package performance

import (
    "context"
    "sync"
    "time"
    "math"
    "sort"
    "net"
    "github.com/google/gopacket"
    "github.com/google/gopacket/pcap"
)

type PerformanceMetrics struct {
    Timestamp       time.Time     `json:"timestamp"`
    Latency         time.Duration `json:"latency"`
    Jitter          time.Duration `json:"jitter"`
    PacketLoss      float64       `json:"packet_loss"`
    Throughput      float64       `json:"throughput"`
    Bandwidth       float64       `json:"bandwidth"`
    Utilization     float64       `json:"utilization"`
    ErrorRate       float64       `json:"error_rate"`
    RetransmitRate  float64       `json:"retransmit_rate"`
    QueueDepth      int           `json:"queue_depth"`
    BufferUsage     float64       `json:"buffer_usage"`
}

type NetworkInterface struct {
    Name            string        `json:"name"`
    Type            string        `json:"type"`
    Speed           uint64        `json:"speed"`
    Duplex          string        `json:"duplex"`
    MTU             int           `json:"mtu"`
    AdminStatus     string        `json:"admin_status"`
    OperStatus      string        `json:"oper_status"`
    LastChange      time.Time     `json:"last_change"`
    InOctets        uint64        `json:"in_octets"`
    OutOctets       uint64        `json:"out_octets"`
    InPackets       uint64        `json:"in_packets"`
    OutPackets      uint64        `json:"out_packets"`
    InErrors        uint64        `json:"in_errors"`
    OutErrors       uint64        `json:"out_errors"`
    InDiscards      uint64        `json:"in_discards"`
    OutDiscards     uint64        `json:"out_discards"`
}

type PerformanceMonitor struct {
    Interfaces      map[string]*NetworkInterface
    Metrics         map[string][]PerformanceMetrics
    ThresholdEngine *ThresholdEngine
    AlertManager    *AlertManager
    FlowAnalyzer    *FlowAnalyzer
    LatencyProbe    *LatencyProbe
    ThroughputTest  *ThroughputTester
    PacketCapture   *PacketCaptureEngine
    mutex           sync.RWMutex
}

func NewPerformanceMonitor() *PerformanceMonitor {
    return &PerformanceMonitor{
        Interfaces:      make(map[string]*NetworkInterface),
        Metrics:         make(map[string][]PerformanceMetrics),
        ThresholdEngine: NewThresholdEngine(),
        AlertManager:    NewAlertManager(),
        FlowAnalyzer:    NewFlowAnalyzer(),
        LatencyProbe:    NewLatencyProbe(),
        ThroughputTest:  NewThroughputTester(),
        PacketCapture:   NewPacketCaptureEngine(),
    }
}

func (pm *PerformanceMonitor) StartMonitoring(ctx context.Context, interval time.Duration) error {
    ticker := time.NewTicker(interval)
    defer ticker.Stop()

    for {
        select {
        case <-ctx.Done():
            return ctx.Err()
        case <-ticker.C:
            pm.collectMetrics()
        }
    }
}

func (pm *PerformanceMonitor) collectMetrics() {
    pm.mutex.Lock()
    defer pm.mutex.Unlock()

    for interfaceName, iface := range pm.Interfaces {
        metrics := pm.gatherInterfaceMetrics(iface)
        
        // Store metrics
        pm.Metrics[interfaceName] = append(pm.Metrics[interfaceName], metrics)
        
        // Keep only last 1000 measurements
        if len(pm.Metrics[interfaceName]) > 1000 {
            pm.Metrics[interfaceName] = pm.Metrics[interfaceName][1:]
        }
        
        // Check thresholds
        violations := pm.ThresholdEngine.CheckThresholds(interfaceName, metrics)
        for _, violation := range violations {
            pm.AlertManager.TriggerAlert(violation)
        }
    }
}

func (pm *PerformanceMonitor) gatherInterfaceMetrics(iface *NetworkInterface) PerformanceMetrics {
    metrics := PerformanceMetrics{
        Timestamp: time.Now(),
    }
    
    // Calculate throughput
    metrics.Throughput = pm.calculateThroughput(iface)
    
    // Calculate utilization
    metrics.Utilization = pm.calculateUtilization(iface)
    
    // Measure latency
    metrics.Latency = pm.LatencyProbe.MeasureLatency(iface.Name)
    
    // Calculate jitter
    metrics.Jitter = pm.calculateJitter(iface.Name)
    
    // Detect packet loss
    metrics.PacketLoss = pm.detectPacketLoss(iface)
    
    // Calculate error rates
    metrics.ErrorRate = pm.calculateErrorRate(iface)
    metrics.RetransmitRate = pm.calculateRetransmitRate(iface)
    
    // Get queue and buffer information
    metrics.QueueDepth = pm.getQueueDepth(iface.Name)
    metrics.BufferUsage = pm.getBufferUsage(iface.Name)
    
    return metrics
}

func (pm *PerformanceMonitor) calculateThroughput(iface *NetworkInterface) float64 {
    // Get current counters
    currentTime := time.Now()
    currentInOctets := pm.getCurrentInOctets(iface.Name)
    currentOutOctets := pm.getCurrentOutOctets(iface.Name)
    
    // Calculate delta from previous measurement
    prevMetrics := pm.getPreviousMetrics(iface.Name)
    if prevMetrics == nil {
        return 0
    }
    
    timeDelta := currentTime.Sub(prevMetrics.Timestamp).Seconds()
    if timeDelta <= 0 {
        return 0
    }
    
    inDelta := float64(currentInOctets - iface.InOctets)
    outDelta := float64(currentOutOctets - iface.OutOctets)
    
    // Calculate bits per second
    throughput := ((inDelta + outDelta) * 8) / timeDelta
    
    // Update interface counters
    iface.InOctets = currentInOctets
    iface.OutOctets = currentOutOctets
    
    return throughput
}

func (pm *PerformanceMonitor) calculateUtilization(iface *NetworkInterface) float64 {
    throughput := pm.calculateThroughput(iface)
    if iface.Speed == 0 {
        return 0
    }
    
    return (throughput / float64(iface.Speed)) * 100
}

func (pm *PerformanceMonitor) calculateJitter(interfaceName string) time.Duration {
    recentLatencies := pm.getRecentLatencies(interfaceName, 10)
    if len(recentLatencies) < 2 {
        return 0
    }
    
    var deltas []float64
    for i := 1; i < len(recentLatencies); i++ {
        delta := math.Abs(float64(recentLatencies[i] - recentLatencies[i-1]))
        deltas.append(delta)
    }
    
    // Calculate average jitter
    var sum float64
    for _, delta := range deltas {
        sum += delta
    }
    
    avgJitter := sum / float64(len(deltas))
    return time.Duration(avgJitter)
}

type ThresholdEngine struct {
    Thresholds map[string]map[string]Threshold
    mutex      sync.RWMutex
}

type Threshold struct {
    MetricName    string    `json:"metric_name"`
    WarningLevel  float64   `json:"warning_level"`
    CriticalLevel float64   `json:"critical_level"`
    Operator      string    `json:"operator"` // "gt", "lt", "eq"
    Enabled       bool      `json:"enabled"`
    LastTriggered time.Time `json:"last_triggered"`
}

type ThresholdViolation struct {
    InterfaceName string    `json:"interface_name"`
    MetricName    string    `json:"metric_name"`
    CurrentValue  float64   `json:"current_value"`
    ThresholdValue float64  `json:"threshold_value"`
    Severity      string    `json:"severity"`
    Timestamp     time.Time `json:"timestamp"`
    Description   string    `json:"description"`
}

func NewThresholdEngine() *ThresholdEngine {
    return &ThresholdEngine{
        Thresholds: make(map[string]map[string]Threshold),
    }
}

func (te *ThresholdEngine) SetThreshold(interfaceName, metricName string, threshold Threshold) {
    te.mutex.Lock()
    defer te.mutex.Unlock()
    
    if te.Thresholds[interfaceName] == nil {
        te.Thresholds[interfaceName] = make(map[string]Threshold)
    }
    
    te.Thresholds[interfaceName][metricName] = threshold
}

func (te *ThresholdEngine) CheckThresholds(interfaceName string, metrics PerformanceMetrics) []ThresholdViolation {
    te.mutex.RLock()
    defer te.mutex.RUnlock()
    
    var violations []ThresholdViolation
    
    interfaceThresholds, exists := te.Thresholds[interfaceName]
    if !exists {
        return violations
    }
    
    metricValues := map[string]float64{
        "latency":        float64(metrics.Latency.Nanoseconds()) / 1e6, // Convert to milliseconds
        "jitter":         float64(metrics.Jitter.Nanoseconds()) / 1e6,
        "packet_loss":    metrics.PacketLoss,
        "throughput":     metrics.Throughput,
        "utilization":    metrics.Utilization,
        "error_rate":     metrics.ErrorRate,
        "retransmit_rate": metrics.RetransmitRate,
        "buffer_usage":   metrics.BufferUsage,
    }
    
    for metricName, currentValue := range metricValues {
        threshold, exists := interfaceThresholds[metricName]
        if !exists || !threshold.Enabled {
            continue
        }
        
        violation := te.checkSingleThreshold(interfaceName, metricName, currentValue, threshold)
        if violation != nil {
            violations = append(violations, *violation)
        }
    }
    
    return violations
}

func (te *ThresholdEngine) checkSingleThreshold(interfaceName, metricName string, 
                                              currentValue float64, threshold Threshold) *ThresholdViolation {
    var violated bool
    var severity string
    var thresholdValue float64
    
    switch threshold.Operator {
    case "gt":
        if currentValue > threshold.CriticalLevel {
            violated = true
            severity = "critical"
            thresholdValue = threshold.CriticalLevel
        } else if currentValue > threshold.WarningLevel {
            violated = true
            severity = "warning"
            thresholdValue = threshold.WarningLevel
        }
    case "lt":
        if currentValue < threshold.CriticalLevel {
            violated = true
            severity = "critical"
            thresholdValue = threshold.CriticalLevel
        } else if currentValue < threshold.WarningLevel {
            violated = true
            severity = "warning"
            thresholdValue = threshold.WarningLevel
        }
    }
    
    if !violated {
        return nil
    }
    
    return &ThresholdViolation{
        InterfaceName:  interfaceName,
        MetricName:     metricName,
        CurrentValue:   currentValue,
        ThresholdValue: thresholdValue,
        Severity:       severity,
        Timestamp:      time.Now(),
        Description:    te.generateViolationDescription(metricName, currentValue, thresholdValue, severity),
    }
}

type LatencyProbe struct {
    Targets    map[string]string // interface -> target IP
    Interval   time.Duration
    Timeout    time.Duration
    Results    map[string][]LatencyResult
    mutex      sync.RWMutex
}

type LatencyResult struct {
    Timestamp time.Time     `json:"timestamp"`
    Target    string        `json:"target"`
    Latency   time.Duration `json:"latency"`
    Success   bool          `json:"success"`
    PacketSize int          `json:"packet_size"`
}

func NewLatencyProbe() *LatencyProbe {
    return &LatencyProbe{
        Targets:  make(map[string]string),
        Results:  make(map[string][]LatencyResult),
        Interval: 1 * time.Second,
        Timeout:  5 * time.Second,
    }
}

func (lp *LatencyProbe) MeasureLatency(interfaceName string) time.Duration {
    target, exists := lp.Targets[interfaceName]
    if !exists {
        return 0
    }
    
    start := time.Now()
    conn, err := net.DialTimeout("udp", target+":53", lp.Timeout)
    if err != nil {
        lp.recordResult(interfaceName, LatencyResult{
            Timestamp:  start,
            Target:     target,
            Success:    false,
            PacketSize: 64,
        })
        return 0
    }
    defer conn.Close()
    
    latency := time.Since(start)
    
    lp.recordResult(interfaceName, LatencyResult{
        Timestamp:  start,
        Target:     target,
        Latency:    latency,
        Success:    true,
        PacketSize: 64,
    })
    
    return latency
}

func (lp *LatencyProbe) recordResult(interfaceName string, result LatencyResult) {
    lp.mutex.Lock()
    defer lp.mutex.Unlock()
    
    lp.Results[interfaceName] = append(lp.Results[interfaceName], result)
    
    // Keep only last 100 results
    if len(lp.Results[interfaceName]) > 100 {
        lp.Results[interfaceName] = lp.Results[interfaceName][1:]
    }
}

type ThroughputTester struct {
    TestServers map[string]string // interface -> test server
    TestDuration time.Duration
    PacketSize   int
    Concurrency  int
    Results      map[string][]ThroughputResult
    mutex        sync.RWMutex
}

type ThroughputResult struct {
    Timestamp      time.Time     `json:"timestamp"`
    Server         string        `json:"server"`
    UploadSpeed    float64       `json:"upload_speed"`   // Mbps
    DownloadSpeed  float64       `json:"download_speed"` // Mbps
    RTT            time.Duration `json:"rtt"`
    PacketLoss     float64       `json:"packet_loss"`
    TestDuration   time.Duration `json:"test_duration"`
}

func NewThroughputTester() *ThroughputTester {
    return &ThroughputTester{
        TestServers:  make(map[string]string),
        Results:      make(map[string][]ThroughputResult),
        TestDuration: 10 * time.Second,
        PacketSize:   1500,
        Concurrency:  4,
    }
}

func (tt *ThroughputTester) RunThroughputTest(interfaceName string) ThroughputResult {
    server, exists := tt.TestServers[interfaceName]
    if !exists {
        return ThroughputResult{}
    }
    
    start := time.Now()
    
    // Run upload test
    uploadSpeed := tt.runUploadTest(server)
    
    // Run download test
    downloadSpeed := tt.runDownloadTest(server)
    
    // Measure RTT
    rtt := tt.measureRTT(server)
    
    result := ThroughputResult{
        Timestamp:     start,
        Server:        server,
        UploadSpeed:   uploadSpeed,
        DownloadSpeed: downloadSpeed,
        RTT:           rtt,
        TestDuration:  time.Since(start),
    }
    
    tt.recordThroughputResult(interfaceName, result)
    
    return result
}

func (tt *ThroughputTester) runUploadTest(server string) float64 {
    // Implement upload speed test
    // This is a simplified implementation
    conn, err := net.Dial("tcp", server+":8080")
    if err != nil {
        return 0
    }
    defer conn.Close()
    
    data := make([]byte, tt.PacketSize)
    start := time.Now()
    totalBytes := 0
    
    for time.Since(start) < tt.TestDuration {
        n, err := conn.Write(data)
        if err != nil {
            break
        }
        totalBytes += n
    }
    
    duration := time.Since(start).Seconds()
    if duration == 0 {
        return 0
    }
    
    // Convert to Mbps
    return float64(totalBytes*8) / (duration * 1e6)
}

func (tt *ThroughputTester) runDownloadTest(server string) float64 {
    // Implement download speed test
    conn, err := net.Dial("tcp", server+":8081")
    if err != nil {
        return 0
    }
    defer conn.Close()
    
    buffer := make([]byte, tt.PacketSize)
    start := time.Now()
    totalBytes := 0
    
    for time.Since(start) < tt.TestDuration {
        n, err := conn.Read(buffer)
        if err != nil {
            break
        }
        totalBytes += n
    }
    
    duration := time.Since(start).Seconds()
    if duration == 0 {
        return 0
    }
    
    // Convert to Mbps
    return float64(totalBytes*8) / (duration * 1e6)
}

import asyncio
import numpy as np
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import pandas as pd
import time

class OptimizationType(Enum):
    BANDWIDTH_ALLOCATION = "bandwidth_allocation"
    QOS_TUNING = "qos_tuning"
    BUFFER_SIZING = "buffer_sizing"
    CONGESTION_CONTROL = "congestion_control"
    ROUTING_OPTIMIZATION = "routing_optimization"
    LOAD_BALANCING = "load_balancing"

@dataclass
class PerformanceBottleneck:
    interface_name: str
    bottleneck_type: str
    severity: str
    current_value: float
    expected_value: float
    impact_score: float
    root_cause: str
    recommendations: List[str]
    detected_at: float

@dataclass
class OptimizationAction:
    action_type: OptimizationType
    target_interface: str
    parameters: Dict[str, Any]
    expected_improvement: float
    risk_level: str
    execution_time: float
    rollback_procedure: str

class NetworkPerformanceOptimizer:
    def __init__(self):
        self.bottleneck_detector = BottleneckDetector()
        self.optimization_engine = OptimizationEngine()
        self.traffic_analyzer = TrafficAnalyzer()
        self.qos_manager = QoSManager()
        self.congestion_controller = CongestionController()
        self.route_optimizer = RouteOptimizer()
        self.anomaly_detector = AnomalyDetector()
        
    async def run_optimization_cycle(self, interfaces: List[str]) -> Dict[str, Any]:
        """Run complete optimization cycle for specified interfaces"""
        optimization_results = {
            'cycle_id': self._generate_cycle_id(),
            'start_time': time.time(),
            'interfaces_analyzed': interfaces,
            'bottlenecks_detected': [],
            'optimizations_applied': [],
            'performance_improvements': {},
            'recommendations': []
        }
        
        # Detect performance bottlenecks
        for interface in interfaces:
            bottlenecks = await self.bottleneck_detector.detect_bottlenecks(interface)
            optimization_results['bottlenecks_detected'].extend(bottlenecks)
        
        # Generate optimization actions
        optimization_actions = []
        for bottleneck in optimization_results['bottlenecks_detected']:
            actions = self.optimization_engine.generate_optimization_actions(bottleneck)
            optimization_actions.extend(actions)
        
        # Prioritize and execute optimizations
        prioritized_actions = self._prioritize_optimizations(optimization_actions)
        
        for action in prioritized_actions:
            try:
                result = await self._execute_optimization(action)
                optimization_results['optimizations_applied'].append({
                    'action': action,
                    'result': result,
                    'success': result.get('success', False)
                })
            except Exception as e:
                optimization_results['optimizations_applied'].append({
                    'action': action,
                    'error': str(e),
                    'success': False
                })
        
        # Measure improvements
        optimization_results['performance_improvements'] = await self._measure_improvements(
            interfaces, optimization_results['start_time']
        )
        
        # Generate recommendations for manual review
        optimization_results['recommendations'] = self._generate_recommendations(
            optimization_results['bottlenecks_detected'],
            optimization_results['optimizations_applied']
        )
        
        optimization_results['end_time'] = time.time()
        optimization_results['duration'] = optimization_results['end_time'] - optimization_results['start_time']
        
        return optimization_results
    
    def _prioritize_optimizations(self, actions: List[OptimizationAction]) -> List[OptimizationAction]:
        """Prioritize optimization actions based on impact and risk"""
        return sorted(actions, key=lambda x: (
            -x.expected_improvement,  # Higher improvement first
            self._risk_score(x.risk_level),  # Lower risk first
            x.execution_time  # Faster execution first
        ))
    
    def _risk_score(self, risk_level: str) -> int:
        risk_scores = {'low': 1, 'medium': 2, 'high': 3, 'critical': 4}
        return risk_scores.get(risk_level.lower(), 2)

class BottleneckDetector:
    """Advanced bottleneck detection using machine learning and statistical analysis"""
    
    def __init__(self):
        self.anomaly_models = {}
        self.baseline_metrics = {}
        self.detection_rules = self._load_detection_rules()
        
    async def detect_bottlenecks(self, interface_name: str) -> List[PerformanceBottleneck]:
        """Detect performance bottlenecks for specified interface"""
        bottlenecks = []
        
        # Get current performance metrics
        current_metrics = await self._get_current_metrics(interface_name)
        
        # Statistical analysis
        statistical_bottlenecks = self._detect_statistical_anomalies(
            interface_name, current_metrics
        )
        bottlenecks.extend(statistical_bottlenecks)
        
        # Machine learning-based detection
        ml_bottlenecks = self._detect_ml_anomalies(interface_name, current_metrics)
        bottlenecks.extend(ml_bottlenecks)
        
        # Rule-based detection
        rule_bottlenecks = self._detect_rule_based_bottlenecks(
            interface_name, current_metrics
        )
        bottlenecks.extend(rule_bottlenecks)
        
        # Correlation analysis
        correlation_bottlenecks = self._detect_correlation_bottlenecks(
            interface_name, current_metrics
        )
        bottlenecks.extend(correlation_bottlenecks)
        
        # Remove duplicates and prioritize
        unique_bottlenecks = self._deduplicate_bottlenecks(bottlenecks)
        prioritized_bottlenecks = self._prioritize_bottlenecks(unique_bottlenecks)
        
        return prioritized_bottlenecks
    
    def _detect_statistical_anomalies(self, interface_name: str, 
                                    current_metrics: Dict[str, float]) -> List[PerformanceBottleneck]:
        """Detect anomalies using statistical methods"""
        bottlenecks = []
        baseline = self.baseline_metrics.get(interface_name, {})
        
        for metric_name, current_value in current_metrics.items():
            if metric_name not in baseline:
                continue
                
            baseline_stats = baseline[metric_name]
            
            # Z-score analysis
            z_score = abs((current_value - baseline_stats['mean']) / baseline_stats['std'])
            
            if z_score > 3:  # 3-sigma rule
                bottleneck = PerformanceBottleneck(
                    interface_name=interface_name,
                    bottleneck_type=f"{metric_name}_anomaly",
                    severity=self._calculate_severity(z_score),
                    current_value=current_value,
                    expected_value=baseline_stats['mean'],
                    impact_score=min(z_score / 3 * 100, 100),
                    root_cause=f"Statistical anomaly detected: {z_score:.2f} standard deviations from baseline",
                    recommendations=self._generate_statistical_recommendations(metric_name, z_score),
                    detected_at=time.time()
                )
                bottlenecks.append(bottleneck)
        
        return bottlenecks
    
    def _detect_ml_anomalies(self, interface_name: str,
                           current_metrics: Dict[str, float]) -> List[PerformanceBottleneck]:
        """Detect anomalies using machine learning models"""
        bottlenecks = []
        
        if interface_name not in self.anomaly_models:
            return bottlenecks
        
        model = self.anomaly_models[interface_name]
        
        # Prepare feature vector
        feature_vector = [current_metrics.get(metric, 0) for metric in model.feature_names_]
        feature_vector = np.array(feature_vector).reshape(1, -1)
        
        # Detect anomaly
        anomaly_score = model.decision_function(feature_vector)[0]
        is_anomaly = model.predict(feature_vector)[0] == -1
        
        if is_anomaly:
            # Identify which metrics contribute most to the anomaly
            feature_contributions = self._calculate_feature_contributions(
                model, feature_vector, current_metrics
            )
            
            for metric_name, contribution in feature_contributions.items():
                if contribution > 0.3:  # Significant contribution threshold
                    bottleneck = PerformanceBottleneck(
                        interface_name=interface_name,
                        bottleneck_type=f"{metric_name}_ml_anomaly",
                        severity=self._calculate_ml_severity(anomaly_score),
                        current_value=current_metrics[metric_name],
                        expected_value=self._get_expected_value(interface_name, metric_name),
                        impact_score=abs(anomaly_score * 100),
                        root_cause=f"ML anomaly detected: score {anomaly_score:.3f}, contribution {contribution:.2f}",
                        recommendations=self._generate_ml_recommendations(metric_name, anomaly_score),
                        detected_at=time.time()
                    )
                    bottlenecks.append(bottleneck)
        
        return bottlenecks
    
    def _detect_rule_based_bottlenecks(self, interface_name: str,
                                     current_metrics: Dict[str, float]) -> List[PerformanceBottleneck]:
        """Detect bottlenecks using predefined rules"""
        bottlenecks = []
        
        for rule in self.detection_rules:
            if self._evaluate_rule(rule, current_metrics):
                bottleneck = PerformanceBottleneck(
                    interface_name=interface_name,
                    bottleneck_type=rule['type'],
                    severity=rule['severity'],
                    current_value=current_metrics.get(rule['metric'], 0),
                    expected_value=rule.get('threshold', 0),
                    impact_score=rule.get('impact_score', 50),
                    root_cause=rule['description'],
                    recommendations=rule.get('recommendations', []),
                    detected_at=time.time()
                )
                bottlenecks.append(bottleneck)
        
        return bottlenecks
    
    def _load_detection_rules(self) -> List[Dict[str, Any]]:
        """Load predefined detection rules"""
        return [
            {
                'name': 'high_utilization',
                'type': 'bandwidth_saturation',
                'metric': 'utilization',
                'operator': 'gt',
                'threshold': 85.0,
                'severity': 'high',
                'description': 'Interface utilization exceeds 85%',
                'recommendations': [
                    'Consider link upgrade',
                    'Implement traffic shaping',
                    'Review traffic patterns'
                ],
                'impact_score': 80
            },
            {
                'name': 'high_latency',
                'type': 'latency_degradation',
                'metric': 'latency',
                'operator': 'gt',
                'threshold': 100.0,  # milliseconds
                'severity': 'medium',
                'description': 'Latency exceeds 100ms threshold',
                'recommendations': [
                    'Check for congestion',
                    'Optimize routing',
                    'Review QoS policies'
                ],
                'impact_score': 60
            },
            {
                'name': 'packet_loss',
                'type': 'reliability_issue',
                'metric': 'packet_loss',
                'operator': 'gt',
                'threshold': 0.1,  # percent
                'severity': 'critical',
                'description': 'Packet loss detected',
                'recommendations': [
                    'Check physical connections',
                    'Review buffer sizes',
                    'Analyze error logs'
                ],
                'impact_score': 90
            },
            {
                'name': 'high_jitter',
                'type': 'jitter_variance',
                'metric': 'jitter',
                'operator': 'gt',
                'threshold': 20.0,  # milliseconds
                'severity': 'medium',
                'description': 'Jitter exceeds acceptable variance',
                'recommendations': [
                    'Implement QoS',
                    'Check for traffic bursts',
                    'Review scheduling policies'
                ],
                'impact_score': 50
            }
        ]

class OptimizationEngine:
    """Generate and execute network optimization actions"""
    
    def __init__(self):
        self.optimization_strategies = {
            OptimizationType.BANDWIDTH_ALLOCATION: BandwidthOptimizer(),
            OptimizationType.QOS_TUNING: QoSOptimizer(),
            OptimizationType.BUFFER_SIZING: BufferOptimizer(),
            OptimizationType.CONGESTION_CONTROL: CongestionOptimizer(),
            OptimizationType.ROUTING_OPTIMIZATION: RoutingOptimizer(),
            OptimizationType.LOAD_BALANCING: LoadBalancingOptimizer()
        }
    
    def generate_optimization_actions(self, bottleneck: PerformanceBottleneck) -> List[OptimizationAction]:
        """Generate optimization actions for a detected bottleneck"""
        actions = []
        
        # Map bottleneck types to optimization strategies
        strategy_mapping = {
            'bandwidth_saturation': [OptimizationType.BANDWIDTH_ALLOCATION, OptimizationType.LOAD_BALANCING],
            'latency_degradation': [OptimizationType.QOS_TUNING, OptimizationType.ROUTING_OPTIMIZATION],
            'reliability_issue': [OptimizationType.BUFFER_SIZING, OptimizationType.CONGESTION_CONTROL],
            'jitter_variance': [OptimizationType.QOS_TUNING, OptimizationType.BUFFER_SIZING]
        }
        
        relevant_strategies = strategy_mapping.get(bottleneck.bottleneck_type, [])
        
        for strategy_type in relevant_strategies:
            strategy = self.optimization_strategies[strategy_type]
            strategy_actions = strategy.generate_actions(bottleneck)
            actions.extend(strategy_actions)
        
        return actions

class BandwidthOptimizer:
    """Optimize bandwidth allocation and utilization"""
    
    def generate_actions(self, bottleneck: PerformanceBottleneck) -> List[OptimizationAction]:
        actions = []
        
        if 'saturation' in bottleneck.bottleneck_type:
            # Traffic shaping action
            actions.append(OptimizationAction(
                action_type=OptimizationType.BANDWIDTH_ALLOCATION,
                target_interface=bottleneck.interface_name,
                parameters={
                    'action': 'implement_traffic_shaping',
                    'rate_limit': bottleneck.current_value * 0.9,  # 10% reduction
                    'burst_size': 1500,
                    'queue_algorithm': 'fair_queuing'
                },
                expected_improvement=15.0,
                risk_level='low',
                execution_time=30.0,
                rollback_procedure='remove_traffic_shaping_policy'
            ))
            
            # Compression action
            actions.append(OptimizationAction(
                action_type=OptimizationType.BANDWIDTH_ALLOCATION,
                target_interface=bottleneck.interface_name,
                parameters={
                    'action': 'enable_compression',
                    'compression_type': 'adaptive',
                    'compression_ratio': 2.0
                },
                expected_improvement=25.0,
                risk_level='medium',
                execution_time=60.0,
                rollback_procedure='disable_compression'
            ))
        
        return actions

class QoSOptimizer:
    """Optimize Quality of Service parameters"""
    
    def generate_actions(self, bottleneck: PerformanceBottleneck) -> List[OptimizationAction]:
        actions = []
        
        if 'latency' in bottleneck.bottleneck_type or 'jitter' in bottleneck.bottleneck_type:
            # Priority queuing optimization
            actions.append(OptimizationAction(
                action_type=OptimizationType.QOS_TUNING,
                target_interface=bottleneck.interface_name,
                parameters={
                    'action': 'optimize_queue_priorities',
                    'real_time_priority': 'high',
                    'interactive_priority': 'medium',
                    'bulk_priority': 'low',
                    'queue_weights': [40, 35, 25]
                },
                expected_improvement=20.0,
                risk_level='low',
                execution_time=45.0,
                rollback_procedure='restore_default_qos'
            ))
            
            # Bandwidth guarantee optimization
            actions.append(OptimizationAction(
                action_type=OptimizationType.QOS_TUNING,
                target_interface=bottleneck.interface_name,
                parameters={
                    'action': 'set_bandwidth_guarantees',
                    'voice_guarantee': '10%',
                    'video_guarantee': '20%',
                    'data_guarantee': '70%'
                },
                expected_improvement=18.0,
                risk_level='medium',
                execution_time=30.0,
                rollback_procedure='remove_bandwidth_guarantees'
            ))
        
        return actions

class BufferOptimizer:
    """Optimize buffer sizes and queue management"""
    
    def generate_actions(self, bottleneck: PerformanceBottleneck) -> List[OptimizationAction]:
        actions = []
        
        if 'reliability' in bottleneck.bottleneck_type or 'jitter' in bottleneck.bottleneck_type:
            # Buffer size optimization
            current_buffer_size = self._get_current_buffer_size(bottleneck.interface_name)
            optimal_buffer_size = self._calculate_optimal_buffer_size(
                bottleneck.interface_name, bottleneck.current_value
            )
            
            actions.append(OptimizationAction(
                action_type=OptimizationType.BUFFER_SIZING,
                target_interface=bottleneck.interface_name,
                parameters={
                    'action': 'optimize_buffer_size',
                    'current_size': current_buffer_size,
                    'target_size': optimal_buffer_size,
                    'queue_algorithm': 'random_early_detection'
                },
                expected_improvement=12.0,
                risk_level='low',
                execution_time=20.0,
                rollback_procedure=f'restore_buffer_size_{current_buffer_size}'
            ))
        
        return actions
    
    def _calculate_optimal_buffer_size(self, interface_name: str, current_value: float) -> int:
        """Calculate optimal buffer size based on current conditions"""
        # Simplified calculation - in practice, this would be more sophisticated
        bandwidth_delay_product = self._get_bandwidth_delay_product(interface_name)
        return int(bandwidth_delay_product * 1.5)  # 1.5x BDP for optimal performance

class PerformanceAnalytics:
    """Advanced analytics for network performance optimization"""
    
    def __init__(self):
        self.metrics_database = MetricsDatabase()
        self.trend_analyzer = TrendAnalyzer()
        self.correlation_engine = CorrelationEngine()
        self.predictive_model = PredictiveModel()
    
    def analyze_performance_trends(self, interface_name: str, 
                                 time_window: int = 86400) -> Dict[str, Any]:
        """Analyze performance trends over specified time window"""
        metrics_data = self.metrics_database.get_metrics(interface_name, time_window)
        
        analysis = {
            'trend_analysis': {},
            'seasonal_patterns': {},
            'anomaly_periods': [],
            'correlation_insights': {},
            'predictive_forecast': {}
        }
        
        # Trend analysis
        for metric_name in ['latency', 'throughput', 'utilization', 'packet_loss']:
            if metric_name in metrics_data:
                trend_data = self.trend_analyzer.analyze_trend(
                    metrics_data[metric_name]
                )
                analysis['trend_analysis'][metric_name] = trend_data
        
        # Seasonal pattern detection
        analysis['seasonal_patterns'] = self.trend_analyzer.detect_seasonal_patterns(
            metrics_data
        )
        
        # Anomaly period identification
        analysis['anomaly_periods'] = self.trend_analyzer.identify_anomaly_periods(
            metrics_data
        )
        
        # Correlation analysis
        analysis['correlation_insights'] = self.correlation_engine.analyze_correlations(
            metrics_data
        )
        
        # Predictive forecasting
        analysis['predictive_forecast'] = self.predictive_model.generate_forecast(
            metrics_data, forecast_horizon=24  # 24 hours
        )
        
        return analysis
    
    def generate_performance_report(self, interfaces: List[str]) -> Dict[str, Any]:
        """Generate comprehensive performance report"""
        report = {
            'report_id': self._generate_report_id(),
            'generated_at': time.time(),
            'interfaces': interfaces,
            'executive_summary': {},
            'detailed_analysis': {},
            'recommendations': [],
            'action_items': []
        }
        
        # Collect data for all interfaces
        all_metrics = {}
        for interface in interfaces:
            all_metrics[interface] = self.analyze_performance_trends(interface)
        
        # Generate executive summary
        report['executive_summary'] = self._generate_executive_summary(all_metrics)
        
        # Detailed analysis per interface
        report['detailed_analysis'] = all_metrics
        
        # Generate recommendations
        report['recommendations'] = self._generate_performance_recommendations(all_metrics)
        
        # Create action items
        report['action_items'] = self._generate_action_items(all_metrics)
        
        return report

class AutomatedOptimizationOrchestrator:
    """Orchestrate automated network optimization"""
    
    def __init__(self):
        self.optimizer = NetworkPerformanceOptimizer()
        self.scheduler = OptimizationScheduler()
        self.approval_engine = ApprovalEngine()
        self.rollback_manager = RollbackManager()
        
    async def run_automated_optimization(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Run automated optimization with approval gates"""
        optimization_session = {
            'session_id': self._generate_session_id(),
            'start_time': time.time(),
            'config': config,
            'phases': [],
            'overall_result': 'pending'
        }
        
        try:
            # Phase 1: Analysis and Detection
            analysis_phase = await self._run_analysis_phase(config)
            optimization_session['phases'].append(analysis_phase)
            
            # Phase 2: Action Generation
            action_phase = await self._run_action_generation_phase(analysis_phase)
            optimization_session['phases'].append(action_phase)
            
            # Phase 3: Approval Process
            approval_phase = await self._run_approval_phase(action_phase)
            optimization_session['phases'].append(approval_phase)
            
            # Phase 4: Execution
            if approval_phase['approved']:
                execution_phase = await self._run_execution_phase(approval_phase)
                optimization_session['phases'].append(execution_phase)
                
                # Phase 5: Validation
                validation_phase = await self._run_validation_phase(execution_phase)
                optimization_session['phases'].append(validation_phase)
                
                optimization_session['overall_result'] = 'completed'
            else:
                optimization_session['overall_result'] = 'cancelled'
                
        except Exception as e:
            optimization_session['overall_result'] = 'failed'
            optimization_session['error'] = str(e)
            
            # Attempt rollback
            await self.rollback_manager.emergency_rollback(optimization_session)
        
        optimization_session['end_time'] = time.time()
        optimization_session['duration'] = optimization_session['end_time'] - optimization_session['start_time']
        
        return optimization_session