - name: monitoring-config
          mountPath: /config
        - name: monitoring-data
          mountPath: /data
        securityContext:
          privileged: true
        resources:
          limits:
            cpu: 500m
            memory: 1Gi
          requests:
            cpu: 100m
            memory: 256Mi
      volumes:
      - name: proc
        hostPath:
          path: /proc
      - name: sys
        hostPath:
          path: /sys
      - name: monitoring-config
        configMap:
          name: runtime-monitoring-config-${application_name}
      - name: monitoring-data
        persistentVolumeClaim:
          claimName: monitoring-data-pvc
EOF

    # Create runtime monitoring configuration
    create_runtime_monitoring_config "$application_name" "$monitoring_profile"
}

# Create comprehensive runtime monitoring configuration
create_runtime_monitoring_config() {
    local application_name="$1"
    local monitoring_profile="$2"
    
    kubectl create configmap runtime-monitoring-config-${application_name} -n monitoring --from-literal=config.yaml="$(cat <<EOF
# Runtime monitoring configuration for $application_name
runtime_monitoring:
  target_application: "$application_name"
  monitoring_profile: "$monitoring_profile"
  
  # Scheduler monitoring
  scheduler:
    enable_gmp_monitoring: true
    enable_goroutine_tracking: true
    enable_processor_monitoring: true
    enable_work_stealing_analysis: true
    sampling_rate: "1ms"
    
  # Performance monitoring
  performance:
    enable_latency_tracking: true
    enable_throughput_monitoring: true
    enable_cpu_utilization: true
    enable_memory_profiling: true
    enable_gc_monitoring: true
    
  # NUMA monitoring
  numa:
    enable_topology_monitoring: true
    enable_affinity_tracking: true
    enable_memory_bandwidth: true
    enable_latency_analysis: true
    
  # Concurrency monitoring
  concurrency:
    enable_goroutine_lifecycle: true
    enable_channel_monitoring: true
    enable_lock_contention: true
    enable_sync_primitive_analysis: true
    
  # Real-time analysis
  realtime:
    enable_bottleneck_detection: true
    enable_anomaly_detection: true
    enable_pattern_recognition: true
    enable_predictive_analysis: true

# Monitoring thresholds
thresholds:
  goroutine_count_warning: 50000
  goroutine_count_critical: 100000
  scheduler_latency_warning: "1ms"
  scheduler_latency_critical: "5ms"
  cpu_utilization_warning: 80
  cpu_utilization_critical: 95
  gc_pause_warning: "10ms"
  gc_pause_critical: "50ms"
  memory_usage_warning: 80
  memory_usage_critical: 95

# Alert configuration
alerts:
  enabled: true
  channels:
    - slack
    - pagerduty
    - webhook
  conditions:
    scheduler_bottleneck: true
    performance_regression: true
    resource_exhaustion: true
    anomaly_detection: true

# Data collection
data_collection:
  collection_interval: "1s"
  aggregation_interval: "10s"
  retention_period: "30d"
  compression_enabled: true
  encryption_enabled: true

# Export configuration
export:
  prometheus_enabled: true
  influxdb_enabled: true
  elasticsearch_enabled: true
  custom_exporters: []

# Analysis configuration
analysis:
  enable_machine_learning: true
  enable_time_series_analysis: true
  enable_correlation_analysis: true
  enable_predictive_modeling: true
  
# Optimization recommendations
optimization:
  enable_recommendations: true
  auto_apply_safe_optimizations: false
  recommendation_confidence_threshold: 0.9
  optimization_categories:
    - scheduler_tuning
    - memory_optimization
    - numa_optimization
    - concurrency_optimization
EOF
)" --dry-run=client -o yaml | kubectl apply -f -
}

# Configure advanced scheduler profiling
configure_scheduler_profiling() {
    local application_name="$1"
    local monitoring_profile="$2"
    
    # Deploy scheduler profiler
    kubectl apply -f - <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: scheduler-profiler-${application_name}
  namespace: monitoring
spec:
  replicas: 1
  selector:
    matchLabels:
      app: scheduler-profiler
      target: ${application_name}
  template:
    metadata:
      labels:
        app: scheduler-profiler
        target: ${application_name}
    spec:
      containers:
      - name: profiler
        image: registry.company.com/monitoring/go-scheduler-profiler:latest
        ports:
        - containerPort: 8080
          name: http
        - containerPort: 6060
          name: pprof
        env:
        - name: TARGET_APPLICATION
          value: "$application_name"
        - name: PROFILING_MODE
          value: "comprehensive"
        - name: PROFILING_DURATION
          value: "60s"
        - name: PROFILING_INTERVAL
          value: "300s"
        - name: ENABLE_SCHEDULER_TRACE
          value: "true"
        - name: ENABLE_EXECUTION_TRACE
          value: "true"
        volumeMounts:
        - name: profiling-config
          mountPath: /config
        - name: profiling-output
          mountPath: /output
        - name: analysis-tools
          mountPath: /tools
        resources:
          limits:
            cpu: 1
            memory: 2Gi
          requests:
            cpu: 200m
            memory: 512Mi
      volumes:
      - name: profiling-config
        configMap:
          name: scheduler-profiling-config-${application_name}
      - name: profiling-output
        persistentVolumeClaim:
          claimName: profiling-output-pvc
      - name: analysis-tools
        configMap:
          name: profiling-analysis-tools
EOF

    # Create scheduler profiling configuration
    create_scheduler_profiling_config "$application_name"
}

# Deploy comprehensive performance analysis
deploy_performance_analysis() {
    local application_name="$1"
    
    # Create performance analysis job
    kubectl apply -f - <<EOF
apiVersion: batch/v1
kind: CronJob
metadata:
  name: scheduler-performance-analysis-${application_name}
  namespace: monitoring
spec:
  schedule: "0 */2 * * *"  # Every 2 hours
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: analyzer
            image: registry.company.com/monitoring/scheduler-performance-analyzer:latest
            command:
            - /bin/sh
            - -c
            - |
              # Comprehensive scheduler performance analysis
              
              echo "Starting scheduler performance analysis..."
              
              # Analyze scheduler traces
              /app/trace-analyzer \\
                --trace-dir /traces \\
                --analysis-mode comprehensive \\
                --output-format json \\
                --include-recommendations true
              
              # Analyze goroutine patterns
              /app/goroutine-analyzer \\
                --profiling-data /profiling \\
                --pattern-detection true \\
                --lifecycle-analysis true \\
                --memory-analysis true
              
              # Analyze concurrency patterns
              /app/concurrency-analyzer \\
                --concurrency-data /concurrency \\
                --bottleneck-detection true \\
                --optimization-suggestions true
              
              # Generate comprehensive report
              /app/report-generator \\
                --analysis-results /analysis \\
                --report-format "html,json,pdf" \\
                --include-visualizations true \\
                --upload-results true
            env:
            - name: TARGET_APPLICATION
              value: "$application_name"
            - name: ANALYSIS_DEPTH
              value: "comprehensive"
            - name: MACHINE_LEARNING_ENABLED
              value: "true"
            volumeMounts:
            - name: traces
              mountPath: /traces
            - name: profiling
              mountPath: /profiling
            - name: concurrency
              mountPath: /concurrency
            - name: analysis
              mountPath: /analysis
            - name: reports
              mountPath: /reports
            resources:
              limits:
                cpu: 4
                memory: 8Gi
              requests:
                cpu: 1
                memory: 2Gi
          volumes:
          - name: traces
            persistentVolumeClaim:
              claimName: traces-pvc
          - name: profiling
            persistentVolumeClaim:
              claimName: profiling-pvc
          - name: concurrency
            persistentVolumeClaim:
              claimName: concurrency-pvc
          - name: analysis
            persistentVolumeClaim:
              claimName: analysis-pvc
          - name: reports
            persistentVolumeClaim:
              claimName: reports-pvc
          restartPolicy: OnFailure
EOF

    # Setup real-time bottleneck detection
    setup_bottleneck_detection "$application_name"
}

# Setup automated optimization
setup_automated_optimization() {
    local application_name="$1"
    
    # Deploy optimization engine
    kubectl apply -f - <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: scheduler-optimizer-${application_name}
  namespace: monitoring
spec:
  replicas: 1
  selector:
    matchLabels:
      app: scheduler-optimizer
      target: ${application_name}
  template:
    metadata:
      labels:
        app: scheduler-optimizer
        target: ${application_name}
    spec:
      containers:
      - name: optimizer
        image: registry.company.com/monitoring/scheduler-optimizer:latest
        ports:
        - containerPort: 8080
        env:
        - name: TARGET_APPLICATION
          value: "$application_name"
        - name: OPTIMIZATION_MODE
          value: "adaptive"
        - name: AUTO_APPLY_OPTIMIZATIONS
          value: "false"  # Manual approval required
        - name: SAFETY_VALIDATION
          value: "strict"
        - name: ROLLBACK_ENABLED
          value: "true"
        volumeMounts:
        - name: optimization-config
          mountPath: /config
        - name: optimization-data
          mountPath: /data
        - name: optimization-results
          mountPath: /results
        resources:
          limits:
            cpu: 2
            memory: 4Gi
          requests:
            cpu: 500m
            memory: 1Gi
      volumes:
      - name: optimization-config
        configMap:
          name: scheduler-optimization-config-${application_name}
      - name: optimization-data
        persistentVolumeClaim:
          claimName: optimization-data-pvc
      - name: optimization-results
        persistentVolumeClaim:
          claimName: optimization-results-pvc
EOF

    # Create optimization engine configuration
    create_optimization_engine_config "$application_name"
}

# Configure comprehensive alerting
configure_scheduler_alerting() {
    local application_name="$1"
    
    # Deploy alerting service
    kubectl apply -f - <<EOF
apiVersion: apps/v1
kind: Deployment
metadata:
  name: scheduler-alerting-${application_name}
  namespace: monitoring
spec:
  replicas: 2
  selector:
    matchLabels:
      app: scheduler-alerting
      target: ${application_name}
  template:
    metadata:
      labels:
        app: scheduler-alerting
        target: ${application_name}
    spec:
      containers:
      - name: alerting
        image: registry.company.com/monitoring/scheduler-alerting:latest
        ports:
        - containerPort: 8080
        - containerPort: 9090
          name: metrics
        env:
        - name: TARGET_APPLICATION
          value: "$application_name"
        - name: ALERT_CHANNELS
          value: "slack,pagerduty,webhook"
        - name: ENABLE_PREDICTIVE_ALERTS
          value: "true"
        - name: ENABLE_ANOMALY_ALERTS
          value: "true"
        volumeMounts:
        - name: alerting-config
          mountPath: /config
        - name: alerting-rules
          mountPath: /rules
        - name: alerting-templates
          mountPath: /templates
        resources:
          limits:
            cpu: 500m
            memory: 1Gi
          requests:
            cpu: 100m
            memory: 256Mi
      volumes:
      - name: alerting-config
        configMap:
          name: scheduler-alerting-config-${application_name}
      - name: alerting-rules
        configMap:
          name: scheduler-alerting-rules
      - name: alerting-templates
        configMap:
          name: scheduler-alerting-templates
EOF

    # Create dashboard deployment
    create_scheduler_dashboard "$application_name"
}

# Main scheduler monitoring function
main() {
    local command="$1"
    shift
    
    case "$command" in
        "setup")
            setup_scheduler_monitoring "$@"
            ;;
        "monitor")
            start_scheduler_monitoring "$@"
            ;;
        "profile")
            run_scheduler_profiling "$@"
            ;;
        "analyze")
            analyze_scheduler_performance "$@"
            ;;
        "optimize")
            optimize_scheduler_performance "$@"
            ;;
        *)
            echo "Usage: $0 {setup|monitor|profile|analyze|optimize} [options]"
            exit 1
            ;;
    esac
}

# Execute main function
main "$@"

# Go Systems Engineering Career Progression
GO_SYSTEMS_LEVELS = [
    "Junior Go Developer",
    "Go Systems Engineer",
    "Senior Go Runtime Engineer", 
    "Principal Go Performance Architect",
    "Distinguished Go Systems Expert"
]

# Systems Engineering Specialization Areas
SYSTEMS_SPECIALIZATIONS = [
    "High-Performance Computing",
    "Real-Time Systems Engineering",
    "Distributed Systems Architecture", 
    "Cloud-Native Platform Engineering",
    "Concurrent Systems Design"
]

# Industry Focus Areas
INDUSTRY_SYSTEMS_TRACKS = [
    "Financial Technology Trading Systems",
    "Gaming and Real-Time Media Processing",
    "IoT and Embedded Systems",
    "Large-Scale Infrastructure Platforms"
]

# Example: Systems optimization contributions
apiVersion: v1
kind: ConfigMap
metadata:
  name: systems-portfolio-examples
data:
  scheduler-optimization.yaml: |
    # Contributed advanced scheduler optimization framework
    # Features: Adaptive load balancing, NUMA awareness, real-time monitoring
    
  concurrent-algorithms.yaml: |
    # Created high-performance concurrent algorithm library
    # Features: Lock-free data structures, wait-free queues, scalable hash tables
    
  runtime-profiling-tools.yaml: |
    # Developed advanced Go runtime profiling and analysis tools
    # Features: Scheduler visualization, bottleneck detection, optimization recommendations