package loadbalancer

import (
    "context"
    "sync"
    "time"
    "math"
    "sort"
)

type LoadBalancer struct {
    Name                string
    Algorithm           Algorithm
    HealthChecker       *HealthChecker
    BackendPool         *BackendPool
    SessionAffinity     *SessionAffinityManager
    CircuitBreaker      *CircuitBreaker
    MetricsCollector    *MetricsCollector
    RateLimiter         *RateLimiter
    mutex               sync.RWMutex
}

type Backend struct {
    ID                  string
    Address             string
    Port                int
    Weight              int
    Capacity            int
    CurrentConnections  int64
    TotalRequests       int64
    SuccessfulRequests  int64
    FailedRequests      int64
    AvgResponseTime     time.Duration
    HealthStatus        HealthStatus
    Zone                string
    Rack                string
    LastHealthCheck     time.Time
    CPUUtilization      float64
    MemoryUtilization   float64
    ActiveSessions      int64
    Priority            int
    MaintenanceMode     bool
    mutex               sync.RWMutex
}

type Algorithm interface {
    SelectBackend(backends []*Backend, request *Request) (*Backend, error)
    UpdateBackendStats(backend *Backend, response *Response)
    GetAlgorithmName() string
}

// Weighted Least Connections with Response Time Algorithm
type WeightedLeastConnectionsRT struct {
    rtWeightFactor    float64
    connectionWeight  float64
    responseTimeWeight float64
}

func (w *WeightedLeastConnectionsRT) SelectBackend(backends []*Backend, 
                                                  request *Request) (*Backend, error) {
    if len(backends) == 0 {
        return nil, ErrNoHealthyBackends
    }
    
    var bestBackend *Backend
    var bestScore float64 = math.Inf(1)
    
    for _, backend := range backends {
        if backend.HealthStatus != HealthStatusHealthy || backend.MaintenanceMode {
            continue
        }
        
        // Calculate composite score
        score := w.calculateBackendScore(backend)
        
        if score < bestScore {
            bestScore = score
            bestBackend = backend
        }
    }
    
    if bestBackend == nil {
        return nil, ErrNoHealthyBackends
    }
    
    return bestBackend, nil
}

func (w *WeightedLeastConnectionsRT) calculateBackendScore(backend *Backend) float64 {
    backend.mutex.RLock()
    defer backend.mutex.RUnlock()
    
    // Normalize metrics
    connectionScore := float64(backend.CurrentConnections) / float64(backend.Capacity)
    responseTimeScore := float64(backend.AvgResponseTime.Milliseconds()) / 1000.0
    weightFactor := 1.0 / float64(backend.Weight)
    
    // Composite score (lower is better)
    score := (w.connectionWeight * connectionScore +
              w.responseTimeWeight * responseTimeScore) * weightFactor
    
    return score
}

// Power of Two Choices Algorithm
type PowerOfTwoChoices struct {
    hashFunc func(string) uint64
}

func (p *PowerOfTwoChoices) SelectBackend(backends []*Backend, 
                                         request *Request) (*Backend, error) {
    if len(backends) == 0 {
        return nil, ErrNoHealthyBackends
    }
    
    healthyBackends := p.filterHealthyBackends(backends)
    if len(healthyBackends) == 0 {
        return nil, ErrNoHealthyBackends
    }
    
    if len(healthyBackends) == 1 {
        return healthyBackends[0], nil
    }
    
    // Select two random backends
    hash1 := p.hashFunc(request.ID + "1")
    hash2 := p.hashFunc(request.ID + "2")
    
    backend1 := healthyBackends[hash1%uint64(len(healthyBackends))]
    backend2 := healthyBackends[hash2%uint64(len(healthyBackends))]
    
    // Choose the one with fewer connections
    if backend1.CurrentConnections <= backend2.CurrentConnections {
        return backend1, nil
    }
    
    return backend2, nil
}

// Consistent Hashing with Virtual Nodes
type ConsistentHashing struct {
    hashRing        *HashRing
    virtualNodes    int
    replicationFactor int
}

type HashRing struct {
    nodes           map[uint64]*Backend
    sortedHashes    []uint64
    mutex           sync.RWMutex
}

func (c *ConsistentHashing) SelectBackend(backends []*Backend, 
                                         request *Request) (*Backend, error) {
    if len(backends) == 0 {
        return nil, ErrNoHealthyBackends
    }
    
    // Update hash ring if needed
    c.updateHashRing(backends)
    
    // Calculate hash for request
    requestHash := c.calculateRequestHash(request)
    
    // Find the first backend clockwise
    backend := c.hashRing.findBackend(requestHash)
    
    if backend == nil || backend.HealthStatus != HealthStatusHealthy {
        // Fallback to next healthy backend
        return c.findNextHealthyBackend(requestHash, backends)
    }
    
    return backend, nil
}

func (c *ConsistentHashing) updateHashRing(backends []*Backend) {
    c.hashRing.mutex.Lock()
    defer c.hashRing.mutex.Unlock()
    
    // Clear existing ring
    c.hashRing.nodes = make(map[uint64]*Backend)
    c.hashRing.sortedHashes = nil
    
    // Add virtual nodes for each backend
    for _, backend := range backends {
        for i := 0; i < c.virtualNodes; i++ {
            virtualNodeKey := fmt.Sprintf("%s#%d", backend.ID, i)
            hash := c.calculateHash(virtualNodeKey)
            c.hashRing.nodes[hash] = backend
            c.hashRing.sortedHashes = append(c.hashRing.sortedHashes, hash)
        }
    }
    
    // Sort hashes
    sort.Slice(c.hashRing.sortedHashes, func(i, j int) bool {
        return c.hashRing.sortedHashes[i] < c.hashRing.sortedHashes[j]
    })
}

// Adaptive Load Balancing with Machine Learning
type AdaptiveLoadBalancer struct {
    predictor        *PerformancePredictor
    learningRate     float64
    adaptationPeriod time.Duration
    weights          map[string]float64
    metrics          *AdaptiveMetrics
}

func (a *AdaptiveLoadBalancer) SelectBackend(backends []*Backend, 
                                           request *Request) (*Backend, error) {
    if len(backends) == 0 {
        return nil, ErrNoHealthyBackends
    }
    
    // Predict performance for each backend
    predictions := make(map[*Backend]*PerformancePrediction)
    
    for _, backend := range backends {
        if backend.HealthStatus != HealthStatusHealthy {
            continue
        }
        
        prediction := a.predictor.PredictPerformance(backend, request)
        predictions[backend] = prediction
    }
    
    if len(predictions) == 0 {
        return nil, ErrNoHealthyBackends
    }
    
    // Select backend with best predicted performance
    bestBackend := a.selectBestPredictedBackend(predictions)
    
    return bestBackend, nil
}

func (a *AdaptiveLoadBalancer) selectBestPredictedBackend(
    predictions map[*Backend]*PerformancePrediction) *Backend {
    
    var bestBackend *Backend
    var bestScore float64 = -1
    
    for backend, prediction := range predictions {
        // Calculate composite score
        score := a.calculatePredictionScore(prediction)
        
        if score > bestScore {
            bestScore = score
            bestBackend = backend
        }
    }
    
    return bestBackend
}

func (a *AdaptiveLoadBalancer) calculatePredictionScore(
    prediction *PerformancePrediction) float64 {
    
    // Weighted combination of predicted metrics
    score := (a.weights["response_time"] * (1.0 - prediction.ResponseTime) +
              a.weights["success_rate"] * prediction.SuccessRate +
              a.weights["throughput"] * prediction.Throughput +
              a.weights["resource_efficiency"] * prediction.ResourceEfficiency)
    
    return score
}

// Geographic Load Balancing
type GeographicLoadBalancer struct {
    geoIPDB         *GeoIPDatabase
    latencyMatrix   *LatencyMatrix
    affinityRules   []*AffinityRule
}

func (g *GeographicLoadBalancer) SelectBackend(backends []*Backend, 
                                              request *Request) (*Backend, error) {
    if len(backends) == 0 {
        return nil, ErrNoHealthyBackends
    }
    
    // Determine client location
    clientLocation := g.geoIPDB.GetLocation(request.ClientIP)
    
    // Filter backends by affinity rules
    eligibleBackends := g.applyAffinityRules(backends, clientLocation, request)
    
    if len(eligibleBackends) == 0 {
        // Fallback to all healthy backends
        eligibleBackends = g.filterHealthyBackends(backends)
    }
    
    // Select backend with lowest latency
    bestBackend := g.selectLowestLatencyBackend(eligibleBackends, clientLocation)
    
    return bestBackend, nil
}

func (g *GeographicLoadBalancer) selectLowestLatencyBackend(
    backends []*Backend, 
    clientLocation *Location) *Backend {
    
    var bestBackend *Backend
    var lowestLatency time.Duration = time.Hour // Initialize with high value
    
    for _, backend := range backends {
        backendLocation := &Location{
            Country: backend.Zone,
            Region:  backend.Rack,
        }
        
        latency := g.latencyMatrix.GetLatency(clientLocation, backendLocation)
        
        if latency < lowestLatency {
            lowestLatency = latency
            bestBackend = backend
        }
    }
    
    return bestBackend
}

class AdvancedHealthChecker:
    def __init__(self):
        self.health_checks = {}
        self.health_history = {}
        self.anomaly_detector = HealthAnomalyDetector()
        self.adaptive_threshold = AdaptiveThresholdCalculator()
        
    def register_health_check(self, backend_id, health_check_config):
        """Register comprehensive health check for backend"""
        health_check = MultiLevelHealthCheck(
            backend_id=backend_id,
            config=health_check_config,
            levels=[
                L4HealthCheck(health_check_config.l4_config),
                L7HealthCheck(health_check_config.l7_config),
                ApplicationHealthCheck(health_check_config.app_config),
                DeepHealthCheck(health_check_config.deep_config)
            ]
        )
        
        self.health_checks[backend_id] = health_check
        self.health_history[backend_id] = HealthHistory()
        
        # Start health check monitoring
        self.start_health_monitoring(health_check)
    
    def start_health_monitoring(self, health_check):
        """Start continuous health monitoring"""
        def health_monitor_loop():
            while health_check.enabled:
                try:
                    # Execute all health check levels
                    results = self.execute_health_checks(health_check)
                    
                    # Analyze results
                    overall_health = self.analyze_health_results(results)
                    
                    # Update health status
                    previous_status = health_check.backend.health_status
                    health_check.backend.health_status = overall_health.status
                    
                    # Store health history
                    self.health_history[health_check.backend_id].add_result(
                        overall_health
                    )
                    
                    # Detect anomalies
                    anomalies = self.anomaly_detector.detect_anomalies(
                        health_check.backend_id,
                        overall_health,
                        self.health_history[health_check.backend_id]
                    )
                    
                    # Handle status changes
                    if previous_status != overall_health.status:
                        self.handle_health_status_change(
                            health_check.backend,
                            previous_status,
                            overall_health.status,
                            results
                        )
                    
                    # Adaptive threshold adjustment
                    self.adaptive_threshold.update_thresholds(
                        health_check.backend_id,
                        results
                    )
                    
                    time.sleep(health_check.config.check_interval)
                    
                except Exception as e:
                    logger.error(f"Health check failed for {health_check.backend_id}: {e}")
                    time.sleep(health_check.config.error_retry_interval)
        
        threading.Thread(target=health_monitor_loop, daemon=True).start()
    
    def execute_health_checks(self, health_check):
        """Execute all levels of health checks"""
        results = {}
        
        for level in health_check.levels:
            try:
                start_time = time.time()
                result = level.execute(health_check.backend)
                end_time = time.time()
                
                result.execution_time = end_time - start_time
                results[level.name] = result
                
                # Short-circuit on critical failures
                if result.status == HealthStatus.CRITICAL and level.critical:
                    break
                    
            except Exception as e:
                results[level.name] = HealthCheckResult(
                    status=HealthStatus.UNKNOWN,
                    message=f"Health check execution failed: {e}",
                    execution_time=0,
                    error=e
                )
        
        return results
    
    def analyze_health_results(self, results):
        """Analyze health check results to determine overall health"""
        if not results:
            return OverallHealthResult(
                status=HealthStatus.UNKNOWN,
                confidence=0.0,
                details=results
            )
        
        # Weight different health check levels
        weights = {
            'l4_check': 0.15,
            'l7_check': 0.25,
            'application_check': 0.35,
            'deep_check': 0.25
        }
        
        weighted_score = 0.0
        total_weight = 0.0
        
        for check_name, result in results.items():
            if check_name in weights:
                score = self.convert_status_to_score(result.status)
                weighted_score += weights[check_name] * score
                total_weight += weights[check_name]
        
        if total_weight > 0:
            overall_score = weighted_score / total_weight
            overall_status = self.convert_score_to_status(overall_score)
            confidence = self.calculate_confidence(results)
        else:
            overall_status = HealthStatus.UNKNOWN
            confidence = 0.0
        
        return OverallHealthResult(
            status=overall_status,
            confidence=confidence,
            details=results,
            score=overall_score if total_weight > 0 else 0.0
        )

class CircuitBreakerManager:
    def __init__(self):
        self.circuit_breakers = {}
        self.global_circuit_breaker = GlobalCircuitBreaker()
        
    def get_circuit_breaker(self, backend_id):
        """Get or create circuit breaker for backend"""
        if backend_id not in self.circuit_breakers:
            config = CircuitBreakerConfig(
                failure_threshold=5,
                success_threshold=3,
                timeout=30,
                max_concurrent_requests=100,
                slow_call_threshold=5000,  # 5 seconds
                slow_call_rate_threshold=0.5,
                minimum_number_of_calls=10,
                sliding_window_size=100,
                sliding_window_type=SlidingWindowType.COUNT_BASED
            )
            
            self.circuit_breakers[backend_id] = AdvancedCircuitBreaker(
                backend_id, config
            )
        
        return self.circuit_breakers[backend_id]
    
    def record_result(self, backend_id, request_result):
        """Record request result for circuit breaker"""
        circuit_breaker = self.get_circuit_breaker(backend_id)
        circuit_breaker.record_result(request_result)
        
        # Also record for global circuit breaker
        self.global_circuit_breaker.record_result(backend_id, request_result)

class AdvancedCircuitBreaker:
    def __init__(self, backend_id, config):
        self.backend_id = backend_id
        self.config = config
        self.state = CircuitBreakerState.CLOSED
        self.failure_count = 0
        self.success_count = 0
        self.last_failure_time = None
        self.half_open_start_time = None
        self.request_count = 0
        self.slow_call_count = 0
        self.metrics_window = SlidingWindow(config.sliding_window_size)
        self.concurrent_requests = 0
        self.mutex = threading.Lock()
    
    def call_permitted(self):
        """Check if call is permitted through circuit breaker"""
        with self.mutex:
            if self.state == CircuitBreakerState.CLOSED:
                return self.concurrent_requests < self.config.max_concurrent_requests
            
            elif self.state == CircuitBreakerState.OPEN:
                if time.time() - self.last_failure_time >= self.config.timeout:
                    self.transition_to_half_open()
                    return True
                return False
            
            elif self.state == CircuitBreakerState.HALF_OPEN:
                return self.concurrent_requests < self.config.max_concurrent_requests
            
            return False
    
    def record_result(self, result):
        """Record request result and update circuit breaker state"""
        with self.mutex:
            self.metrics_window.add_result(result)
            self.request_count += 1
            
            if result.success:
                self.record_success(result)
            else:
                self.record_failure(result)
            
            # Check if slow call
            if result.duration > self.config.slow_call_threshold:
                self.slow_call_count += 1
            
            # Evaluate state transition
            self.evaluate_state_transition()
    
    def record_success(self, result):
        """Record successful request"""
        if self.state == CircuitBreakerState.HALF_OPEN:
            self.success_count += 1
            if self.success_count >= self.config.success_threshold:
                self.transition_to_closed()
    
    def record_failure(self, result):
        """Record failed request"""
        self.failure_count += 1
        self.last_failure_time = time.time()
        
        if self.state == CircuitBreakerState.HALF_OPEN:
            self.transition_to_open()
    
    def evaluate_state_transition(self):
        """Evaluate whether circuit breaker should change state"""
        if self.state == CircuitBreakerState.CLOSED:
            if self.should_open_circuit():
                self.transition_to_open()
    
    def should_open_circuit(self):
        """Determine if circuit should open based on metrics"""
        if self.request_count < self.config.minimum_number_of_calls:
            return False
        
        # Check failure rate
        failure_rate = self.metrics_window.get_failure_rate()
        if failure_rate >= self.config.failure_rate_threshold:
            return True
        
        # Check slow call rate
        slow_call_rate = self.slow_call_count / self.request_count
        if slow_call_rate >= self.config.slow_call_rate_threshold:
            return True
        
        return False
    
    def transition_to_open(self):
        """Transition circuit breaker to OPEN state"""
        self.state = CircuitBreakerState.OPEN
        self.last_failure_time = time.time()
        logger.warning(f"Circuit breaker OPENED for backend {self.backend_id}")
    
    def transition_to_half_open(self):
        """Transition circuit breaker to HALF_OPEN state"""
        self.state = CircuitBreakerState.HALF_OPEN
        self.success_count = 0
        self.half_open_start_time = time.time()
        logger.info(f"Circuit breaker HALF-OPEN for backend {self.backend_id}")
    
    def transition_to_closed(self):
        """Transition circuit breaker to CLOSED state"""
        self.state = CircuitBreakerState.CLOSED
        self.failure_count = 0
        self.success_count = 0
        self.request_count = 0
        self.slow_call_count = 0
        self.metrics_window.reset()
        logger.info(f"Circuit breaker CLOSED for backend {self.backend_id}")

package affinity

import (
    "crypto/sha256"
    "encoding/hex"
    "sync"
    "time"
)

type SessionAffinityManager struct {
    strategy        AffinityStrategy
    sessionStore    SessionStore
    cookieManager   *CookieManager
    stickyTable     *StickyTable
    rebalancer      *AffinityRebalancer
    mutex           sync.RWMutex
}

type AffinityStrategy interface {
    DetermineBackend(request *Request, availableBackends []*Backend) (*Backend, error)
    HandleBackendFailure(sessionID string, failedBackend *Backend, 
                        availableBackends []*Backend) (*Backend, error)
}

// Consistent Hash-based Session Affinity
type ConsistentHashAffinity struct {
    hashRing        *ConsistentHashRing
    virtualNodes    int
    sessionTimeout  time.Duration
    failoverEnabled bool
}

func (c *ConsistentHashAffinity) DetermineBackend(request *Request, 
                                                 availableBackends []*Backend) (*Backend, error) {
    // Extract or generate session ID
    sessionID := c.extractSessionID(request)
    if sessionID == "" {
        sessionID = c.generateSessionID(request)
    }
    
    // Update hash ring with current backends
    c.updateHashRing(availableBackends)
    
    // Find backend using consistent hashing
    backend := c.hashRing.GetBackend(sessionID)
    
    if backend == nil || !c.isBackendHealthy(backend) {
        if c.failoverEnabled {
            return c.handleFailover(sessionID, backend, availableBackends)
        }
        return nil, ErrNoHealthyBackend
    }
    
    return backend, nil
}

func (c *ConsistentHashAffinity) updateHashRing(backends []*Backend) {
    c.hashRing.UpdateBackends(backends, c.virtualNodes)
}

func (c *ConsistentHashAffinity) handleFailover(sessionID string, 
                                               failedBackend *Backend,
                                               availableBackends []*Backend) (*Backend, error) {
    // Remove failed backend from hash ring temporarily
    c.hashRing.RemoveBackend(failedBackend)
    
    // Find next backend in the ring
    nextBackend := c.hashRing.GetBackend(sessionID)
    
    if nextBackend != nil && c.isBackendHealthy(nextBackend) {
        return nextBackend, nil
    }
    
    // Fallback to any healthy backend
    for _, backend := range availableBackends {
        if c.isBackendHealthy(backend) {
            return backend, nil
        }
    }
    
    return nil, ErrNoHealthyBackend
}

// Cookie-based Session Affinity
type CookieAffinity struct {
    cookieName      string
    cookiePath      string
    cookieDomain    string
    cookieSecure    bool
    cookieHttpOnly  bool
    cookieSameSite  SameSiteMode
    encryptionKey   []byte
    signatureKey    []byte
}

func (c *CookieAffinity) DetermineBackend(request *Request, 
                                         availableBackends []*Backend) (*Backend, error) {
    // Check for existing affinity cookie
    cookie := request.GetCookie(c.cookieName)
    if cookie != nil {
        backendID := c.decryptCookie(cookie.Value)
        if backendID != "" {
            backend := c.findBackendByID(backendID, availableBackends)
            if backend != nil && c.isBackendHealthy(backend) {
                return backend, nil
            }
        }
    }
    
    // No valid cookie found, select new backend
    backend := c.selectBackendForNewSession(availableBackends)
    if backend == nil {
        return nil, ErrNoHealthyBackend
    }
    
    // Create affinity cookie
    c.createAffinityCookie(request.Response, backend.ID)
    
    return backend, nil
}

func (c *CookieAffinity) createAffinityCookie(response *Response, backendID string) {
    encryptedValue := c.encryptCookie(backendID)
    
    cookie := &Cookie{
        Name:     c.cookieName,
        Value:    encryptedValue,
        Path:     c.cookiePath,
        Domain:   c.cookieDomain,
        Secure:   c.cookieSecure,
        HttpOnly: c.cookieHttpOnly,
        SameSite: c.cookieSameSite,
        MaxAge:   int(sessionTimeout.Seconds()),
    }
    
    response.SetCookie(cookie)
}

// IP Hash-based Session Affinity
type IPHashAffinity struct {
    hashFunction    HashFunction
    stickyDuration  time.Duration
    subnetMask      string
}

func (i *IPHashAffinity) DetermineBackend(request *Request, 
                                         availableBackends []*Backend) (*Backend, error) {
    // Extract client IP (considering proxy headers)
    clientIP := i.extractClientIP(request)
    
    // Apply subnet mask if configured
    if i.subnetMask != "" {
        clientIP = i.applySubnetMask(clientIP, i.subnetMask)
    }
    
    // Calculate hash
    hash := i.hashFunction(clientIP)
    
    // Select backend based on hash
    backendIndex := hash % uint64(len(availableBackends))
    backend := availableBackends[backendIndex]
    
    if !i.isBackendHealthy(backend) {
        // Use next healthy backend
        return i.findNextHealthyBackend(availableBackends, int(backendIndex))
    }
    
    return backend, nil
}

// Header-based Session Affinity
type HeaderAffinity struct {
    headerName      string
    headerTransform HeaderTransformFunc
    fallbackStrategy AffinityStrategy
}

func (h *HeaderAffinity) DetermineBackend(request *Request, 
                                         availableBackends []*Backend) (*Backend, error) {
    headerValue := request.GetHeader(h.headerName)
    if headerValue == "" {
        if h.fallbackStrategy != nil {
            return h.fallbackStrategy.DetermineBackend(request, availableBackends)
        }
        return nil, ErrNoAffinityHeader
    }
    
    // Transform header value if needed
    if h.headerTransform != nil {
        headerValue = h.headerTransform(headerValue)
    }
    
    // Calculate hash based on header value
    hash := calculateStringHash(headerValue)
    backendIndex := hash % uint64(len(availableBackends))
    
    return availableBackends[backendIndex], nil
}

// Advanced Session Store
type DistributedSessionStore struct {
    redisCluster    *RedisCluster
    consistentHash  *ConsistentHashRing
    sessionTimeout  time.Duration
    compressionType CompressionType
    encryptionKey   []byte
}

func (d *DistributedSessionStore) StoreSession(sessionID string, 
                                              session *Session) error {
    // Serialize session data
    data, err := d.serializeSession(session)
    if err != nil {
        return err
    }
    
    // Compress if configured
    if d.compressionType != CompressionNone {
        data, err = d.compressData(data)
        if err != nil {
            return err
        }
    }
    
    // Encrypt if configured
    if d.encryptionKey != nil {
        data, err = d.encryptData(data)
        if err != nil {
            return err
        }
    }
    
    // Determine Redis node using consistent hashing
    node := d.consistentHash.GetNode(sessionID)
    
    // Store in Redis with timeout
    return node.SetEx(sessionID, data, d.sessionTimeout)
}

func (d *DistributedSessionStore) GetSession(sessionID string) (*Session, error) {
    // Determine Redis node
    node := d.consistentHash.GetNode(sessionID)
    
    // Retrieve from Redis
    data, err := node.Get(sessionID)
    if err != nil {
        return nil, err
    }
    
    // Decrypt if configured
    if d.encryptionKey != nil {
        data, err = d.decryptData(data)
        if err != nil {
            return nil, err
        }
    }
    
    // Decompress if configured
    if d.compressionType != CompressionNone {
        data, err = d.decompressData(data)
        if err != nil {
            return nil, err
        }
    }
    
    // Deserialize session data
    return d.deserializeSession(data)
}

// Session Affinity Rebalancer
type AffinityRebalancer struct {
    rebalanceInterval time.Duration
    loadThreshold     float64
    sessionMigrator   *SessionMigrator
}

func (a *AffinityRebalancer) StartRebalancing(sessionStore SessionStore, 
                                             loadBalancer *LoadBalancer) {
    ticker := time.NewTicker(a.rebalanceInterval)
    defer ticker.Stop()
    
    for range ticker.C {
        a.rebalanceIfNeeded(sessionStore, loadBalancer)
    }
}

func (a *AffinityRebalancer) rebalanceIfNeeded(sessionStore SessionStore, 
                                              loadBalancer *LoadBalancer) {
    // Check backend load distribution
    backends := loadBalancer.GetBackends()
    loadStats := a.calculateLoadStats(backends)
    
    if a.shouldRebalance(loadStats) {
        // Identify sessions to migrate
        migrations := a.planSessionMigrations(backends, loadStats)
        
        // Execute migrations
        for _, migration := range migrations {
            err := a.sessionMigrator.MigrateSession(
                migration.SessionID,
                migration.SourceBackend,
                migration.TargetBackend,
                sessionStore
            )
            if err != nil {
                logger.Errorf("Failed to migrate session %s: %v", 
                             migration.SessionID, err)
            }
        }
    }
}

class MultiTierLoadBalancer:
    def __init__(self):
        self.global_load_balancer = GlobalLoadBalancer()
        self.regional_load_balancers = {}
        self.local_load_balancers = {}
        self.service_mesh_lb = ServiceMeshLoadBalancer()
        
    def route_request(self, request):
        """Route request through multi-tier load balancing"""
        # Tier 1: Global Load Balancing (DNS/GeoDNS)
        region = self.global_load_balancer.select_region(request)
        
        # Tier 2: Regional Load Balancing
        regional_lb = self.regional_load_balancers[region]
        cluster = regional_lb.select_cluster(request)
        
        # Tier 3: Local Load Balancing
        local_lb = self.local_load_balancers[cluster]
        backend = local_lb.select_backend(request)
        
        # Tier 4: Service Mesh Load Balancing (for microservices)
        if request.service_mesh_enabled:
            final_endpoint = self.service_mesh_lb.select_endpoint(
                request, backend
            )
            return final_endpoint
        
        return backend
    
    def implement_traffic_splitting(self, request, traffic_split_config):
        """Implement advanced traffic splitting patterns"""
        # Canary deployment
        if traffic_split_config.canary_enabled:
            if self.should_route_to_canary(request, traffic_split_config):
                return self.route_to_canary_backend(request)
        
        # Blue-green deployment
        if traffic_split_config.blue_green_enabled:
            active_environment = traffic_split_config.active_environment
            return self.route_to_environment(request, active_environment)
        
        # A/B testing
        if traffic_split_config.ab_testing_enabled:
            test_group = self.determine_test_group(request, traffic_split_config)
            return self.route_to_test_group(request, test_group)
        
        # Default routing
        return self.route_request(request)

class ServiceMeshLoadBalancer:
    def __init__(self):
        self.service_registry = ServiceRegistry()
        self.circuit_breakers = {}
        self.retry_policies = {}
        self.timeout_policies = {}
        self.observability = ObservabilityManager()
        
    def select_endpoint(self, request, service_backend):
        """Select endpoint within service mesh"""
        # Get service instances
        service_instances = self.service_registry.get_instances(
            service_backend.service_name
        )
        
        # Apply service mesh policies
        eligible_instances = self.apply_service_mesh_policies(
            service_instances, request
        )
        
        # Load balance among eligible instances
        selected_instance = self.load_balance_instances(
            eligible_instances, request
        )
        
        # Record selection for observability
        self.observability.record_endpoint_selection(
            request, service_backend, selected_instance
        )
        
        return selected_instance
    
    def apply_service_mesh_policies(self, instances, request):
        """Apply service mesh policies to filter instances"""
        eligible_instances = instances
        
        # Circuit breaker filtering
        eligible_instances = [
            instance for instance in eligible_instances
            if not self.is_circuit_breaker_open(instance)
        ]
        
        # Retry policy filtering
        if request.retry_count > 0:
            retry_policy = self.get_retry_policy(request.service_name)
            if retry_policy:
                eligible_instances = retry_policy.filter_instances(
                    eligible_instances, request
                )
        
        # Timeout policy filtering
        timeout_policy = self.get_timeout_policy(request.service_name)
        if timeout_policy:
            eligible_instances = timeout_policy.filter_instances(
                eligible_instances, request
            )
        
        return eligible_instances

class AdaptiveLoadBalancer:
    def __init__(self):
        self.performance_monitor = PerformanceMonitor()
        self.ml_predictor = MLPredictor()
        self.auto_scaler = AutoScaler()
        self.cost_optimizer = CostOptimizer()
        
    def adaptive_backend_selection(self, request, backends):
        """Adaptively select backend based on current conditions"""
        # Collect real-time metrics
        current_metrics = self.performance_monitor.get_current_metrics(backends)
        
        # Predict performance
        performance_predictions = {}
        for backend in backends:
            prediction = self.ml_predictor.predict_performance(
                backend, request, current_metrics
            )
            performance_predictions[backend] = prediction
        
        # Consider cost implications
        cost_analysis = self.cost_optimizer.analyze_costs(
            backends, request, performance_predictions
        )
        
        # Make selection based on multiple criteria
        selection_score = {}
        for backend in backends:
            score = self.calculate_selection_score(
                backend,
                performance_predictions[backend],
                cost_analysis[backend],
                current_metrics[backend]
            )
            selection_score[backend] = score
        
        # Select best backend
        best_backend = max(selection_score, key=selection_score.get)
        
        # Trigger auto-scaling if needed
        if self.should_trigger_autoscaling(current_metrics, request):
            self.auto_scaler.trigger_scaling(backends, request)
        
        return best_backend
    
    def calculate_selection_score(self, backend, prediction, cost_analysis, metrics):
        """Calculate composite selection score"""
        weights = {
            'performance': 0.4,
            'cost': 0.3,
            'reliability': 0.2,
            'resource_efficiency': 0.1
        }
        
        performance_score = self.normalize_performance_score(prediction)
        cost_score = self.normalize_cost_score(cost_analysis)
        reliability_score = self.normalize_reliability_score(metrics)
        efficiency_score = self.normalize_efficiency_score(metrics)
        
        composite_score = (
            weights['performance'] * performance_score +
            weights['cost'] * cost_score +
            weights['reliability'] * reliability_score +
            weights['resource_efficiency'] * efficiency_score
        )
        
        return composite_score

class LoadBalancerOrchestrator:
    def __init__(self):
        self.load_balancers = {}
        self.routing_policies = {}
        self.traffic_policies = {}
        self.monitoring = OrchestrationMonitoring()
        
    def orchestrate_traffic_flow(self, request):
        """Orchestrate traffic flow across multiple load balancers"""
        # Determine routing policy
        routing_policy = self.get_routing_policy(request)
        
        # Apply traffic policies
        traffic_policy = self.get_traffic_policy(request)
        modified_request = traffic_policy.apply(request)
        
        # Execute multi-stage load balancing
        routing_path = routing_policy.calculate_routing_path(modified_request)
        
        final_backend = None
        for stage in routing_path.stages:
            load_balancer = self.load_balancers[stage.load_balancer_id]
            backend = load_balancer.select_backend(modified_request)
            
            if stage.is_final:
                final_backend = backend
            else:
                # Intermediate stage processing
                modified_request = stage.process_intermediate(
                    modified_request, backend
                )
        
        # Record orchestration metrics
        self.monitoring.record_orchestration(
            request, routing_path, final_backend
        )
        
        return final_backend

#include <sys/epoll.h>
#include <sys/socket.h>
#include <netinet/in.h>
#include <arpa/inet.h>
#include <unistd.h>
#include <fcntl.h>
#include <string.h>
#include <stdlib.h>
#include <stdio.h>

#define MAX_EVENTS 1000
#define BUFFER_SIZE 8192
#define MAX_BACKENDS 100

typedef struct {
    int fd;
    struct sockaddr_in addr;
    int weight;
    int current_connections;
    int total_requests;
    int failed_requests;
    long long avg_response_time;
    int health_status;
    time_t last_health_check;
} backend_t;

typedef struct {
    int epoll_fd;
    int listen_fd;
    backend_t backends[MAX_BACKENDS];
    int backend_count;
    int current_backend;
    pthread_mutex_t backend_mutex;
    struct epoll_event events[MAX_EVENTS];
} load_balancer_t;

typedef struct {
    int client_fd;
    int backend_fd;
    backend_t *backend;
    char client_buffer[BUFFER_SIZE];
    char backend_buffer[BUFFER_SIZE];
    int client_buffer_len;
    int backend_buffer_len;
    struct timespec start_time;
} connection_t;

// High-performance load balancer main loop
int load_balancer_main_loop(load_balancer_t *lb) {
    int nfds, i;
    connection_t *conn;
    
    while (1) {
        nfds = epoll_wait(lb->epoll_fd, lb->events, MAX_EVENTS, -1);
        if (nfds == -1) {
            perror("epoll_wait");
            continue;
        }
        
        for (i = 0; i < nfds; i++) {
            struct epoll_event *event = &lb->events[i];
            
            if (event->data.fd == lb->listen_fd) {
                // New client connection
                handle_new_connection(lb);
            } else {
                conn = (connection_t *)event->data.ptr;
                
                if (event->events & EPOLLIN) {
                    if (event->data.fd == conn->client_fd) {
                        handle_client_data(lb, conn);
                    } else if (event->data.fd == conn->backend_fd) {
                        handle_backend_data(lb, conn);
                    }
                } else if (event->events & EPOLLOUT) {
                    if (event->data.fd == conn->client_fd) {
                        handle_client_write(lb, conn);
                    } else if (event->data.fd == conn->backend_fd) {
                        handle_backend_write(lb, conn);
                    }
                } else if (event->events & (EPOLLHUP | EPOLLERR)) {
                    handle_connection_error(lb, conn);
                }
            }
        }
    }
    
    return 0;
}

// Handle new client connection
static void handle_new_connection(load_balancer_t *lb) {
    struct sockaddr_in client_addr;
    socklen_t client_len = sizeof(client_addr);
    int client_fd, backend_fd;
    backend_t *backend;
    connection_t *conn;
    
    // Accept client connection
    client_fd = accept(lb->listen_fd, (struct sockaddr *)&client_addr, &client_len);
    if (client_fd == -1) {
        perror("accept");
        return;
    }
    
    // Set non-blocking
    set_nonblocking(client_fd);
    
    // Select backend using load balancing algorithm
    backend = select_backend(lb);
    if (!backend) {
        close(client_fd);
        return;
    }
    
    // Connect to backend
    backend_fd = create_backend_connection(backend);
    if (backend_fd == -1) {
        close(client_fd);
        backend->failed_requests++;
        return;
    }
    
    // Create connection structure
    conn = malloc(sizeof(connection_t));
    memset(conn, 0, sizeof(connection_t));
    conn->client_fd = client_fd;
    conn->backend_fd = backend_fd;
    conn->backend = backend;
    clock_gettime(CLOCK_MONOTONIC, &conn->start_time);
    
    // Add to epoll
    add_connection_to_epoll(lb, conn);
    
    // Update backend stats
    pthread_mutex_lock(&lb->backend_mutex);
    backend->current_connections++;
    backend->total_requests++;
    pthread_mutex_unlock(&lb->backend_mutex);
}

// Backend selection using weighted round-robin
static backend_t *select_backend(load_balancer_t *lb) {
    backend_t *selected = NULL;
    int total_weight = 0;
    int i;
    
    pthread_mutex_lock(&lb->backend_mutex);
    
    // Calculate total weight of healthy backends
    for (i = 0; i < lb->backend_count; i++) {
        if (lb->backends[i].health_status == 1) {
            total_weight += lb->backends[i].weight;
        }
    }
    
    if (total_weight == 0) {
        pthread_mutex_unlock(&lb->backend_mutex);
        return NULL;
    }
    
    // Weighted round-robin selection
    static int current_weight = 0;
    int best_weight = -1;
    
    for (i = 0; i < lb->backend_count; i++) {
        backend_t *backend = &lb->backends[i];
        
        if (backend->health_status != 1) continue;
        
        backend->current_weight += backend->weight;
        
        if (backend->current_weight > best_weight) {
            best_weight = backend->current_weight;
            selected = backend;
        }
    }
    
    if (selected) {
        selected->current_weight -= total_weight;
    }
    
    pthread_mutex_unlock(&lb->backend_mutex);
    return selected;
}

// High-performance data forwarding
static void handle_client_data(load_balancer_t *lb, connection_t *conn) {
    ssize_t bytes_read, bytes_written;
    
    bytes_read = read(conn->client_fd, conn->client_buffer, BUFFER_SIZE);
    if (bytes_read <= 0) {
        if (bytes_read == 0 || errno != EAGAIN) {
            close_connection(lb, conn);
        }
        return;
    }
    
    conn->client_buffer_len = bytes_read;
    
    // Forward to backend
    bytes_written = write(conn->backend_fd, conn->client_buffer, bytes_read);
    if (bytes_written != bytes_read) {
        if (bytes_written == -1 && errno == EAGAIN) {
            // Backend not ready, enable EPOLLOUT
            modify_epoll_events(lb, conn->backend_fd, EPOLLIN | EPOLLOUT);
        } else {
            close_connection(lb, conn);
        }
    }
}

static void handle_backend_data(load_balancer_t *lb, connection_t *conn) {
    ssize_t bytes_read, bytes_written;
    
    bytes_read = read(conn->backend_fd, conn->backend_buffer, BUFFER_SIZE);
    if (bytes_read <= 0) {
        if (bytes_read == 0 || errno != EAGAIN) {
            close_connection(lb, conn);
        }
        return;
    }
    
    conn->backend_buffer_len = bytes_read;
    
    // Forward to client
    bytes_written = write(conn->client_fd, conn->backend_buffer, bytes_read);
    if (bytes_written != bytes_read) {
        if (bytes_written == -1 && errno == EAGAIN) {
            // Client not ready, enable EPOLLOUT
            modify_epoll_events(lb, conn->client_fd, EPOLLIN | EPOLLOUT);
        } else {
            close_connection(lb, conn);
        }
    }
}

// Connection cleanup and statistics update
static void close_connection(load_balancer_t *lb, connection_t *conn) {
    struct timespec end_time;
    long long response_time;
    
    // Calculate response time
    clock_gettime(CLOCK_MONOTONIC, &end_time);
    response_time = (end_time.tv_sec - conn->start_time.tv_sec) * 1000 +
                   (end_time.tv_nsec - conn->start_time.tv_nsec) / 1000000;
    
    // Update backend statistics
    pthread_mutex_lock(&lb->backend_mutex);
    conn->backend->current_connections--;
    
    // Update average response time (exponential moving average)
    if (conn->backend->avg_response_time == 0) {
        conn->backend->avg_response_time = response_time;
    } else {
        conn->backend->avg_response_time = 
            (conn->backend->avg_response_time * 9 + response_time) / 10;
    }
    pthread_mutex_unlock(&lb->backend_mutex);
    
    // Remove from epoll and close sockets
    epoll_ctl(lb->epoll_fd, EPOLL_CTL_DEL, conn->client_fd, NULL);
    epoll_ctl(lb->epoll_fd, EPOLL_CTL_DEL, conn->backend_fd, NULL);
    close(conn->client_fd);
    close(conn->backend_fd);
    free(conn);
}

class LoadBalancerMonitoring:
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.alerting_engine = AlertingEngine()
        self.dashboard = MonitoringDashboard()
        self.analytics_engine = AnalyticsEngine()
        
    def setup_monitoring(self, load_balancer):
        """Setup comprehensive monitoring for load balancer"""
        # Core metrics
        self.setup_core_metrics(load_balancer)
        
        # Performance metrics
        self.setup_performance_metrics(load_balancer)
        
        # Health metrics
        self.setup_health_metrics(load_balancer)
        
        # Business metrics
        self.setup_business_metrics(load_balancer)
        
        # Alerting rules
        self.setup_alerting_rules(load_balancer)
        
        # Real-time dashboard
        self.setup_dashboard(load_balancer)
    
    def setup_core_metrics(self, load_balancer):
        """Setup core load balancer metrics"""
        metrics = {
            'requests_per_second': Counter(
                'lb_requests_total',
                'Total number of requests processed',
                ['backend', 'status']
            ),
            'response_time': Histogram(
                'lb_response_time_seconds',
                'Response time histogram',
                ['backend'],
                buckets=[0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
            ),
            'active_connections': Gauge(
                'lb_active_connections',
                'Number of active connections',
                ['backend']
            ),
            'backend_health': Gauge(
                'lb_backend_health',
                'Backend health status (1=healthy, 0=unhealthy)',
                ['backend']
            ),
            'error_rate': Gauge(
                'lb_error_rate',
                'Error rate percentage',
                ['backend']
            )
        }
        
        self.metrics_collector.register_metrics(metrics)
    
    def collect_real_time_metrics(self, load_balancer):
        """Collect real-time metrics from load balancer"""
        while True:
            try:
                # Collect backend metrics
                for backend in load_balancer.backends:
                    self.collect_backend_metrics(backend)
                
                # Collect load balancer metrics
                self.collect_lb_metrics(load_balancer)
                
                # Analyze patterns
                patterns = self.analytics_engine.analyze_patterns(
                    load_balancer
                )
                
                # Check for anomalies
                anomalies = self.analytics_engine.detect_anomalies(
                    load_balancer
                )
                
                # Generate alerts if needed
                for anomaly in anomalies:
                    self.alerting_engine.generate_alert(anomaly)
                
                time.sleep(5)  # Collect every 5 seconds
                
            except Exception as e:
                logger.error(f"Metrics collection failed: {e}")
                time.sleep(30)
    
    def collect_backend_metrics(self, backend):
        """Collect metrics for individual backend"""
        # Request metrics
        self.metrics_collector.record_metric(
            'requests_per_second',
            backend.requests_per_second,
            labels={'backend': backend.id}
        )
        
        # Response time metrics
        self.metrics_collector.record_metric(
            'response_time',
            backend.avg_response_time,
            labels={'backend': backend.id}
        )
        
        # Connection metrics
        self.metrics_collector.record_metric(
            'active_connections',
            backend.current_connections,
            labels={'backend': backend.id}
        )
        
        # Health metrics
        health_value = 1 if backend.health_status == HealthStatus.HEALTHY else 0
        self.metrics_collector.record_metric(
            'backend_health',
            health_value,
            labels={'backend': backend.id}
        )
        
        # Error rate
        error_rate = (backend.failed_requests / backend.total_requests * 100 
                     if backend.total_requests > 0 else 0)
        self.metrics_collector.record_metric(
            'error_rate',
            error_rate,
            labels={'backend': backend.id}
        )
    
    def setup_alerting_rules(self, load_balancer):
        """Setup alerting rules for load balancer"""
        rules = [
            AlertRule(
                name='HighErrorRate',
                condition='lb_error_rate > 5',
                duration='2m',
                severity='warning',
                description='High error rate detected'
            ),
            AlertRule(
                name='BackendDown',
                condition='lb_backend_health == 0',
                duration='30s',
                severity='critical',
                description='Backend is down'
            ),
            AlertRule(
                name='HighResponseTime',
                condition='lb_response_time_seconds > 2',
                duration='5m',
                severity='warning',
                description='High response time detected'
            ),
            AlertRule(
                name='NoHealthyBackends',
                condition='sum(lb_backend_health) == 0',
                duration='10s',
                severity='critical',
                description='No healthy backends available'
            )
        ]
        
        for rule in rules:
            self.alerting_engine.add_rule(rule)

class LoadBalancerAnalytics:
    def __init__(self):
        self.pattern_analyzer = PatternAnalyzer()
        self.trend_analyzer = TrendAnalyzer()
        self.capacity_planner = CapacityPlanner()
        
    def analyze_traffic_patterns(self, load_balancer, time_range):
        """Analyze traffic patterns and provide insights"""
        traffic_data = self.collect_traffic_data(load_balancer, time_range)
        
        patterns = {
            'peak_hours': self.pattern_analyzer.identify_peak_hours(traffic_data),
            'traffic_trends': self.trend_analyzer.analyze_trends(traffic_data),
            'seasonal_patterns': self.pattern_analyzer.identify_seasonal_patterns(traffic_data),
            'geographic_distribution': self.analyze_geographic_distribution(traffic_data),
            'protocol_distribution': self.analyze_protocol_distribution(traffic_data)
        }
        
        # Generate insights
        insights = self.generate_insights(patterns)
        
        # Capacity planning recommendations
        capacity_recommendations = self.capacity_planner.generate_recommendations(
            patterns, load_balancer
        )
        
        return AnalyticsReport(
            patterns=patterns,
            insights=insights,
            recommendations=capacity_recommendations
        )
    
    def generate_optimization_recommendations(self, load_balancer):
        """Generate optimization recommendations"""
        current_performance = self.analyze_current_performance(load_balancer)
        
        recommendations = []
        
        # Algorithm optimization
        if current_performance.load_distribution_variance > 0.3:
            recommendations.append(
                OptimizationRecommendation(
                    type='algorithm',
                    description='Consider using weighted least connections algorithm',
                    impact='medium',
                    implementation_effort='low'
                )
            )
        
        # Health check optimization
        if current_performance.false_positive_rate > 0.05:
            recommendations.append(
                OptimizationRecommendation(
                    type='health_check',
                    description='Adjust health check sensitivity',
                    impact='high',
                    implementation_effort='low'
                )
            )
        
        # Scaling recommendations
        if current_performance.cpu_utilization > 0.8:
            recommendations.append(
                OptimizationRecommendation(
                    type='scaling',
                    description='Scale up load balancer capacity',
                    impact='high',
                    implementation_effort='medium'
                )
            )
        
        return recommendations