# 智能弹性伸缩伪代码示例
class AIPredictiveScaler:
    def __init__(self, prediction_model, cost_optimizer):
        self.predictor = prediction_model
        self.optimizer = cost_optimizer
    
    def daily_optimize(self):
        # 1. 预测未来24小时流量
        forecast = self.predictor.forecast(hours=24)
        
        # 2. 分析当前资源利用率
        current_metrics = self.collect_metrics()
        
        # 3. 计算最优资源配置
        optimal_config = self.optimizer.find_best_config(
            forecast=forecast,
            current=current_metrics,
            budget_constraint=monthly_budget,
            sla_requirements=response_time_p99 < 200ms
        )
        
        # 4. 评估变更风险
        risk_score = self.assess_change_risk(optimal_config)
        
        if risk_score < RISK_THRESHOLD:
            # 5. 安全执行配置变更
            self.apply_scaling(optimal_config)
            log(f"资源配置已优化: {optimal_config}")
        else:
            # 高风险变更，发送人工审批请求
            alert(f"需要人工审批的配置变更: {optimal_config}")

# 使用 Prometheus + Grafana 采集核心指标
# 1. 安装 Node Exporter 收集系统指标
docker run -d \
  --name node-exporter \
  --net=host \
  --pid=host \
  -v "/:/host:ro,rslave" \
  prom/node-exporter:latest \
  --path.rootfs=/host

# 2. 安装 cAdvisor 收集容器指标
docker run -d \
  --name=cadvisor \
  --volume=/:/rootfs:ro \
  --volume=/var/run:/var/run:ro \
  --volume=/sys:/sys:ro \
  --volume=/var/lib/docker/:/var/lib/docker:ro \
  --volume=/dev/disk/:/dev/disk:ro \
  --publish=8080:8080 \
  --detach=true \
  --privileged \
  --device=/dev/kmsg \
  gcr.io/cadvisor/cadvisor:latest

# 3. 安装 Prometheus
cat > prometheus.yml << 'EOF'
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'node-exporter'
    static_configs:
      - targets: ['localhost:9100']
  
  - job_name: 'cadvisor'
    static_configs:
      - targets: ['localhost:8080']
  
  - job_name: 'vps-billing'
    static_configs:
      - targets: ['localhost:9090']
EOF

docker run -d \
  --name=prometheus \
  -p 9090:9090 \
  -v $(pwd)/prometheus.yml:/etc/prometheus/prometheus.yml \
  prom/prometheus:latest

# 创建预测服务目录
mkdir -p ~/ai-cost-optimizer && cd ~/ai-cost-optimizer

# 创建虚拟环境
python3 -m venv venv
source venv/bin/activate

# 安装依赖
pip install prophet pandas numpy flask requests

# 创建预测服务
cat > predictor.py << 'PYEOF'
#!/usr/bin/env python3
"""
AI VPS Cost Optimizer - Traffic Prediction Service
基于Prophet的流量预测引擎
"""

import json
import os
import logging
from datetime import datetime, timedelta
from typing import Dict, List, Optional

import numpy as np
import pandas as pd
from prophet import Prophet

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class TrafficPredictor:
    """流量预测器 - 基于Prophet的时间序列预测"""
    
    def __init__(self, forecast_days: int = 30, confidence_level: float = 0.9):
        self.forecast_days = forecast_days
        self.confidence_level = confidence_level
        self.models = {}  # 缓存不同指标的预测模型
        
    def train_model(self, metric_name: str, historical_data: List[Dict]) -> Prophet:
        """训练单个指标的预测模型"""
        df = pd.DataFrame(historical_data)
        df['ds'] = pd.to_datetime(df['timestamp'])
        df['y'] = df['value']
        
        model = Prophet(
            interval_width=self.confidence_level,
            yearly_seasonality=True,
            weekly_seasonality=True,
            daily_seasonality=False,
            changepoint_prior_scale=0.05,
        )
        model.fit(df)
        self.models[metric_name] = model
        logger.info(f"模型训练完成: {metric_name}, 数据点: {len(df)}")
        return model
    
    def predict(self, metric_name: str, hours_ahead: int = 72) -> Dict:
        """预测未来N小时的指标值"""
        if metric_name not in self.models:
            raise ValueError(f"模型 '{metric_name}' 尚未训练")
        
        model = self.models[metric_name]
        future = model.make_future_dataframe(periods=hours_ahead, freq='h')
        forecast = model.predict(future)
        
        # 取最后hours_ahead条记录
        prediction = forecast.tail(hours_ahead)
        
        return {
            'metric': metric_name,
            'forecast_hours': hours_ahead,
            'predictions': prediction[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].to_dict('records'),
            'summary': {
                'mean_demand': float(prediction['yhat'].mean()),
                'max_demand': float(prediction['yhat'].max()),
                'min_demand': float(prediction['yhat'].min()),
                'peak_hour': str(prediction.loc[prediction['yhat'].idxmax(), 'ds']),
            }
        }
    
    def get_resource_recommendation(self, cpu_forecast: Dict, 
                                     memory_forecast: Dict,
                                     bandwidth_forecast: Dict) -> Dict:
        """根据预测结果给出资源配置建议"""
        cpu_peak = cpu_forecast['summary']['max_demand']
        mem_peak = memory_forecast['summary']['max_demand']
        bw_peak = bandwidth_forecast['summary']['max_demand']
        
        # 预留30%安全余量
        recommended_cpu = int(np.ceil(cpu_peak * 1.3))
        recommended_memory_gb = int(np.ceil(mem_peak * 1.3 / 1024))
        recommended_bandwidth_mbps = int(np.ceil(bw_peak * 1.3 / (1024 * 1024)))
        
        # 确保最低配置
        recommended_cpu = max(recommended_cpu, 1)
        recommended_memory_gb = max(recommended_memory_gb, 1)
        recommended_bandwidth_mbps = max(recommended_bandwidth_mbps, 1)
        
        return {
            'recommended_config': {
                'cpu_cores': recommended_cpu,
                'memory_gb': recommended_memory_gb,
                'bandwidth_mbps': recommended_bandwidth_mbps,
            },
            'confidence': 'high',
            'safety_margin': '30%',
            'note': f'基于峰值预测: CPU={cpu_peak:.2f}%, MEM={mem_peak:.2f}MB, BW={bw_peak:.2f}bps'
        }


# 简化的Flask API服务
from flask import Flask, request, jsonify

app = Flask(__name__)
predictor = TrafficPredictor(forecast_days=30)


@app.route('/api/v1/train', methods=['POST'])
def train():
    """训练预测模型"""
    data = request.json
    metric_name = data.get('metric')
    historical_data = data.get('data', [])
    
    if not metric_name or not historical_data:
        return jsonify({'error': '缺少必要参数'}), 400
    
    model = predictor.train_model(metric_name, historical_data)
    return jsonify({
        'status': 'success',
        'metric': metric_name,
        'data_points': len(historical_data),
        'model_available': True
    })


@app.route('/api/v1/predict/<metric_name>', methods=['GET'])
def predict(metric_name):
    """获取预测结果"""
    hours = int(request.args.get('hours', 72))
    try:
        result = predictor.predict(metric_name, hours)
        return jsonify(result)
    except ValueError as e:
        return jsonify({'error': str(e)}), 404


@app.route('/api/v1/recommend', methods=['GET'])
def recommend():
    """获取资源配置建议"""
    try:
        cpu_pred = predictor.predict('cpu_usage', 24)
        mem_pred = predictor.predict('memory_mb', 24)
        bw_pred = predictor.predict('bandwidth_bps', 24)
        
        rec = predictor.get_resource_recommendation(cpu_pred, mem_pred, bw_pred)
        return jsonify(rec)
    except Exception as e:
        return jsonify({'error': str(e)}), 500


if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=False)
PYEOF

echo "✅ 预测服务已创建"

cat > optimizer.py << 'PYEOF'
#!/usr/bin/env python3
"""
AI VPS Cost Optimizer - Decision Engine
基于AI预测的成本优化决策引擎
"""

import json
import math
from datetime import datetime, timedelta
from typing import Dict, List, Optional
from dataclasses import dataclass, asdict


@dataclass
class InstanceConfig:
    """实例配置"""
    name: str
    cpu_cores: int
    memory_gb: int
    storage_gb: int
    bandwidth_mbps: int
    monthly_cost: float
    provider: str
    region: str


@dataclass
class OptimizationResult:
    """优化结果"""
    timestamp: str
    current_monthly_cost: float
    optimized_monthly_cost: float
    savings_amount: float
    savings_percentage: float
    recommended_actions: List[Dict]
    risk_level: str  # low, medium, high
    confidence: float


class CostOptimizer:
    """成本优化决策引擎"""
    
    def __init__(self, monthly_budget: float = None):
        self.monthly_budget = monthly_budget
        self.instance_catalog = self._load_instance_catalog()
    
    def _load_instance_catalog(self) -> List[InstanceConfig]:
        """加载可用实例目录（实际使用时应从API获取）"""
        return [
            InstanceConfig("s1.small", 1, 1, 20, 1, 45.0, "aliyun", "cn-hangzhou"),
            InstanceConfig("s1.medium", 2, 2, 40, 3, 90.0, "aliyun", "cn-hangzhou"),
            InstanceConfig("s2.large", 2, 4, 80, 5, 168.0, "aliyun", "cn-hangzhou"),
            InstanceConfig("s3.xlarge", 4, 8, 160, 10, 320.0, "aliyun", "cn-hangzhou"),
            InstanceConfig("s3.2xlarge", 4, 16, 320, 20, 580.0, "aliyun", "cn-hangzhou"),
            InstanceConfig("s4.4xlarge", 8, 32, 500, 50, 1080.0, "aliyun", "cn-hangzhou"),
            InstanceConfig("c7.large", 2, 4, 50, 5, 150.0, "custom", "any"),
            InstanceConfig("c7.xlarge", 4, 8, 100, 10, 280.0, "custom", "any"),
        ]
    
    def find_optimal_instances(self, traffic_forecast: Dict, 
                                sla_requirements: Dict) -> List[InstanceConfig]:
        """
        根据流量预测和SLA要求，找到最优实例组合
        
        策略：
        1. 将一天分为多个时段（如每4小时一个时段）
        2. 为每个时段找到满足需求的最低成本实例
        3. 合并相邻时段的相同配置，减少切换次数
        """
        predictions = traffic_forecast.get('predictions', [])
        if not predictions:
            return []
        
        # 按4小时分组
        time_slots = self._group_into_slots(predictions)
        
        optimal_configs = []
        for slot in time_slots:
            avg_cpu = sum(p['yhat'] for p in slot) / len(slot)
            peak_cpu = max(p['yhat'] for p in slot)
            
            # 根据CPU需求选择实例
            config = self._select_best_instance(avg_cpu, peak_cpu, sla_requirements)
            if config:
                optimal_configs.append({
                    'slot': slot[0]['ds'],
                    'avg_cpu': avg_cpu,
                    'peak_cpu': peak_cpu,
                    'config': asdict(config),
                    'cost_per_slot': config.monthly_cost / 6  # 每月约6个4小时slot
                })
        
        return optimal_configs
    
    def _group_into_slots(self, predictions: List[Dict], 
                          slot_hours: int = 4) -> List[List[Dict]]:
        """将预测数据按时间槽分组"""
        slots = []
        current_slot = []
        
        for pred in predictions:
            current_slot.append(pred)
            if len(current_slot) >= slot_hours:
                slots.append(current_slot)
                current_slot = []
        
        if current_slot:
            slots.append(current_slot)
        
        return slots
    
    def _select_best_instance(self, avg_cpu: float, peak_cpu: float,
                               sla: Dict) -> Optional[InstanceConfig]:
        """选择满足需求的最低成本实例"""
        # 计算所需的最小资源
        required_cpu_cores = max(1, math.ceil(avg_cpu / 70))  # 单核建议不超过70%
        safety_factor = sla.get('safety_margin', 1.3)
        
        candidates = []
        for instance in self.instance_catalog:
            # 检查是否满足需求
            if instance.cpu_cores >= required_cpu_cores:
                # 计算性价比得分
                cost_per_core = instance.monthly_cost / instance.cpu_cores
                cost_per_mem = instance.monthly_cost / instance.memory_gb
                
                # 综合评分（越低越好）
                score = cost_per_core * 0.6 + cost_per_mem * 0.4
                candidates.append((score, instance))
        
        if candidates:
            candidates.sort(key=lambda x: x[0])
            return candidates[0][1]
        
        return None
    
    def generate_optimization_report(self, current_cost: float,
                                      forecast: Dict,
                                      sla: Dict) -> OptimizationResult:
        """生成完整的优化报告"""
        optimal_configs = self.find_optimal_instances(forecast, sla)
        
        if not optimal_configs:
            return OptimizationResult(
                timestamp=datetime.now().isoformat(),
                current_monthly_cost=current_cost,
                optimized_monthly_cost=current_cost,
                savings_amount=0,
                savings_percentage=0,
                recommended_actions=[],
                risk_level='high',
                confidence=0.0
            )
        
        # 计算优化后成本
        optimized_cost = sum(c['cost_per_slot'] for c in optimal_configs)
        
        # 生成建议行动
        actions = self._generate_actions(optimal_configs, current_cost, optimized_cost)
        
        savings_pct = ((current_cost - optimized_cost) / current_cost) * 100 if current_cost > 0 else 0
        
        return OptimizationResult(
            timestamp=datetime.now().isoformat(),
            current_monthly_cost=current_cost,
            optimized_monthly_cost=optimized_cost,
            savings_amount=max(0, current_cost - optimized_cost),
            savings_percentage=max(0, savings_pct),
            recommended_actions=actions,
            risk_level='medium' if len(actions) > 3 else 'low',
            confidence=0.85
        )
    
    def _generate_actions(self, configs: List[Dict], 
                           current_cost: float,
                           optimized_cost: float) -> List[Dict]:
        """生成具体的优化建议行动"""
        actions = []
        
        for i, config in enumerate(configs):
            actions.append({
                'type': 'scale_down' if config['config']['monthly_cost'] < current_cost / len(configs) else 'maintain',
                'time_window': str(config['slot']),
                'current_estimate': f"¥{current_cost / len(configs):.0f}/月",
                'recommended': f"{config['config']['name']} (¥{config['config']['monthly_cost']:.0f}/月)",
                'action': 'resize_instance' if config['config']['monthly_cost'] < current_cost / len(configs) else 'no_action'
            })
        
        # 添加总体建议
        total_savings = current_cost - optimized_cost
        if total_savings > 0:
            actions.insert(0, {
                'type': 'overall_savings',
                'description': f"通过分时段弹性配置，预计每月可节省 ¥{total_savings:.0f} ({(total_savings/current_cost)*100:.1f}%)",
                'priority': 'high',
                'estimated_roi': '立即生效'
            })
        
        return actions


# 使用示例
if __name__ == '__main__':
    optimizer = CostOptimizer(monthly_budget=1000)
    
    # 模拟预测数据
    mock_forecast = {
        'predictions': [
            {'ds': '2026-07-24T00:00:00', 'yhat': 15.2, 'yhat_lower': 12.1, 'yhat_upper': 18.3},
            {'ds': '2026-07-24T01:00:00', 'yhat': 12.8, 'yhat_lower': 10.5, 'yhat_upper': 15.1},
            {'ds': '2026-07-24T08:00:00', 'yhat': 65.3, 'yhat_lower': 58.2, 'yhat_upper': 72.4},
            {'ds': '2026-07-24T14:00:00', 'yhat': 82.1, 'yhat_lower': 75.6, 'yhat_upper': 88.7},
            {'ds': '2026-07-24T20:00:00', 'yhat': 45.6, 'yhat_lower': 40.1, 'yhat_upper': 51.2},
        ]
    }
    
    report = optimizer.generate_optimization_report(
        current_cost=680.0,
        forecast=mock_forecast,
        sla={'safety_margin': 1.3, 'response_time_p99_ms': 200}
    )
    
    print(json.dumps(asdict(report), indent=2, ensure_ascii=False))
PYEOF

echo "✅ 优化引擎已创建"

cat > scheduler.sh << 'BASHEOF'
#!/bin/bash
# AI VPS Cost Optimizer - 定时调度脚本
# 每天凌晨2点执行一次成本优化分析

set -euo pipefail

LOG_DIR="/var/log/ai-cost-optimizer"
CONFIG_DIR="$HOME/ai-cost-optimizer/config"
REPORT_DIR="$HOME/ai-cost-optimizer/reports"

mkdir -p "$LOG_DIR" "$CONFIG_DIR" "$REPORT_DIR"

LOG_FILE="$LOG_DIR/optimizer-$(date +%Y%m%d).log"

log() {
    echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" | tee -a "$LOG_FILE"
}

log "🚀 开始执行AI成本优化任务"

# 1. 采集最新监控数据
log "📊 步骤1: 采集监控数据..."
METRICS=$(curl -s http://localhost:9090/api/v1/query?query=node_cpu_seconds_total | \
    python3 -c "
import sys, json
data = json.load(sys.stdin)
for ts, val in data['data']['result'][0]['values'][-5:]:
    print(f'{ts},{val}')
" 2>/dev/null || echo "采集失败，使用缓存数据")

# 2. 运行预测
log "🔮 步骤2: 运行流量预测..."
PREDICTION=$(python3 -c "
from predictor import TrafficPredictor
import json

predictor = TrafficPredictor()
# 从Prometheus获取历史数据并训练
# ... (实际使用时连接Prometheus API)

result = predictor.predict('cpu_usage', 168)  # 预测7天
print(json.dumps(result, default=str))
" 2>/dev/null || echo "{}")

# 3. 生成优化建议
log "💡 步骤3: 生成优化建议..."
REPORT=$(python3 -c "
from optimizer import CostOptimizer, asdict
import json

optimizer = CostOptimizer()
report = optimizer.generate_optimization_report(
    current_cost=680.0,
    forecast=json.loads('''$PREDICTION'''),
    sla={'safety_margin': 1.3}
)
print(json.dumps(asdict(report), default=str, indent=2))
" 2>/dev/null || echo "{}")

# 4. 保存报告
echo "$REPORT" > "$REPORT_DIR/report-$(date +%Y%m%d-%H%M%S).json"

# 5. 如果节省超过阈值，自动执行
SAVINGS_PCT=$(echo "$REPORT" | python3 -c "
import sys, json
data = json.load(sys.stdin)
print(data.get('savings_percentage', 0))
" 2>/dev/null || echo "0")

if (( $(echo "$SAVINGS_PCT > 20" | bc -l) )); then
    log "💰 检测到显著节省机会 ($SAVINGS_PCT%)，触发自动优化流程"
    # 发送通知
    curl -X POST webhook_url -d "{\"text\": \"🎉 AI成本优化建议: 预计每月节省 ${SAVINGS_PCT}%\"}"
else
    log "✅ 成本在合理范围内，无需调整"
fi

log "✅ AI成本优化任务完成"
BASHEOF

chmod +x scheduler.sh

# 添加到 crontab (每天凌晨2点执行)
echo "0 2 * * * $HOME/ai-cost-optimizer/scheduler.sh >> /var/log/ai-cost-optimizer/cron.log 2>&1" | crontab -

echo "✅ 调度器已配置，每天凌晨2点自动执行"

cat > dashboard.html << 'HTMLEOF'
<!DOCTYPE html>
<html lang="zh-CN">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>AI VPS 成本优化仪表盘</title>
    <style>
        :root {
            --bg-primary: #0f172a;
            --bg-secondary: #1e293b;
            --text-primary: #f1f5f9;
            --text-secondary: #94a3b8;
            --accent: #6366f1;
            --success: #10b981;
            --warning: #f59e0b;
            --danger: #ef4444;
        }
        * { margin: 0; padding: 0; box-sizing: border-box; }
        body {
            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
            background: var(--bg-primary);
            color: var(--text-primary);
            min-height: 100vh;
        }
        .container { max-width: 1200px; margin: 0 auto; padding: 20px; }
        header {
            display: flex; justify-content: space-between; align-items: center;
            padding: 20px 0; border-bottom: 1px solid #334155;
        }
        h1 { font-size: 24px; }
        .badge {
            background: var(--accent); color: white; padding: 4px 12px;
            border-radius: 20px; font-size: 12px;
        }
        .grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)); gap: 20px; margin: 24px 0; }
        .card {
            background: var(--bg-secondary); border-radius: 12px; padding: 24px;
            border: 1px solid #334155;
        }
        .card-title { color: var(--text-secondary); font-size: 14px; margin-bottom: 8px; }
        .card-value { font-size: 32px; font-weight: bold; }
        .card-change { font-size: 14px; margin-top: 8px; }
        .positive { color: var(--success); }
        .negative { color: var(--danger); }
        table { width: 100%; border-collapse: collapse; margin-top: 16px; }
        th, td { padding: 12px; text-align: left; border-bottom: 1px solid #334155; }
        th { color: var(--text-secondary); font-size: 12px; text-transform: uppercase; }
        tr:hover { background: rgba(99, 102, 241, 0.1); }
    </style>
</head>
<body>
    <div class="container">
        <header>
            <div>
                <h1>🤖 AI VPS 成本优化仪表盘</h1>
                <p style="color: var(--text-secondary); margin-top: 4px;">Last updated: <span id="lastUpdate"></span></p>
            </div>
            <span class="badge">AI Powered</span>
        </header>

        <div class="grid">
            <div class="card">
                <div class="card-title">月度总成本</div>
                <div class="card-value" id="monthlyCost">¥680</div>
                <div class="card-change positive">↓ 优化后可降至 ¥280</div>
            </div>
            <div class="card">
                <div class="card-title">预计月节省</div>
                <div class="card-value positive" id="monthlySavings">¥400</div>
                <div class="card-change positive">年节省 ¥4,800</div>
            </div>
            <div class="card">
                <div class="card-title">资源利用率</div>
                <div class="card-value" id="utilization">23%</div>
                <div class="card-change negative">优化目标: ≥60%</div>
            </div>
            <div class="card">
                <div class="card-title">AI置信度</div>
                <div class="card-value" id="confidence">92%</div>
                <div class="card-change">基于30天历史数据</div>
            </div>
        </div>

        <div class="card">
            <h3 style="margin-bottom: 16px;">📋 优化建议</h3>
            <table>
                <thead>
                    <tr>
                        <th>时段</th>
                        <th>当前配置</th>
                        <th>推荐配置</th>
                        <th>节省</th>
                        <th>操作</th>
                    </tr>
                </thead>
                <tbody id="recommendations">
                    <tr>
                        <td>00:00 - 08:00</td>
                        <td>s3.2xlarge (4C16G)</td>
                        <td>s1.medium (2C2G)</td>
                        <td class="positive">-¥490/月</td>
                        <td><button style="background: var(--accent); color: white; border: none; padding: 6px 16px; border-radius: 6px; cursor: pointer;">执行</button></td>
                    </tr>
                    <tr>
                        <td>08:00 - 20:00</td>
                        <td>s3.2xlarge (4C16G)</td>
                        <td>s3.xlarge (4C8G)</td>
                        <td class="positive">-¥260/月</td>
                        <td><button style="background: var(--accent); color: white; border: none; padding: 6px 16px; border-radius: 6px; cursor: pointer;">执行</button></td>
                    </tr>
                    <tr>
                        <td>20:00 - 00:00</td>
                        <td>s3.2xlarge (4C16G)</td>
                        <td>s2.large (2C4G)</td>
                        <td class="positive">-¥412/月</td>
                        <td><button style="background: var(--accent); color: white; border: none; padding: 6px 16px; border-radius: 6px; cursor: pointer;">执行</button></td>
                    </tr>
                </tbody>
            </table>
        </div>
    </div>
    <script>
        document.getElementById('lastUpdate').textContent = new Date().toLocaleString('zh-CN');
    </script>
</body>
</html>
HTMLEOF

echo "✅ 仪表盘HTML已创建"

# 存储优化策略
STORAGE_OPTIMIZATION = {
    "快照管理": {
        "策略": "自动删除超过30天的快照",
        "工具": "cron + API调用",
        "预期节省": "15-30% 存储费用"
    },
    "冷热分层": {
        "策略": "3个月未访问的数据移至冷存储",
        "工具": "rsync + 定时任务",
        "预期节省": "50-70% 归档存储费用"
    },
    "去重压缩": {
        "策略": "对备份数据进行重复数据删除和压缩",
        "工具": "restic/borg",
        "预期节省": "40-60% 备份存储"
    },
    "CDN加速": {
        "策略": "静态资源走CDN，减少源站带宽",
        "工具": "Cloudflare/Nginx+CDN",
        "预期节省": "30-50% 带宽费用"
    }
}