package cdn

import (
    "context"
    "sync"
    "time"
    "net/http"
    "crypto/sha256"
)

type EdgeNode struct {
    ID                string
    Location          *GeoLocation
    Capacity          *NodeCapacity
    CacheManager      *CacheManager
    OriginConnector   *OriginConnector
    LoadBalancer      *EdgeLoadBalancer
    SecurityEngine    *SecurityEngine
    AnalyticsEngine   *AnalyticsEngine
    HealthMonitor     *HealthMonitor
    PerfOptimizer     *PerformanceOptimizer
    mutex             sync.RWMutex
}

type CacheManager struct {
    L1Cache          *MemoryCache
    L2Cache          *SSDCache
    L3Cache          *HDDCache
    CachePolicy      *CachePolicy
    PurgeManager     *PurgeManager
    Prefetcher       *ContentPrefetcher
    CompressionEngine *CompressionEngine
    HitRatio         *CacheMetrics
}

func (e *EdgeNode) ServeContent(ctx context.Context, request *ContentRequest) (*ContentResponse, error) {
    e.mutex.RLock()
    defer e.mutex.RUnlock()
    
    // Security validation
    if err := e.SecurityEngine.ValidateRequest(request); err != nil {
        return nil, err
    }
    
    // Content key generation
    contentKey := e.generateContentKey(request)
    
    // Multi-tier cache lookup
    content, cacheHit := e.CacheManager.GetContent(contentKey)
    
    if cacheHit {
        // Cache hit - serve from cache
        e.AnalyticsEngine.RecordCacheHit(request, content)
        return e.serveFromCache(content, request)
    }
    
    // Cache miss - fetch from origin
    originContent, err := e.fetchFromOrigin(ctx, request)
    if err != nil {
        return nil, err
    }
    
    // Store in cache for future requests
    e.CacheManager.StoreContent(contentKey, originContent)
    
    // Record analytics
    e.AnalyticsEngine.RecordCacheMiss(request, originContent)
    
    return e.optimizeAndServe(originContent, request)
}

func (cm *CacheManager) GetContent(key string) (*CachedContent, bool) {
    // L1 Cache (Memory) - fastest access
    if content, found := cm.L1Cache.Get(key); found {
        cm.HitRatio.RecordL1Hit()
        return content, true
    }
    
    // L2 Cache (SSD) - fast access
    if content, found := cm.L2Cache.Get(key); found {
        // Promote to L1 cache
        cm.L1Cache.Set(key, content, cm.CachePolicy.L1TTL)
        cm.HitRatio.RecordL2Hit()
        return content, true
    }
    
    // L3 Cache (HDD) - slower but large capacity
    if content, found := cm.L3Cache.Get(key); found {
        // Promote to L2 and L1 caches
        cm.L2Cache.Set(key, content, cm.CachePolicy.L2TTL)
        cm.L1Cache.Set(key, content, cm.CachePolicy.L1TTL)
        cm.HitRatio.RecordL3Hit()
        return content, true
    }
    
    cm.HitRatio.RecordMiss()
    return nil, false
}

func (cm *CacheManager) StoreContent(key string, content *OriginContent) {
    cachedContent := &CachedContent{
        Content:      content.Data,
        Headers:      content.Headers,
        ETag:         content.ETag,
        LastModified: content.LastModified,
        TTL:          cm.calculateTTL(content),
        StoredAt:     time.Now(),
        AccessCount:  0,
        Size:         len(content.Data),
    }
    
    // Intelligent cache tier selection based on content characteristics
    tier := cm.selectOptimalCacheTier(cachedContent)
    
    switch tier {
    case CacheTierL1:
        cm.L1Cache.Set(key, cachedContent, cm.CachePolicy.L1TTL)
    case CacheTierL2:
        cm.L2Cache.Set(key, cachedContent, cm.CachePolicy.L2TTL)
        // Also store in L1 if frequently accessed
        if cm.isFrequentlyAccessed(key) {
            cm.L1Cache.Set(key, cachedContent, cm.CachePolicy.L1TTL)
        }
    case CacheTierL3:
        cm.L3Cache.Set(key, cachedContent, cm.CachePolicy.L3TTL)
    }
}

func (cm *CacheManager) selectOptimalCacheTier(content *CachedContent) CacheTier {
    // Decision matrix based on content characteristics
    if content.Size < 1024*1024 && content.IsFrequentlyRequested() {
        return CacheTierL1 // Small, hot content in memory
    }
    
    if content.Size < 100*1024*1024 && content.IsModeratelyRequested() {
        return CacheTierL2 // Medium content on SSD
    }
    
    return CacheTierL3 // Large or infrequent content on HDD
}

// Advanced Content Prefetching
type ContentPrefetcher struct {
    MLPredictor      *MachineLearningPredictor
    PatternAnalyzer  *RequestPatternAnalyzer
    PrefetchQueue    *PriorityQueue
    BandwidthManager *BandwidthManager
}

func (cp *ContentPrefetcher) PredictAndPrefetch(ctx context.Context) {
    for {
        select {
        case <-ctx.Done():
            return
        case <-time.After(30 * time.Second):
            cp.executePrefetchCycle()
        }
    }
}

func (cp *ContentPrefetcher) executePrefetchCycle() {
    // Analyze request patterns
    patterns := cp.PatternAnalyzer.AnalyzeRecentPatterns()
    
    // Predict likely requests
    predictions := cp.MLPredictor.PredictLikelyRequests(patterns)
    
    // Filter by confidence and available bandwidth
    viablePredictions := cp.filterViablePredictions(predictions)
    
    // Execute prefetch operations
    for _, prediction := range viablePredictions {
        if cp.BandwidthManager.CanAllocateBandwidth(prediction.EstimatedSize) {
            go cp.prefetchContent(prediction)
        }
    }
}

func (cp *ContentPrefetcher) prefetchContent(prediction *ContentPrediction) {
    // Allocate bandwidth
    cp.BandwidthManager.AllocateBandwidth(prediction.EstimatedSize)
    defer cp.BandwidthManager.ReleaseBandwidth(prediction.EstimatedSize)
    
    // Fetch content from origin
    content, err := cp.fetchContentFromOrigin(prediction.URL)
    if err != nil {
        return
    }
    
    // Store in appropriate cache tier
    cp.storeInCache(prediction.URL, content)
}

class AdvancedCachePolicy:
    def __init__(self):
        self.lru_policy = LRUPolicy()
        self.lfu_policy = LFUPolicy()
        self.arc_policy = ARCPolicy()
        self.ml_policy = MLCachePolicy()
        self.hybrid_policy = HybridCachePolicy()
        
    def select_eviction_candidate(self, cache_tier, required_space):
        """Select optimal cache eviction candidate using multiple policies"""
        candidates = {
            'lru': self.lru_policy.get_eviction_candidate(cache_tier),
            'lfu': self.lfu_policy.get_eviction_candidate(cache_tier),
            'arc': self.arc_policy.get_eviction_candidate(cache_tier),
            'ml': self.ml_policy.get_eviction_candidate(cache_tier),
            'hybrid': self.hybrid_policy.get_eviction_candidate(cache_tier)
        }
        
        # Score each candidate
        best_candidate = None
        best_score = float('inf')
        
        for policy_name, candidate in candidates.items():
            if candidate:
                score = self.calculate_eviction_score(candidate, policy_name)
                if score < best_score:
                    best_score = score
                    best_candidate = candidate
        
        return best_candidate
    
    def calculate_eviction_score(self, candidate, policy_name):
        """Calculate composite eviction score"""
        weights = {
            'lru': 0.2,
            'lfu': 0.2,
            'arc': 0.2,
            'ml': 0.3,
            'hybrid': 0.1
        }
        
        # Base score from policy
        base_score = candidate.policy_score
        
        # Adjust for content characteristics
        content_factor = self.calculate_content_factor(candidate)
        
        # Adjust for business value
        business_factor = self.calculate_business_factor(candidate)
        
        # Adjust for cost considerations
        cost_factor = self.calculate_cost_factor(candidate)
        
        composite_score = (base_score * content_factor * 
                          business_factor * cost_factor * weights[policy_name])
        
        return composite_score

class ARCPolicy:
    """Adaptive Replacement Cache policy implementation"""
    
    def __init__(self, cache_size):
        self.cache_size = cache_size
        self.p = 0  # Target size for T1
        self.t1 = OrderedDict()  # Recently used pages
        self.t2 = OrderedDict()  # Frequently used pages
        self.b1 = OrderedDict()  # Ghost list for T1
        self.b2 = OrderedDict()  # Ghost list for T2
        
    def access(self, key, content):
        """Process cache access using ARC algorithm"""
        if key in self.t1:
            # Hit in T1 - move to T2
            del self.t1[key]
            self.t2[key] = content
            return content
        
        if key in self.t2:
            # Hit in T2 - move to end
            del self.t2[key]
            self.t2[key] = content
            return content
        
        if key in self.b1:
            # Hit in B1 - adapt and move to T2
            self.adapt(len(self.b1), len(self.b2))
            self.replace(key)
            del self.b1[key]
            self.t2[key] = content
            return None
        
        if key in self.b2:
            # Hit in B2 - adapt and move to T2
            self.adapt(len(self.b1), len(self.b2))
            self.replace(key)
            del self.b2[key]
            self.t2[key] = content
            return None
        
        # Cache miss
        if len(self.t1) + len(self.b1) == self.cache_size:
            if len(self.t1) < self.cache_size:
                # Remove from B1
                self.b1.popitem(last=False)
                self.replace(key)
            else:
                # Remove from T1
                self.t1.popitem(last=False)
        elif len(self.t1) + len(self.t2) + len(self.b1) + len(self.b2) >= self.cache_size:
            if len(self.t1) + len(self.t2) + len(self.b1) + len(self.b2) == 2 * self.cache_size:
                # Remove from B2
                self.b2.popitem(last=False)
            self.replace(key)
        
        self.t1[key] = content
        return None
    
    def adapt(self, b1_size, b2_size):
        """Adapt the target size for T1"""
        if b1_size >= b2_size:
            self.p = min(self.cache_size, self.p + max(1, b2_size / b1_size))
        else:
            self.p = max(0, self.p - max(1, b1_size / b2_size))
    
    def replace(self, key):
        """Replace a page according to ARC policy"""
        if len(self.t1) >= 1 and ((key in self.b2 and len(self.t1) == self.p) or len(self.t1) > self.p):
            # Move from T1 to B1
            old_key = next(iter(self.t1))
            old_content = self.t1.pop(old_key)
            self.b1[old_key] = None
        else:
            # Move from T2 to B2
            old_key = next(iter(self.t2))
            old_content = self.t2.pop(old_key)
            self.b2[old_key] = None

class MLCachePolicy:
    """Machine Learning-based cache policy"""
    
    def __init__(self):
        self.feature_extractor = FeatureExtractor()
        self.predictor = CachePredictor()
        self.feedback_collector = FeedbackCollector()
        
    def predict_future_access(self, content_items):
        """Predict future access patterns using ML"""
        features = []
        for item in content_items:
            feature_vector = self.feature_extractor.extract_features(item)
            features.append(feature_vector)
        
        # Predict access probabilities
        access_predictions = self.predictor.predict_access_probability(features)
        
        return access_predictions
    
    def get_eviction_candidate(self, cache_tier):
        """Select eviction candidate using ML predictions"""
        content_items = cache_tier.get_all_items()
        
        # Get ML predictions for future access
        predictions = self.predict_future_access(content_items)
        
        # Find item with lowest predicted access probability
        min_probability = float('inf')
        eviction_candidate = None
        
        for i, item in enumerate(content_items):
            predicted_access = predictions[i]
            
            # Consider multiple factors in addition to access probability
            composite_score = self.calculate_ml_score(item, predicted_access)
            
            if composite_score < min_probability:
                min_probability = composite_score
                eviction_candidate = item
        
        return eviction_candidate
    
    def calculate_ml_score(self, item, predicted_access):
        """Calculate composite ML-based score for eviction decision"""
        # Base score from ML prediction
        ml_score = 1.0 - predicted_access
        
        # Adjust for content size (prefer evicting larger items)
        size_factor = min(2.0, item.size / (1024 * 1024))  # MB
        
        # Adjust for age (prefer evicting older items)
        age_factor = min(2.0, (time.time() - item.last_access) / 3600)  # hours
        
        # Adjust for access frequency
        frequency_factor = 1.0 / max(1, item.access_count)
        
        composite_score = ml_score * size_factor * age_factor * frequency_factor
        
        return composite_score

class FeatureExtractor:
    def extract_features(self, content_item):
        """Extract features for ML cache prediction"""
        features = {
            # Temporal features
            'hour_of_day': time.localtime().tm_hour,
            'day_of_week': time.localtime().tm_wday,
            'time_since_last_access': time.time() - content_item.last_access,
            'time_since_creation': time.time() - content_item.created_at,
            
            # Content characteristics
            'content_size': content_item.size,
            'content_type': self.encode_content_type(content_item.mime_type),
            'compression_ratio': content_item.compression_ratio,
            
            # Access patterns
            'access_count': content_item.access_count,
            'access_frequency': content_item.access_count / max(1, 
                (time.time() - content_item.created_at) / 3600),
            'unique_client_count': len(content_item.unique_clients),
            
            # Geographic features
            'request_geographic_spread': content_item.geographic_spread,
            'primary_region': self.encode_region(content_item.primary_region),
            
            # Business features
            'content_priority': content_item.business_priority,
            'customer_tier': content_item.customer_tier,
            'revenue_impact': content_item.revenue_impact
        }
        
        return list(features.values())

package edge

import (
    "context"
    "fmt"
    "time"
    "sync"
)

type EdgeFunctionRuntime struct {
    Functions        map[string]*EdgeFunction
    ResourceManager  *ResourceManager
    SecurityManager  *SecurityManager
    MonitoringAgent  *MonitoringAgent
    ScalingManager   *ScalingManager
    CodeCache        *CodeCache
    mutex            sync.RWMutex
}

type EdgeFunction struct {
    ID              string
    Name            string
    Runtime         RuntimeType
    Code            []byte
    Config          *FunctionConfig
    Resources       *ResourceAllocation
    Metrics         *FunctionMetrics
    WarmInstances   []*FunctionInstance
    ColdInstances   []*FunctionInstance
    LastDeployment  time.Time
}

type FunctionInstance struct {
    ID              string
    Runtime         *IsolatedRuntime
    State           InstanceState
    LastUsed        time.Time
    RequestCount    int64
    MemoryUsage     int64
    CPUUsage        float64
    StartupTime     time.Duration
}

func (efr *EdgeFunctionRuntime) ExecuteFunction(ctx context.Context, 
                                               functionID string, 
                                               request *EdgeRequest) (*EdgeResponse, error) {
    efr.mutex.RLock()
    function, exists := efr.Functions[functionID]
    efr.mutex.RUnlock()
    
    if !exists {
        return nil, fmt.Errorf("function %s not found", functionID)
    }
    
    // Get or create function instance
    instance, err := efr.getOrCreateInstance(function)
    if err != nil {
        return nil, err
    }
    
    // Security validation
    if err := efr.SecurityManager.ValidateExecution(function, request); err != nil {
        return nil, err
    }
    
    // Execute function with timeout
    executionCtx, cancel := context.WithTimeout(ctx, function.Config.Timeout)
    defer cancel()
    
    startTime := time.Now()
    response, err := instance.Execute(executionCtx, request)
    executionTime := time.Since(startTime)
    
    // Update metrics
    efr.updateExecutionMetrics(function, instance, executionTime, err)
    
    // Return instance to pool or terminate
    efr.returnInstance(function, instance)
    
    return response, err
}

func (efr *EdgeFunctionRuntime) getOrCreateInstance(function *EdgeFunction) (*FunctionInstance, error) {
    // Try to get warm instance first
    if len(function.WarmInstances) > 0 {
        instance := function.WarmInstances[0]
        function.WarmInstances = function.WarmInstances[1:]
        return instance, nil
    }
    
    // Check if we can create new instance
    if !efr.ResourceManager.CanAllocateResources(function.Resources) {
        return nil, fmt.Errorf("insufficient resources")
    }
    
    // Create new instance (cold start)
    instance, err := efr.createNewInstance(function)
    if err != nil {
        return nil, err
    }
    
    return instance, nil
}

func (efr *EdgeFunctionRuntime) createNewInstance(function *EdgeFunction) (*FunctionInstance, error) {
    // Allocate resources
    allocation, err := efr.ResourceManager.AllocateResources(function.Resources)
    if err != nil {
        return nil, err
    }
    
    // Create isolated runtime
    runtime, err := efr.createIsolatedRuntime(function, allocation)
    if err != nil {
        efr.ResourceManager.ReleaseResources(allocation)
        return nil, err
    }
    
    // Load function code
    if err := runtime.LoadCode(function.Code); err != nil {
        runtime.Terminate()
        efr.ResourceManager.ReleaseResources(allocation)
        return nil, err
    }
    
    instance := &FunctionInstance{
        ID:          generateInstanceID(),
        Runtime:     runtime,
        State:       InstanceStateInitializing,
        LastUsed:    time.Now(),
        RequestCount: 0,
    }
    
    // Initialize function
    if err := instance.Initialize(); err != nil {
        instance.Terminate()
        return nil, err
    }
    
    instance.State = InstanceStateReady
    return instance, nil
}

func (efr *EdgeFunctionRuntime) createIsolatedRuntime(function *EdgeFunction, 
                                                     allocation *ResourceAllocation) (*IsolatedRuntime, error) {
    switch function.Runtime {
    case RuntimeJavaScript:
        return NewV8Runtime(allocation)
    case RuntimeWebAssembly:
        return NewWASMRuntime(allocation)
    case RuntimePython:
        return NewPythonRuntime(allocation)
    case RuntimeGo:
        return NewGoRuntime(allocation)
    default:
        return nil, fmt.Errorf("unsupported runtime: %s", function.Runtime)
    }
}

// WebAssembly Runtime Implementation
type WASMRuntime struct {
    Module          *wasmtime.Module
    Store           *wasmtime.Store
    Instance        *wasmtime.Instance
    Memory          *wasmtime.Memory
    ResourceLimits  *ResourceAllocation
    StartTime       time.Time
}

func NewWASMRuntime(allocation *ResourceAllocation) (*WASMRuntime, error) {
    engine := wasmtime.NewEngine()
    store := wasmtime.NewStore(engine)
    
    // Configure resource limits
    store.SetEpochDeadline(uint64(allocation.MaxExecutionTime.Nanoseconds()))
    
    return &WASMRuntime{
        Store:          store,
        ResourceLimits: allocation,
        StartTime:      time.Now(),
    }, nil
}

func (wr *WASMRuntime) LoadCode(code []byte) error {
    module, err := wasmtime.NewModule(wr.Store.Engine, code)
    if err != nil {
        return err
    }
    
    wr.Module = module
    
    // Create instance with imports
    imports := wr.createImports()
    instance, err := wasmtime.NewInstance(wr.Store, wr.Module, imports)
    if err != nil {
        return err
    }
    
    wr.Instance = instance
    
    // Get memory export
    memoryExport := wr.Instance.GetExport(wr.Store, "memory")
    if memoryExport != nil {
        wr.Memory = memoryExport.Memory()
    }
    
    return nil
}

func (wr *WASMRuntime) Execute(ctx context.Context, request *EdgeRequest) (*EdgeResponse, error) {
    // Get main function
    mainFunc := wr.Instance.GetFunc(wr.Store, "main")
    if mainFunc == nil {
        return nil, fmt.Errorf("main function not found")
    }
    
    // Serialize request
    requestData, err := json.Marshal(request)
    if err != nil {
        return nil, err
    }
    
    // Write request to WASM memory
    requestPtr, err := wr.allocateMemory(len(requestData))
    if err != nil {
        return nil, err
    }
    
    copy(wr.Memory.Data(wr.Store)[requestPtr:], requestData)
    
    // Execute function
    result, err := mainFunc.Call(wr.Store, requestPtr, len(requestData))
    if err != nil {
        return nil, err
    }
    
    // Read response from WASM memory
    responsePtr := result.(int32)
    responseData := wr.readFromMemory(responsePtr)
    
    // Deserialize response
    var response EdgeResponse
    if err := json.Unmarshal(responseData, &response); err != nil {
        return nil, err
    }
    
    return &response, nil
}

// Edge Function Auto-scaling
type EdgeFunctionScaler struct {
    MetricsCollector *MetricsCollector
    ScalingPolicies  map[string]*ScalingPolicy
    InstanceManager  *InstanceManager
}

func (efs *EdgeFunctionScaler) MonitorAndScale() {
    ticker := time.NewTicker(10 * time.Second)
    defer ticker.Stop()
    
    for range ticker.C {
        efs.evaluateScalingDecisions()
    }
}

func (efs *EdgeFunctionScaler) evaluateScalingDecisions() {
    for functionID, policy := range efs.ScalingPolicies {
        metrics := efs.MetricsCollector.GetFunctionMetrics(functionID)
        
        scalingDecision := efs.makeScalingDecision(metrics, policy)
        
        if scalingDecision.ShouldScale() {
            efs.executeScaling(functionID, scalingDecision)
        }
    }
}

func (efs *EdgeFunctionScaler) makeScalingDecision(metrics *FunctionMetrics, 
                                                  policy *ScalingPolicy) *ScalingDecision {
    decision := &ScalingDecision{
        FunctionID: metrics.FunctionID,
        CurrentInstances: metrics.ActiveInstances,
    }
    
    // Evaluate scale-up conditions
    if metrics.AverageLatency > policy.LatencyThreshold ||
       metrics.RequestRate > policy.RequestRateThreshold ||
       metrics.QueueDepth > policy.QueueDepthThreshold {
        
        targetInstances := efs.calculateTargetInstances(metrics, policy)
        if targetInstances > decision.CurrentInstances {
            decision.Action = ScaleUp
            decision.TargetInstances = targetInstances
        }
    }
    
    // Evaluate scale-down conditions
    if metrics.AverageLatency < policy.LatencyThreshold * 0.5 &&
       metrics.RequestRate < policy.RequestRateThreshold * 0.3 &&
       decision.CurrentInstances > policy.MinInstances {
        
        targetInstances := max(policy.MinInstances, 
                              decision.CurrentInstances - 1)
        decision.Action = ScaleDown
        decision.TargetInstances = targetInstances
    }
    
    return decision
}

class GlobalLoadBalancer:
    def __init__(self):
        self.geo_ip_database = GeoIPDatabase()
        self.latency_matrix = GlobalLatencyMatrix()
        self.capacity_monitor = CapacityMonitor()
        self.health_monitor = GlobalHealthMonitor()
        self.traffic_policies = TrafficPolicyEngine()
        
    def route_request(self, request):
        """Route request to optimal edge location"""
        # Determine client location
        client_location = self.geo_ip_database.get_location(request.client_ip)
        
        # Get available edge locations
        available_edges = self.get_available_edge_locations()
        
        # Apply traffic policies
        eligible_edges = self.traffic_policies.filter_edges(
            available_edges, request, client_location
        )
        
        # Calculate routing scores
        edge_scores = {}
        for edge in eligible_edges:
            score = self.calculate_routing_score(
                edge, client_location, request
            )
            edge_scores[edge] = score
        
        # Select best edge location
        best_edge = max(edge_scores, key=edge_scores.get)
        
        return best_edge
    
    def calculate_routing_score(self, edge, client_location, request):
        """Calculate composite routing score for edge location"""
        weights = {
            'latency': 0.4,
            'capacity': 0.3,
            'health': 0.2,
            'cost': 0.1
        }
        
        # Latency score (lower latency = higher score)
        latency = self.latency_matrix.get_latency(client_location, edge.location)
        latency_score = max(0, 100 - latency)
        
        # Capacity score
        capacity_utilization = self.capacity_monitor.get_utilization(edge)
        capacity_score = max(0, 100 - capacity_utilization)
        
        # Health score
        health_status = self.health_monitor.get_health_score(edge)
        
        # Cost score (lower cost = higher score)
        cost_factor = self.calculate_cost_factor(edge, request)
        cost_score = max(0, 100 - cost_factor)
        
        # Composite score
        composite_score = (
            weights['latency'] * latency_score +
            weights['capacity'] * capacity_score +
            weights['health'] * health_status +
            weights['cost'] * cost_score
        )
        
        return composite_score

class TrafficPolicyEngine:
    def __init__(self):
        self.policies = {}
        self.rule_engine = PolicyRuleEngine()
        
    def add_policy(self, policy_name, policy_config):
        """Add traffic steering policy"""
        policy = TrafficPolicy(
            name=policy_name,
            rules=policy_config.rules,
            conditions=policy_config.conditions,
            actions=policy_config.actions
        )
        
        self.policies[policy_name] = policy
    
    def filter_edges(self, edges, request, client_location):
        """Filter edge locations based on traffic policies"""
        eligible_edges = edges.copy()
        
        for policy_name, policy in self.policies.items():
            if policy.applies_to_request(request):
                eligible_edges = policy.filter_edges(
                    eligible_edges, request, client_location
                )
        
        return eligible_edges

class AdvancedTrafficSteering:
    def __init__(self):
        self.ml_predictor = TrafficPredictor()
        self.anomaly_detector = TrafficAnomalyDetector()
        self.cost_optimizer = CostOptimizer()
        
    def intelligent_traffic_steering(self, request, available_edges):
        """Implement AI-driven traffic steering"""
        # Predict traffic patterns
        traffic_prediction = self.ml_predictor.predict_traffic_patterns(
            request, available_edges
        )
        
        # Detect traffic anomalies
        anomalies = self.anomaly_detector.detect_anomalies(request)
        
        # Optimize for cost efficiency
        cost_optimization = self.cost_optimizer.optimize_routing(
            request, available_edges
        )
        
        # Make intelligent routing decision
        routing_decision = self.make_intelligent_decision(
            traffic_prediction, anomalies, cost_optimization
        )
        
        return routing_decision
    
    def make_intelligent_decision(self, prediction, anomalies, cost_opt):
        """Make intelligent routing decision using ML"""
        decision_factors = {
            'predicted_performance': prediction.performance_score,
            'anomaly_risk': anomalies.risk_score,
            'cost_efficiency': cost_opt.efficiency_score,
            'resource_availability': prediction.resource_availability
        }
        
        # Use ML model to make final decision
        optimal_edge = self.ml_predictor.select_optimal_edge(
            decision_factors
        )
        
        return optimal_edge

class CDNPerformanceOptimizer:
    def __init__(self):
        self.compression_optimizer = CompressionOptimizer()
        self.image_optimizer = ImageOptimizer()
        self.protocol_optimizer = ProtocolOptimizer()
        self.bandwidth_optimizer = BandwidthOptimizer()
        
    def optimize_content_delivery(self, content, request):
        """Optimize content for delivery"""
        optimized_content = content
        
        # Compression optimization
        if self.should_compress(content, request):
            optimized_content = self.compression_optimizer.optimize(
                optimized_content, request
            )
        
        # Image optimization
        if content.is_image():
            optimized_content = self.image_optimizer.optimize(
                optimized_content, request
            )
        
        # Protocol optimization
        protocol = self.protocol_optimizer.select_optimal_protocol(request)
        
        # Bandwidth optimization
        optimized_content = self.bandwidth_optimizer.optimize_for_bandwidth(
            optimized_content, request.connection_speed
        )
        
        return optimized_content, protocol
    
    def should_compress(self, content, request):
        """Determine if content should be compressed"""
        # Don't compress already compressed content
        if content.is_already_compressed():
            return False
        
        # Don't compress small files (compression overhead)
        if content.size < 1024:  # 1KB
            return False
        
        # Consider client capabilities
        if not request.supports_compression():
            return False
        
        # Consider content type
        compressible_types = [
            'text/html', 'text/css', 'text/javascript',
            'application/json', 'application/xml'
        ]
        
        return content.mime_type in compressible_types

class EdgeCacheInvalidation:
    def __init__(self):
        self.invalidation_strategies = {
            'immediate': ImmediateInvalidation(),
            'progressive': ProgressiveInvalidation(),
            'smart': SmartInvalidation(),
            'scheduled': ScheduledInvalidation()
        }
        
    def invalidate_content(self, content_pattern, strategy='smart'):
        """Invalidate content across edge locations"""
        invalidation_strategy = self.invalidation_strategies[strategy]
        
        # Find affected edge locations
        affected_edges = self.find_affected_edges(content_pattern)
        
        # Execute invalidation
        results = invalidation_strategy.execute(
            content_pattern, affected_edges
        )
        
        return results
    
    def find_affected_edges(self, content_pattern):
        """Find edge locations that have the content cached"""
        affected_edges = []
        
        for edge in self.get_all_edge_locations():
            if edge.cache_manager.has_content(content_pattern):
                affected_edges.append(edge)
        
        return affected_edges

class SmartInvalidation:
    def execute(self, content_pattern, affected_edges):
        """Smart invalidation based on usage patterns"""
        invalidation_plan = self.create_invalidation_plan(
            content_pattern, affected_edges
        )
        
        results = {}
        
        for edge, plan in invalidation_plan.items():
            if plan.immediate:
                # Immediate invalidation for high-traffic edges
                result = self.immediate_invalidate(edge, content_pattern)
            else:
                # Lazy invalidation for low-traffic edges
                result = self.lazy_invalidate(edge, content_pattern)
            
            results[edge.id] = result
        
        return results
    
    def create_invalidation_plan(self, content_pattern, affected_edges):
        """Create intelligent invalidation plan"""
        plan = {}
        
        for edge in affected_edges:
            # Analyze traffic patterns
            traffic_analysis = self.analyze_edge_traffic(edge, content_pattern)
            
            # Determine invalidation strategy
            if traffic_analysis.is_high_traffic():
                plan[edge] = InvalidationPlan(immediate=True)
            else:
                plan[edge] = InvalidationPlan(immediate=False, ttl_override=300)
        
        return plan