# Example: Prophet-based VPS traffic prediction
from prophet import Prophet
import pandas as pd

# Load historical traffic data (collected every 5 minutes)
df = pd.read_csv("/var/log/vps/traffic_hourly.csv")
df.columns = ["ds", "y"]  # Prophet requires columns named ds (date) and y (value)

# Train the model
model = Prophet(
    yearly_seasonality=True,   # Yearly seasonality
    weekly_seasonality=True,   # Weekly seasonality
    daily_seasonality=True,    # Daily seasonality
    changepoint_prior_scale=0.1  # Sensitivity to traffic spikes
)
model.fit(df)

# Forecast the next 24 hours
future = model.make_future_dataframe(periods=24, freq="H")
forecast = model.predict(future)

# Output prediction results
print(f"Predicted peak traffic: {forecast['yhat'].max():.2f} Gbps")
print(f"Predicted trough traffic: {forecast['yhat'].min():.2f} Gbps")
print(f"Predicted volatility: {(forecast['yhat'].max() - forecast['yhat'].min()) / forecast['yhat'].mean():.2%}")

# nginx_upstream_dynamic.yaml — AI-generated dynamic load balancing config
upstream backend_pool {
    # Weights adjusted dynamically based on traffic prediction
    server 10.0.1.10:8080 weight=15;   # Low负载 instance, weight increased
    server 10.0.1.11:8080 weight=8;    # High负载 instance, weight reduced
    server 10.0.1.12:8080 weight=12;   # Normal负载
    server 10.0.1.13:8080 weight=5;    # Edge node, handles burst traffic only
}

# AI auto-adds warm-up instances based on prediction
# Forecast: traffic will increase 300% in next 2 hours, auto-scale
server 10.0.1.14:8080 weight=10 backup;  # Pre-warmed standby instance

#!/bin/bash
# ai_traffic_scheduler.sh — AI-driven dynamic traffic scheduling script

PREDICTION_ENDPOINT="http://localhost:8000/api/traffic/predict"
NGINX_CONF="/etc/nginx/conf.d/upstream_dynamic.yaml"
LOG_FILE="/var/log/vps/ai_scheduler.log"

# Get AI traffic prediction
FORECAST=$(curl -s "${PREDICTION_ENDPOINT}?hours=2")
PEAK_TRAFFIC=$(echo "$FORECAST" | jq '.peak_gbps')
CURRENT_TRAFFIC=$(echo "$FORECAST" | jq '.current_gbps')

# Calculate traffic growth rate
GROWTH_RATE=$(echo "scale=2; ($PEAK_TRAFFIC - $CURRENT_TRAFFIC) / $CURRENT_TRAFFIC * 100" | bc)

echo "[$(date)] Current traffic: ${CURRENT_TRAFFIC} Gbps, Predicted peak: ${PEAK_TRAFFIC} Gbps, Growth rate: ${GROWTH_RATE}%" | tee -a "$LOG_FILE"

# Dynamically adjust instance count based on growth rate
if (( $(echo "$GROWTH_RATE > 50" | bc -l) )); then
    echo "Traffic surge 50%+, auto-scaling standby instances" | tee -a "$LOG_FILE"
    sed -i 's/server 10.0.1.14:8080 weight=10 backup;/server 10.0.1.14:8080 weight=10;/' "$NGINX_CONF"
    nginx -s reload
elif (( $(echo "$GROWTH_RATE < -30" | bc -l) )); then
    echo "Traffic dropped 30%+, shrinking instances to save resources" | tee -a "$LOG_FILE"
    sed -i 's/server 10.0.1.14:8080 weight=10;/server 10.0.1.14:8080 weight=10 backup;/g' "$NGINX_CONF"
    nginx -s reload
fi

# bandwidth_cost_optimizer.py — AI bandwidth cost optimizer
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

class BandwidthCostOptimizer:
    def __init__(self, traffic_data_path):
        self.df = pd.read_csv(traffic_data_path, parse_dates=["timestamp"])
        self.df.set_index("timestamp", inplace=True)
        self.df.sort_index(inplace=True)
        
        # Pricing models (example: DigitalOcean / Vultr / AWS)
        self.pricing = {
            "fixed_100mbps": {"base": 20, "overage": 0},       # $20/month fixed 100Mbps
            "pay_per_gb": {"base": 0, "overage": 0.05},        # $0.05/GB
            "95th_percentile": {"base": 0, "overage": 0.04},   # $0.04/GB (95th percentile)
            "hybrid": {"base": 10, "overage": 0.03},           # $10 base + $0.03/GB overage
        }
    
    def calculate_cost(self, traffic_gb, mode):
        """Calculate monthly cost under a given billing model"""
        config = self.pricing[mode]
        return config["base"] + traffic_gb * config["overage"]
    
    def analyze(self):
        """Analyze 30 days of traffic data, recommend optimal billing model"""
        hourly = self.df["bytes"].resample("1H").sum()
        total_gb = hourly.sum() / (1024 ** 3)
        
        results = {}
        for mode in self.pricing:
            cost = self.calculate_cost(total_gb, mode)
            
            if mode == "95th_percentile":
                sorted_hours = hourly.dropna().sort_values(ascending=False)
                p95_index = int(len(sorted_hours) * 0.05)
                p95_gbps = sorted_hours.iloc[p95_index] / (1024 ** 3) * 8
                base_bandwidth = max(1, int(np.ceil(p95_gbps)))
                cost = base_bandwidth * 10 + max(0, total_gb - base_bandwidth * 730 * 0.1) * 0.04
            
            results[mode] = {
                "monthly_cost": round(cost, 2),
                "total_traffic_gb": round(total_gb, 2),
            }
        
        sorted_results = sorted(results.items(), key=lambda x: x[1]["monthly_cost"])
        
        return {
            "analysis_date": datetime.now().strftime("%Y-%m-%d"),
            "total_traffic_gb": round(total_gb, 2),
            "recommendation": sorted_results[0][0],
            "savings_vs_current": f"{((results['pay_per_gb']['monthly_cost'] - sorted_results[0][1]['monthly_cost']) / results['pay_per_gb']['monthly_cost'] * 100):.1f}%",
            "all_modes": results,
        }

def auto_switch_billing(current_mode, analysis_result):
    recommended = analysis_result["recommendation"]
    
    if recommended != current_mode:
        if can_switch_now(current_mode, recommended):
            switch_cost = get_switch_cost(current_mode, recommended)
            monthly_saving = analysis_result["all_modes"][recommended]["monthly_cost"] - \
                           analysis_result["all_modes"][current_mode]["monthly_cost"]
            
            if monthly_saving < 0 and switch_cost > abs(monthly_saving) * 3:
                return False, "Switch cost too high, recommend keeping current mode"
            
            execute_billing_switch(current_mode, recommended)
            return True, f"Switched to {recommended}, estimated monthly savings ${abs(monthly_saving)}"
    
    return False, "Current mode is already optimal"

# network_diagnosis_engine.py — AI network故障 diagnosis
import subprocess
import re
from datetime import datetime

class NetworkDiagnosisEngine:
    def __init__(self):
        self.check_interval = 60
        self.fault_library = self.load_fault_library()
    
    def load_fault_library(self):
        return {
            "dns_failure": {
                "symptoms": ["named: resolution time", "getaddrinfo: temporary failure", 
                             "nslookup timeout", "dig SERVFAIL"],
                "diagnosis": self._check_dns,
                "fix": self._fix_dns,
                "severity": "high",
                "auto_fix": True,
            },
            "route_blackhole": {
                "symptoms": ["Destination Host Unreachable", "No route to host",
                             "netstat: 0 active connections"],
                "diagnosis": self._check_routes,
                "fix": self._fix_routes,
                "severity": "critical",
                "auto_fix": True,
            },
            "port_blocked": {
                "symptoms": ["Connection timed out", "ECONNREFUSED", 
                             "nc: connect timed out"],
                "diagnosis": self._check_ports,
                "fix": self._fix_ports,
                "severity": "medium",
                "auto_fix": True,
            },
            "ssh_brute_force": {
                "symptoms": ["Failed password for root", "Invalid user",
                             "Too many authentication failures"],
                "diagnosis": self._check_ssh_logs,
                "fix": self._fix_ssh,
                "severity": "high",
                "auto_fix": True,
            },
            "ssl_expired": {
                "symptoms": ["certificate has expired", "SSL handshake failed",
                             "ERR_CERT_DATE_INVALID"],
                "diagnosis": self._check_ssl,
                "fix": self._fix_ssl,
                "severity": "critical",
                "auto_fix": True,
            },
        }
    
    def diagnose(self):
        """Run full network diagnosis"""
        results = []
        for fault_type, config in self.fault_library.items():
            is_fault, detail = config["diagnosis"]()
            if is_fault:
                result = {
                    "type": fault_type,
                    "severity": config["severity"],
                    "detail": detail,
                    "auto_fix": config["auto_fix"],
                    "fix_command": config["fix"](),
                    "detected_at": datetime.now().isoformat(),
                }
                results.append(result)
        return results
    
    def _check_dns(self):
        try:
            result = subprocess.run(
                ["dig", "+short", "+time=3", "+tries=1", "google.com"],
                capture_output=True, text=True, timeout=10
            )
            if result.returncode != 0 or not result.stdout.strip():
                return True, f"DNS resolution failed: {result.stderr}"
            return False, ""
        except subprocess.TimeoutExpired:
            return True, "DNS query timed out"
    
    def _fix_dns(self):
        return "echo 'nameserver 1.1.1.1' > /etc/resolv.conf && systemctl restart systemd-resolved"
    
    def _check_ssl(self):
        try:
            result = subprocess.run(
                ["openssl", "x509", "-enddate", "-noout", "-in", "/etc/ssl/certs/server.crt"],
                capture_output=True, text=True
            )
            end_date_str = result.stdout.split("=")[1]
            end_date = datetime.strptime(end_date_str.strip(), "%b %d %H:%M:%S %Y %Z")
            days_left = (end_date - datetime.now()).days
            
            if days_left < 0:
                return True, "SSL certificate has expired!"
            elif days_left < 7:
                return True, f"SSL certificate expires in {days_left} days"
            return False, f"SSL certificate valid, {days_left} days remaining"
        except FileNotFoundError:
            return True, "SSL certificate not found, attempting auto-renewal"
    
    def _fix_ssl(self):
        return "certbot renew --force-renewal && systemctl reload nginx"

# ai_cdn_orchestrator.py — AI CDN strategy engine
class AICDNOrchestrator:
    def __init__(self):
        self.traffic_analyzer = TrafficPatternAnalyzer()
        self.cost_model = CDNCostModel()
    
    def generate_cdn_rules(self, traffic_data):
        """Generate CDN rules based on traffic analysis"""
        patterns = self.traffic_analyzer.analyze(traffic_data)
        rules = []
        
        for path_pattern, pattern_info in patterns.items():
            if pattern_info["content_type"] in ["image", "css", "js", "font"]:
                ttl = self._decide_static_ttl(pattern_info)
                rules.append({
                    "match": path_pattern,
                    "action": "cache",
                    "ttl": ttl,
                    "reason": f"Static asset, hit rate {pattern_info['hit_rate']:.1%}"
                })
            
            elif "/api/" in path_pattern or pattern_info["is_dynamic"]:
                rules.append({
                    "match": path_pattern,
                    "action": "no_cache" if pattern_info["hit_rate"] < 0.1 else "short_cache",
                    "ttl": "0" if pattern_info["hit_rate"] < 0.1 else "60",
                    "reason": f"Dynamic content, hit rate only {pattern_info['hit_rate']:.1%}"
                })
            
            elif pattern_info["hit_rate"] > 0.7:
                rules.append({
                    "match": path_pattern,
                    "action": "cache",
                    "ttl": "1h",
                    "reason": f"High hit rate ({pattern_info['hit_rate']:.1%}), significant cache benefit"
                })
            
            else:
                rules.append({
                    "match": path_pattern,
                    "action": "short_cache",
                    "ttl": "300",
                    "reason": "Default short-cache strategy"
                })
        
        return rules

# Nginx + CDN config auto-generated by AI
# Based on historical traffic analysis, optimal cache strategy

# 1. Static assets — long-term cache (7-365 days)
location ~* \.(jpg|jpeg|png|gif|ico|css|js|svg|woff2?|ttf|eot)$ {
    proxy_pass http://origin;
    proxy_cache valid_cache;
    proxy_cache_valid 200 365d;
    add_header Cache-Control "public, max-age=31536000, immutable";
    add_header X-Cache-Strategy "AI-Optimized: static-7d-min";
}

# 2. HTML pages — short cache (1 hour)
location ~* \.html$ {
    proxy_pass http://origin;
    proxy_cache valid_cache;
    proxy_cache_valid 200 1h;
    add_header Cache-Control "public, max-age=3600";
    add_header X-Cache-Strategy "AI-Optimized: html-1h";
}

# 3. API requests — no cache (dynamic content)
location /api/ {
    proxy_pass http://origin;
    proxy_no_cache 1;
    proxy_cache_bypass 1;
    add_header Cache-Control "no-store, no-cache, must-revalidate";
    add_header X-Cache-Strategy "AI-Optimized: api-no-cache";
}

# 4. High-hit dynamic paths — short cache (60 seconds)
location ~* ^/api/v[12]/public/ {
    proxy_pass http://origin;
    proxy_cache valid_cache;
    proxy_cache_valid 200 60s;
    add_header Cache-Control "public, max-age=60";
    add_header X-Cache-Strategy "AI-Optimized: high-hit-short-cache";
}

# ai_ddos_detector.py — AI DDoS intelligent detection
import numpy as np
from sklearn.ensemble import IsolationForest
from collections import defaultdict, deque

class AIDDosDetector:
    def __init__(self, window_size=300):
        self.window_size = window_size
        self.connection_history = defaultdict(lambda: deque(maxlen=window_size))
        self.detector = IsolationForest(contamination=0.05, random_state=42)
        self.threat_level = "normal"
        
        self.attack_signatures = {
            "syn_flood": {"tcp_syn_per_sec": 1000, "connection_rate": 500},
            "http_flood": {"requests_per_sec": 500, "same_path_ratio": 0.8},
            "dns_amplification": {"dns_queries_per_sec": 200, "response_ratio": 50},
            "slowloris": {"slow_connections": 100, "connection_duration_avg": 300},
        }
    
    def analyze_traffic(self, current_metrics):
        """Analyze current traffic metrics, detect DDoS attacks"""
        features = self._extract_features(current_metrics)
        prediction = self.detector.predict([features])
        anomaly_score = self.detector.score_samples([features])[0]
        rule_violations = self._check_rules(current_metrics)
        threat_score = self._calculate_threat_score(anomaly_score, rule_violations, current_metrics)
        
        return {
            "is_attack": threat_score > 0.7,
            "threat_level": self._map_threat_level(threat_score),
            "attack_type": self._identify_attack_type(current_metrics, rule_violations),
            "threat_score": threat_score,
            "recommended_action": self._get_recommended_action(threat_score),
        }
    
    def _get_recommended_action(self, threat_score):
        if threat_score > 0.9:
            return {
                "action": "block_and_alert",
                "steps": [
                    "Enable DDoS protection mode immediately",
                    "Block anomalous IP ranges",
                    "Activate CDN scrubbing center",
                    "Notify security team",
                ],
                "automation_level": "full_auto",
            }
        elif threat_score > 0.7:
            return {
                "action": "throttle_and_monitor",
                "steps": [
                    "Rate-limit anomalous IPs",
                    "Enable request frequency limits",
                    "Enhance logging",
                    "Prepare for manual intervention",
                ],
                "automation_level": "auto_with_confirmation",
            }
        return {"action": "normal", "steps": [], "automation_level": "none"}

# 1. Install dependencies
pip install prophet scikit-learn pandas numpy

# 2. Deploy AI Agent
git clone https://github.com/your-org/ai-network-agent.git
cd ai-network-agent
docker-compose up -d

# 3. Configure monitoring data sources
# Ensure Prometheus collects these metrics:
# - nginx_request_rate
# - nginx_bytes_transferred
# - nginx_active_connections
# - system_network_packets
# - system_network_errors

# 4. Configure notification channels
# Edit config.yaml:
# notification:
#   slack:
#     webhook_url: "https://hooks.slack.com/..."
#   email:
#     smtp_host: "smtp.gmail.com"
#     recipient: "admin@yourdomain.com"

# 5. Start the AI network agent
systemctl start ai-network-agent
systemctl enable ai-network-agent

# 6. View diagnosis reports
curl http://localhost:8080/api/diagnosis/latest

def update_model_with_feedback(old_prediction, actual_value, model):
    """Update prediction model with actual data"""
    error = abs(actual_value - old_prediction) / old_prediction
    
    if error > 0.2:
        recent_data = load_recent_traffic_data(hours=24)
        model.retrain(recent_data)
        log_model_update("Retraining complete, error threshold 20%")
    elif error > 0.1:
        model.adjust_parameters(error=error)
        log_model_update("Parameter adjustment complete, error threshold 10%")
    else:
        log_model_update("Prediction accurate, no adjustment needed")