MCP（Model Context Protocol）全球化部署：智能 DNS 流量调度

import socket
import threading

class EdgeNode:
    def __init__(self, node_id, ip, port):
        self.node_id = node_id
        self.ip = ip
        self.port = port
        self.cache = {}  # 简单缓存示例
        self.load = 0  # 当前负载

    def start(self):
        server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        server_socket.bind((self.ip, self.port))
        server_socket.listen(5)
        print(f"Edge Node {self.node_id} started on {self.ip}:{self.port}")

        while True:
            client_socket, addr = server_socket.accept()
            threading.Thread(target=self.handle_request, args=(client_socket,)).start()

    def handle_request(self, client_socket):
        request = client_socket.recv(1024).decode()
        # 模拟请求处理逻辑
        if request in self.cache:
            response = self.cache[request]
        else:
            # 模拟向数据中心请求数据
            response = f"Data for {request} from Edge Node {self.node_id}"
            self.cache[request] = response
            self.load += 1  # 模拟负载增加

        client_socket.send(response.encode())
        client_socket.close()

import flask
from flask import request, jsonify
import gevent.pywsgi
import requests
import logging
from prometheus_client import start_http_server, Gauge, Counter
import time
import json

# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Prometheus 指标
REQUESTS_TOTAL = Counter('dns_scheduler_requests_total', 'Total DNS scheduling requests')
RESPONSE_TIME = Gauge('dns_scheduler_response_time_seconds', 'DNS response time in seconds')
NODES_AVAILABLE = Gauge('dns_scheduler_nodes_available', 'Available edge nodes count')

class DNSScheduler:
    def __init__(self, config_path):
        self.load_config(config_path)
        self.edge_nodes = self.discover_edge_nodes()
        self.start_prometheus_server()

    def load_config(self, config_path):
        """加载配置文件"""
        try:
            with open(config_path, 'r') as f:
                self.config = json.load(f)
            logging.info(f"Loaded config: {self.config}")
        except Exception as e:
            logging.error(f"Error loading config: {e}")
            raise

    def discover_edge_nodes(self):
        """发现可用的边缘节点"""
        try:
            response = requests.get(self.config['edge_node_discovery_url'])
            if response.status_code == 200:
                nodes = response.json()
                logging.info(f"Discovered {len(nodes)} edge nodes")
                NODES_AVAILABLE.set(len(nodes))
                return nodes
            else:
                logging.warning(f"Failed to discover edge nodes: {response.status_code}")
                return []
        except requests.exceptions.RequestException as e:
            logging.error(f"Error discovering edge nodes: {e}")
            return []

    def schedule(self, client_ip):
        """执行调度逻辑"""
        REQUESTS_TOTAL.inc()
        start_time = time.time()

        # 获取客户端地理位置
        geo_info = self.get_geo_info(client_ip)
        if not geo_info:
            logging.warning(f"Failed to get geo info for IP {client_ip}")
            return None

        # 筛选符合条件的节点
        candidate_nodes = self.filter_nodes(geo_info)

        if not candidate_nodes:
            logging.warning(f"No suitable edge nodes for IP {client_ip}")
            return None

        # 执行调度算法
        target_node = self.run_scheduling_algorithm(candidate_nodes, geo_info)

        if target_node:
            logging.info(f"Scheduled IP {client_ip} to node {target_node['id']}")
        else:
            logging.warning(f"Failed to schedule IP {client_ip}")

        response_time = time.time() - start_time
        RESPONSE_TIME.set(response_time)

        return target_node

    def get_geo_info(self, ip):
        """获取 IP 地理位置信息"""
        try:
            response = requests.get(f"http://ip-api.com/json/{ip}")
            if response.status_code == 200:
                data = response.json()
                if data['status'] == 'success':
                    return {
                        'country': data['country'],
                        'region': data['regionName'],
                        'city': data['city'],
                        'latitude': data['lat'],
                        'longitude': data['lon']
                    }
        except requests.exceptions.RequestException as e:
            logging.error(f"Error getting geo info: {e}")
        return None

    def filter_nodes(self, geo_info):
        """筛选符合条件的边缘节点"""
        candidate_nodes = []
        for node in self.edge_nodes:
            # 简单示例：筛选与客户端同一国家的节点
            if node['country'] == geo_info['country']:
                candidate_nodes.append(node)
        return candidate_nodes

    def run_scheduling_algorithm(self, nodes, geo_info):
        """运行调度算法"""
        # 简单示例：选择负载最低的节点
        if not nodes:
            return None

        # 获取节点实时负载
        for node in nodes:
            try:
                response = requests.get(f"http://{node['ip']}:{node['port']}/status")
                if response.status_code == 200:
                    node['current_load'] = response.json().get('load', 0)
                else:
                    node['current_load'] = float('inf')  # 标记为不可用
            except requests.exceptions.RequestException:
                node['current_load'] = float('inf')

        # 选择负载最低的节点
        min_load_node = min(nodes, key=lambda x: x['current_load'])
        if min_load_node['current_load'] == float('inf'):
            return None

        return min_load_node

    def start_prometheus_server(self):
        """启动 Prometheus 指标服务器"""
        start_http_server(9100)
        logging.info("Prometheus server started on port 9100")

    def update_nodes_periodically(self):
        """定期更新边缘节点列表"""
        while True:
            self.edge_nodes = self.discover_edge_nodes()
            time.sleep(self.config.get('node_discovery_interval', 30))

# Flask 应用
app = flask.Flask(__name__)
scheduler = DNSScheduler('config.json')

@app.route('/schedule', methods=['POST'])
def handle_schedule():
    data = request.json
    client_ip = data.get('client_ip')
    if not client_ip:
        return jsonify({'error': 'Missing client_ip'}), 400

    target_node = scheduler.schedule(client_ip)
    if target_node:
        return jsonify({
            'success': True,
            'node_id': target_node['id'],
            'node_ip': target_node['ip'],
            'node_port': target_node['port']
        })
    else:
        return jsonify({'success': False, 'error': 'No suitable node found'}), 503

if __name__ == '__main__':
    # 启动定期更新节点的线程
    from threading import Thread
    Thread(target=scheduler.update_nodes_periodically, daemon=True).start()

    # 启动 Flask 服务器
    logging.info("Starting DNS Scheduler server...")
    server = gevent.pywsgi.WSGIServer(('0.0.0.0', 5000), app)
    server.serve_forever()

{
  "edge_node_discovery_url": "http://edge-nodes-registry.example.com/api/v1/nodes",
  "node_discovery_interval": 30,
  "default_ttl": 30
}

from flask import Flask, jsonify, request
import requests

app = Flask(__name__)

# 模拟的边缘节点注册表
registered_nodes = []

@app.route('/register', methods=['POST'])
def register_node():
    data = request.json
    required_fields = ['id', 'ip', 'port', 'country', 'region', 'city', 'latitude', 'longitude']
    
    if not all(field in data for field in required_fields):
        return jsonify({'error': 'Missing required fields'}), 400

    # 检查节点是否已注册
    existing_node = next((node for node in registered_nodes if node['id'] == data['id']), None)
    if existing_node:
        # 更新现有节点信息
        existing_node.update(data)
        return jsonify({'success': True, 'message': 'Node updated'})
    else:
        # 注册新节点
        registered_nodes.append(data)
        return jsonify({'success': True, 'message': 'Node registered'})

@app.route('/nodes', methods=['GET'])
def get_nodes():
    return jsonify(registered_nodes)

@app.route('/heartbeat', methods=['POST'])
def heartbeat():
    node_id = request.json.get('id')
    if not node_id:
        return jsonify({'error': 'Missing node id'}), 400

    existing_node = next((node for node in registered_nodes if node['id'] == node_id), None)
    if existing_node:
        # 更新节点状态（如最后活跃时间）
        existing_node['last_heartbeat'] = time.time()
        return jsonify({'success': True})
    else:
        return jsonify({'error': 'Node not found'}), 404

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5001)

# DNS 调度负载均衡配置
upstream dns_scheduler {
    least_conn;
    server dns-scheduler-1.example.com:5000;
    server dns-scheduler-2.example.com:5000;
    server dns-scheduler-3.example.com:5000;
}

server {
    listen 80;

    location /schedule {
        proxy_pass http://dns_scheduler;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    }

    location /metrics {
        proxy_pass http://dns_scheduler-1.example.com:5000/metrics;
    }
}

sudo apt install -y redis-server

appendonly yes
dir /var/lib/redis
dbfilename dump.rdb

sudo systemctl restart redis-server

import redis

class DNSScheduler:
    def __init__(self, config_path):
        self.load_config(config_path)
        self.edge_nodes = self.discover_edge_nodes()
        self.redis_client = redis.Redis(host=self.config['redis_host'], port=self.config['redis_port'], db=0)
        self.start_prometheus_server()

    # ... 其他方法不变 ...

    def discover_edge_nodes(self):
        """从 Redis 获取边缘节点列表"""
        try:
            nodes_json = self.redis_client.get('edge_nodes')
            if nodes_json:
                nodes = json.loads(nodes_json)
                logging.info(f"Loaded {len(nodes)} edge nodes from Redis")
                NODES_AVAILABLE.set(len(nodes))
                return nodes
            else:
                # 从原始发现 URL 获取并存入 Redis
                response = requests.get(self.config['edge_node_discovery_url'])
                if response.status_code == 200:
                    nodes = response.json()
                    self.redis_client.setex('edge_nodes', 30, json.dumps(nodes))  # 缓存 30 秒
                    logging.info(f"Discovered {len(nodes)} edge nodes and stored in Redis")
                    NODES_AVAILABLE.set(len(nodes))
                    return nodes
                else:
                    logging.warning(f"Failed to discover edge nodes: {response.status_code}")
                    return []
        except (requests.exceptions.RequestException, redis.exceptions.RedisError) as e:
            logging.error(f"Error discovering edge nodes: {e}")
            return []

    def update_nodes_periodically(self):
        """定期更新边缘节点列表并存储到 Redis"""
        while True:
            try:
                response = requests.get(self.config['edge_node_discovery_url'])
                if response.status_code == 200:
                    nodes = response.json()
                    self.redis_client.setex('edge_nodes', 30, json.dumps(nodes))  # 更新缓存
                    logging.info(f"Updated edge nodes in Redis: {len(nodes)} nodes")
            except requests.exceptions.RequestException as e:
                logging.error(f"Error updating edge nodes: {e}")
            time.sleep(self.config.get('node_discovery_interval', 30))

{
  "edge_node_discovery_url": "http://edge-nodes-registry.example.com/api/v1/nodes",
  "node_discovery_interval": 30,
  "default_ttl": 30,
  "redis_host": "redis.example.com",
  "redis_port": 6379
}

class MCPContext:
    def __init__(self):
        self.device_info = {
            'type': None,  # 设备类型：Desktop/Mobile/Tablet
            'os': None,    # 操作系统：Windows/macOS/iOS/Android
            'model': None  # 设备型号
        }
        self.network_info = {
            'type': None,  # 网络类型：WiFi/4G/5G
            'bandwidth': None,  # 估计带宽（bps）
            'latency': None     # 估计延迟（ms）
        }
        self.geo_info = {
            'country': None,
            'region': None,
            'city': None,
            'latitude': None,
            'longitude': None
        }
        self.user_behavior = {
            'session_duration': None,  # 会话持续时间（秒）
            'interaction_rate': None   # 交互频率（次/分钟）
        }

def adapt_data_transfer(context, original_data):
    """根据上下文动态调整数据传输策略"""
    adapted_data = original_data.copy()

    # 数据压缩适配
    if context.network_info['bandwidth'] < 5 * 1024 * 1024:  # 带宽小于 5Mbps
        adapted_data = apply_compression(adapted_data, 'zstd', level=3)
    elif context.network_info['bandwidth'] < 10 * 1024 * 1024:  # 带宽在 5-10Mbps
        adapted_data = apply_compression(adapted_data, 'gzip', level=6)
    else:
        adapted_data = apply_compression(adapted_data, 'gzip', level=1)

    # 内容裁剪适配
    if context.device_info['type'] == 'Mobile':
        adapted_data = crop_content_for_mobile(adapted_data, context.device_info['model'])

    # 传输协议选择
    if context.network_info['type'] in ['4G', '5G'] and context.network_info.get('packet_loss', 0) > 5:
        protocol = 'tcp'
    else:
        protocol = 'udp'

    return adapted_data, protocol

def apply_compression(data, algorithm, level):
    """应用数据压缩"""
    if algorithm == 'zstd':
        import zstandard
        compressor = zstandard.ZstdCompressor(level=level)
        return compressor.compress(data)
    elif algorithm == 'gzip':
        import gzip
        return gzip.compress(data, compresslevel=level)
    return data

def crop_content_for_mobile(data, device_model):
    """根据移动设备裁剪内容"""
    # 根据设备型号获取屏幕尺寸信息（简化示例）
    screen_sizes = {
        'iPhone 14': (390, 844),  # 宽 x 高（pt）
        'Samsung S22': (360, 760),
        'Pixel 6': (412, 866)
    }
    width, height = screen_sizes.get(device_model, (360, 640))

    # 裁剪图片和视频内容（伪代码）
    if 'images' in data:
        for i, img in enumerate(data['images']):
            data['images'][i] = resize_image(img, max_width=width)

    if 'videos' in data:
        for i, vid in enumerate(data['videos']):
            data['videos'][i] = transcode_video(vid, max_width=width)

    return data

from cachetools import TTLCache

class MultiLevelCache:
    def __init__(self):
        # L1 缓存：小容量，高命中率
        self.l1_cache = TTLCache(maxsize=1000, ttl=30)
        
        # L2 缓存：中等容量，较长 TTL
        self.l2_cache = TTLCache(maxsize=5000, ttl=300)
        
        # L3 缓存：大容量，最长 TTL
        self.l3_cache = TTLCache(maxsize=20000, ttl=86400)

    def get(self, key):
        # 优先从 L1 获取
        value = self.l1_cache.get(key)
        if value:
            return value
        
        # 然后尝试 L2
        value = self.l2_cache.get(key)
        if value:
            # 提升到 L1（近期频繁访问）
            self.l1_cache[key] = value
            return value
        
        # 最后尝试 L3
        value = self.l3_cache.get(key)
        if value:
            # 提升到 L2 和 L1
            self.l2_cache[key] = value
            self.l1_cache[key] = value
            return value
        
        return None

    def set(self, key, value, cache_level):
        """设置缓存级别"""
        self.l3_cache[key] = value  # 所有数据都进入 L3
        
        if cache_level <= 2:
            self.l2_cache[key] = value
        if cache_level == 1:
            self.l1_cache[key] = value

# 使用示例
cache = MultiLevelCache()
cache.set('hot_data_1', 'value1', 1)
cache.set('warm_data_1', 'value2', 2)
cache.set('cold_data_1', 'value3', 3)

print(cache.get('hot_data_1'))  # 从 L1 获取
print(cache.get('warm_data_1')) # 从 L2 获取，自动提升到 L1
print(cache.get('cold_data_1')) # 从 L3 获取，自动提升到 L2 和 L1

class Preloader:
    def __init__(self, edge_node):
        self.edge_node = edge_node
        self.prediction_model = self.load_prediction_model()
        self.preloaded_keys = set()

    def load_prediction_model(self):
        """加载预测模型（简化示例）"""
        # 在实际应用中，这里可以加载训练好的机器学习模型
        return {
            'search_page': ['search_results', 'product_filters', 'recent_viewed_items'],
            'product_page': ['product_reviews', 'related_products', 'inventory_status'],
            'cart_page': ['checkout_flow', 'payment_options', 'shipping_options']
        }

    def predict_next_requests(self, current_context):
        """预测下一个可能的请求"""
        # 简单示例：基于当前页面预测相关资源
        current_page = current_context.get('current_page')
        if not current_page:
            return []

        return self.prediction_model.get(current_page, [])

    def preload_resources(self, keys):
        """预加载资源"""
        new_keys = [key for key in keys if key not in self.preloaded_keys]
        if not new_keys:
            return

        # 向数据中心请求预加载资源
        try:
            response = requests.post(
                f"{self.edge_node.data_center_url}/preload",
                json={'keys': new_keys}
            )
            if response.status_code == 200:
                preloaded_data = response.json()
                for key, value in preloaded_data.items():
                    self.edge_node.cache[key] = value
                    self.preloaded_keys.add(key)
                    print(f"Preloaded key: {key}")
        except requests.exceptions.RequestException as e:
            print(f"Error preloading resources: {e}")

# 使用示例
preloader = Preloader(edge_node)

# 模拟用户在产品页面
context = {'current_page': 'product_page'}
predicted_keys = preloader.predict_next_requests(context)
preloader.preload_resources(predicted_keys)

import psutil

class NodeResourceManager:
    def __init__(self, edge_node):
        self.edge_node = edge_node
        self.thresholds = {
            'cpu': 80,    # CPU 使用率阈值（%）
            'memory': 85, # 内存使用率阈值（%）
            'load': 70    # 负载阈值
        }

    def monitor_resources(self):
        """监控节点资源"""
        cpu_usage = psutil.cpu_percent()
        memory_usage = psutil.virtual_memory().percent
        current_load = self.edge_node.load

        print(f"Resource usage: CPU={cpu_usage}%, Memory={memory_usage}%, Load={current_load}")

        # 检查是否超过阈值
        if (cpu_usage > self.thresholds['cpu'] or
            memory_usage > self.thresholds['memory'] or
            current_load > self.thresholds['load']):
            self.trigger_resource_optimization()

    def trigger_resource_optimization(self):
        """触发资源优化措施"""
        print("Resource usage exceeds thresholds. Starting optimization...")

        # 清理 L3 缓存中的过期数据
        self.clear_l3_cache()

        # 限制新请求的处理速率
        self.throttle_requests()

        # 如果情况严重，向 DNS 调度中心报告状态，减少新流量
        if self.should_report_to_dns_scheduler():
            self.report_to_dns_scheduler()

    def clear_l3_cache(self):
        """清理 L3 缓存"""
        # 简化示例：清除 L3 缓存中 30% 的数据
        l3_cache_size = len(self.edge_node.cache)
        keys_to_remove = list(self.edge_node.cache.keys())[:int(l3_cache_size * 0.3)]
        
        for key in keys_to_remove:
            if key in self.edge_node.cache:
                del self.edge_node.cache[key]
                print(f"Cleared cache key: {key}")

    def throttle_requests(self):
        """限制请求处理速率"""
        # 简化示例：将处理速率限制为当前的 70%
        self.edge_node.max_concurrent_requests = int(self.edge_node.max_concurrent_requests * 0.7)
        print(f"Throttled requests to {self.edge_node.max_concurrent_requests} concurrent")

    def should_report_to_dns_scheduler(self):
        """判断是否需要向 DNS 调度中心报告"""
        # 简化示例：当 CPU 和内存都超过阈值时报告
        cpu_usage = psutil.cpu_percent()
        memory_usage = psutil.virtual_memory().percent
        return cpu_usage > self.thresholds['cpu'] and memory_usage > self.thresholds['memory']

    def report_to_dns_scheduler(self):
        """向 DNS 调度中心报告状态"""
        try:
            status_data = {
                'node_id': self.edge_node.node_id,
                'status': 'overloaded',
                'available_capacity': 0.3  # 剩余容量 30%
            }
            response = requests.post("http://dns-scheduler.example.com/status/update", json=status_data)
            print(f"Reported overloaded status to DNS scheduler. Response: {response.status_code}")
        except requests.exceptions.RequestException as e:
            print(f"Error reporting to DNS scheduler: {e}")

# 在边缘节点中集成资源管理器
class EdgeNode:
    def __init__(self, node_id, ip, port, data_center_url):
        # ... 现有初始化代码 ...

        # 添加资源管理器
        self.resource_manager = NodeResourceManager(self)

        # 启动资源监控线程
        threading.Thread(target=self.continuous_resource_monitoring, daemon=True).start()

    def continuous_resource_monitoring(self):
        """持续监控资源使用情况"""
        while True:
            self.resource_manager.monitor_resources()
            time.sleep(10)  # 每 10 秒检查一次

# 示例：启动带有资源管理的边缘节点
if __name__ == "__main__":
    edge_node = EdgeNode(
        node_id="edge-node-01",
        ip="0.0.0.0",
        port=8080,
        data_center_url="http://data-center.example.com"
    )
    edge_node.start()

from cachetools import TTLCache

class SmartDNSCache:
    def __init__(self):
        # 默认缓存策略
        self.default_cache = TTLCache(maxsize=10000, ttl=10)
        
        # 区域特定缓存策略
        self.region_caches = {
            'North America': TTLCache(maxsize=5000, ttl=30),
            'Europe': TTLCache(maxsize=3000, ttl=30),
            'Asia': TTLCache(maxsize=4000, ttl=10),
            'Africa': TTLCache(maxsize=2000, ttl=5),
            'South America': TTLCache(maxsize=1500, ttl=10)
        }

    def get(self, key, region):
        """从缓存获取值"""
        # 首先尝试区域特定缓存
        region_cache = self.region_caches.get(region)
        if region_cache:
            value = region_cache.get(key)
            if value:
                return value
        
        # 然后尝试默认缓存
        return self.default_cache.get(key)

    def set(self, key, value, region):
        """设置缓存值"""
        # 添加到区域特定缓存
        region_cache = self.region_caches.get(region)
        if region_cache:
            region_cache[key] = value
        
        # 同步添加到默认缓存
        self.default_cache[key] = value

# 修改 DNS 调度服务以使用智能缓存
class DNSScheduler:
    def __init__(self, config_path):
        # ... 现有初始化代码 ...

        # 添加智能 DNS 缓存
        self.dns_cache = SmartDNSCache()

    def schedule(self, client_ip):
        # 获取客户端地理位置信息
        geo_info = self.get_geo_info(client_ip)
        if not geo_info:
            logging.warning(f"Failed to get geo info for IP {client_ip}")
            return None

        # 构建缓存键
        cache_key = f"{client_ip}:{geo_info['country']}"
        region = geo_info['country']

        # 尝试从缓存获取结果
        cached_result = self.dns_cache.get(cache_key, region)
        if cached_result:
            logging.info(f"Cache hit for IP {client_ip}")
            return cached_result

        # 缓存未命中，执行实际调度
        target_node = super().schedule(client_ip)  # 假设 schedule 方法已重构

        if target_node:
            # 缓存调度结果
            self.dns_cache.set(cache_key, target_node, region)
            logging.info(f"Cached scheduling result for IP {client_ip}")

        return target_node

from datetime import datetime, timedelta
import pytz

class PredictiveScheduler:
    def __init__(self, dns_scheduler):
        self.dns_scheduler = dns_scheduler
        self.traffic_patterns = self.load_traffic_patterns()

    def load_traffic_patterns(self):
        """加载流量模式数据（简化示例）"""
        # 在实际应用中，这里可以从数据库加载历史流量数据和预测模型
        return {
            'daily': {
                'peak_hours': [12, 13, 14, 19, 20, 21],  # 午餐和晚餐高峰
                'traffic_increase': 3.5  # 预计流量增加 3.5 倍
            },
            'weekly': {
                'peak_days': [4, 5, 6],  # 周五、周六、周日
                'traffic_increase': 2.8
            },
            'special_events': [
                {'date': '2023-11-11', 'increase': 5.0},  # 双十一
                {'date': '2023-12-25', 'increase': 4.2}   # 圣诞节
            ]
        }

    def should_predictive_schedule(self):
        """判断是否需要预调度"""
        now = datetime.now(pytz.utc)

        # 检查是否在日常高峰时段前 15 分钟
        if now.hour in self.traffic_patterns['daily']['peak_hours']:
            if now.minute >= 45:  # 高峰前 15 分钟
                return True

        # 检查是否在周末高峰日前一天晚上
        if now.weekday() in [3]:  # 周四晚上预测周五高峰
            if now.hour >= 20:  # 晚上 8 点后
                return True

        # 检查是否临近特殊事件
        for event in self.traffic_patterns['special_events']:
            event_date = datetime.fromisoformat(event['date']).replace(tzinfo=pytz.utc)
            if (event_date - now).days == 1:  # 提前一天准备
                if now.hour >= 18:  # 下午 6 点后
                    return True

        return False

    def perform_predictive_scheduling(self):
        """执行预调度操作"""
        if not self.should_predictive_schedule():
            return

        logging.info("Performing predictive scheduling...")

        # 预先发现更多边缘节点（如果有扩展资源）
        try:
            response = requests.get(
                self.dns_scheduler.config['edge_node_discovery_url'],
                params={'mode': 'expanded'}
            )
            if response.status_code == 200:
                nodes = response.json()
                # 更新节点列表（缓存时间较短）
                self.dns_scheduler.redis_client.setex('edge_nodes_expanded', 900, json.dumps(nodes))
                logging.info(f"Discovered {len(nodes)} expanded edge nodes for predictive scheduling")
        except requests.exceptions.RequestException as e:
            logging.error(f"Error in predictive node discovery: {e}")

        # 预加载热门内容到边缘节点
        self.preload_popular_content()

        # 调整缓存策略，缩短 TTL 以更快适应流量变化
        self.adjust_cache_strategies()

    def preload_popular_content(self):
        """预加载热门内容"""
        try:
            # 获取热门内容列表（简化示例）
            response = requests.get("http://content-management.example.com/api/popular")
            if response.status_code == 200:
                popular_keys = response.json().get('keys', [])
                
                # 向所有边缘节点发送预加载请求
                nodes = self.dns_scheduler.get_all_edge_nodes()
                for node in nodes:
                    try:
                        requests.post(
                            f"http://{node['ip']}:{node['port']}/preload",
                            json={'keys': popular_keys[:50]}  # 限制每次预加载的数量
                        )
                        logging.info(f"Preloaded popular content to node {node['id']}")
                    except requests.exceptions.RequestException as e:
                        logging.error(f"Error preloading to {node['id']}: {e}")
        except requests.exceptions.RequestException as e:
            logging.error(f"Error getting popular content list: {e}")

    def adjust_cache_strategies(self):
        """调整缓存策略"""
        # 缩短 DNS 缓存时间
        self.dns_scheduler.config['default_ttl'] = 10  # 原 30 秒改为 10 秒
        logging.info("Adjusted DNS cache TTL to 10 seconds for predictive scheduling")

        # 通知边缘节点调整缓存策略
        try:
            nodes = self.dns_scheduler.get_all_edge_nodes()
            for node in nodes:
                try:
                    requests.post(
                        f"http://{node['ip']}:{node['port']}/cache/strategy",
                        json={'ttl_factor': 0.5}  # 缓存时间减半
                    )
                    logging.info(f"Adjusted cache strategy for node {node['id']}")
                except requests.exceptions.RequestException as e:
                    logging.error(f"Error adjusting cache for {node['id']}: {e}")
        except Exception as e:
            logging.error(f"Error adjusting edge node cache strategies: {e}")

# 在 DNS 调度服务中集成预调度器
class DNSScheduler:
    def __init__(self, config_path):
        # ... 现有初始化代码 ...

        # 添加预调度器
        self.predictive_scheduler = PredictiveScheduler(self)

        # 启动预调度检查线程
        threading.Thread(target=self.continuous_predictive_check, daemon=True).start()

    def continuous_predictive_check(self):
        """持续检查是否需要预调度"""
        while True:
            self.predictive_scheduler.perform_predictive_scheduling()
            time.sleep(300)  # 每 5 分钟检查一次

# Flask 应用路由保持不变，预调度在后台运行

import zlib

class MCPProtocolOptimizer:
    def __init__(self):
        # 初始化 Huffman 编码表（简化示例）
        self.huffman_table = {
            'protocol_version': {0x0100: '0', 0x0200: '10', 0x0300: '110'},
            'message_type': {0x0001: '0', 0x0002: '10', 0x0003: '110'},
            'country_codes': {'US': '0', 'CN': '10', 'IN': '110', 'JP': '1110'}
        }

        # 初始化字典编码表
        self.dictionary = {
            'device_types': {'Desktop': 0, 'Mobile': 1, 'Tablet': 2},
            'os_types': {'Windows': 0, 'macOS': 1, 'iOS': 2, 'Android': 3}
        }

    def compress_header(self, header):
        """压缩协议头部"""
        compressed = b''

        # 版本号压缩
        version_code = header['protocol_version']
        version_bits = self.huffman_table['protocol_version'].get(version_code, '111')  # 默认编码
        compressed += int(version_bits, 2).to_bytes((len(version_bits) + 7) // 8, byteorder='big')

        # 消息类型压缩
        msg_type = header['message_type']
        msg_type_bits = self.huffman_table['message_type'].get(msg_type, '111')
        compressed += int(msg_type_bits, 2).to_bytes((len(msg_type_bits) + 7) // 8, byteorder='big')

        # 消息标识（不压缩，直接添加）
        compressed += header['message_id'].to_bytes(4, byteorder='big')

        # 其他字段保持不变，实际应用中可根据需要压缩
        compressed += header['context_length'].to_bytes(2, byteorder='big')
        compressed += header['payload_length'].to_bytes(2, byteorder='big')

        return compressed

    def compress_context(self, context):
        """压缩上下文数据"""
        compressed_context = {}

        # 设备类型字典编码
        device_type = context['device_info']['type']
        if device_type in self.dictionary['device_types']:
            compressed_context['device_type'] = self.dictionary['device_types'][device_type]
        else:
            compressed_context['device_type'] = 255  # 未知类型

        # 操作系统字典编码
        os_type = context['device_info']['os']
        if os_type in self.dictionary['os_types']:
            compressed_context['os_type'] = self.dictionary['os_types'][os_type]
        else:
            compressed_context['os_type'] = 255

        # 地理位置 Huffman 编码
        country_code = context['geo_info']['country']
        country_bits = self.huffman_table['country_codes'].get(country_code, '1111')
        compressed_context['country_code'] = int(country_bits, 2).to_bytes((len(country_bits) + 7) // 8, byteorder='big')

        # 其他字段使用 zlib 压缩
        other_data = json.dumps({
            'network_info': context['network_info'],
            'user_behavior': context['user_behavior']
        }).encode('utf-8')
        compressed_other = zlib.compress(other_data, level=6)
        compressed_context['other_data'] = compressed_other

        return compressed_context

    def decompress_context(self, compressed_context):
        """解压缩上下文数据"""
        context = {}

        # 解码设备类型
        device_type_code = compressed_context.get('device_type', 255)
        reverse_dict = {v: k for k, v in self.dictionary['device_types'].items()}
        context['device_info'] = {
            'type': reverse_dict.get(device_type_code, 'Unknown'),
            'os': ''  # 暂时不处理，后面继续
        }

        # 解码操作系统
        os_type_code = compressed_context.get('os_type', 255)
        reverse_os_dict = {v: k for k, v in self.dictionary['os_types'].items()}
        context['device_info']['os'] = reverse_os_dict.get(os_type_code, 'Unknown')

        # 解码国家代码
        country_code_data = compressed_context.get('country_code', b'')
        if country_code_data:
            country_bits = ''.join(f'{byte:08b}' for byte in country_code_data)
            # 查找 Huffman 表中的匹配
            for code, bits in self.huffman_table['country_codes'].items():
                if country_bits.startswith(bits):
                    context['geo_info'] = {'country': code}
                    break
            else:
                context['geo_info'] = {'country': 'Unknown'}
        else:
            context['geo_info'] = {'country': 'Unknown'}

        # 解压其他数据
        other_data = compressed_context.get('other_data')
        if other_data:
            decompressed = zlib.decompress(other_data)
            other_json = json.loads(decompressed.decode('utf-8'))
            context['network_info'] = other_json.get('network_info', {})
            context['user_behavior'] = other_json.get('user_behavior', {})

        return context

# 在 MCP 处理流程中集成协议优化器
optimizer = MCPProtocolOptimizer()

# 压缩示例
original_header = {
    'protocol_version': 0x0100,
    'message_type': 0x0001,
    'message_id': 0x12345678,
    'context_length': 32,
    'payload_length': 65536
}

compressed_header = optimizer.compress_header(original_header)
print(f"Original header length: {len(original_header)} items")
print(f"Compressed header length: {len(compressed_header)} bytes")

original_context = {
    'device_info': {
        'type': 'Mobile',
        'os': 'Android',
        'model': 'Pixel 6'
    },
    'network_info': {
        'type': '5G',
        'bandwidth': 50 * 1024 * 1024,
        'latency': 30
    },
    'geo_info': {
        'country': 'CN',
        'region': 'Beijing',
        'city': 'Beijing'
    },
    'user_behavior': {
        'session_duration': 120,
        'interaction_rate': 5.2
    }
}

compressed_context = optimizer.compress_context(original_context)
print(f"\nOriginal context size: {len(json.dumps(original_context))} bytes")
print(f"Compressed context size: {len(compressed_context['country_code']) + len(compressed_context['other_data'])} bytes")

# 解压缩示例
decompressed_context = optimizer.decompress_context(compressed_context)
print("\nDecompressed context:")
print(json.dumps(decompressed_context, indent=2))

class BatchProcessor:
    def __init__(self, max_batch_size=1000, max_batch_bytes=10*1024*1024):
        self.max_batch_size = max_batch_size
        self.max_batch_bytes = max_batch_bytes
        self.current_batch = []
        self.current_batch_size = 0

    def add_to_batch(self, item):
        """将项目添加到批量中"""
        item_size = self.estimate_item_size(item)
        
        # 检查是否超过批量限制
        if len(self.current_batch) >= self.max_batch_size or \
           self.current_batch_size + item_size > self.max_batch_bytes:
            # 批量已满，处理当前批量然后创建新批量
            self.process_batch()
            self.current_batch = []
            self.current_batch_size = 0
        
        # 添加到批量
        self.current_batch.append(item)
        self.current_batch_size += item_size

    def estimate_item_size(self, item):
        """估计项目大小（简化示例）"""
        # 在实际应用中，可以根据数据结构精确计算大小
        if isinstance(item, dict):
            return 150  # 假设平均大小 150 字节
        elif isinstance(item, str):
            return len(item.encode('utf-8'))
        else:
            return 200  # 默认估计

    def process_batch(self):
        """处理批量数据"""
        if not self.current_batch:
            return

        logging.info(f"Processing batch of {len(self.current_batch)} items, total size {self.current_batch_size} bytes")

        try:
            # 在这里实现批量数据的处理逻辑，如：
            # - 数据验证
            # - 批量数据库操作
            # - 并行数据分发

            # 示例：并行验证和处理
            from concurrent.futures import ThreadPoolExecutor

            with ThreadPoolExecutor(max_workers=8) as executor:
                futures = []
                for item in self.current_batch:
                    futures.append(executor.submit(self.process_item, item))
                
                # 收集结果
                results = [future.result() for future in futures]
                
                # 批量提交结果
                self.batch_commit_results(results)

        except Exception as e:
            logging.error(f"Error processing batch: {e}")
            # 实现重试逻辑或错误处理

        finally:
            # 清空当前批量
            self.current_batch = []
            self.current_batch_size = 0

    def process_item(self, item):
        """处理单个项目（将在 ThreadPool 中执行）"""
        # 实现具体的项目处理逻辑，如验证、转换等
        logging.debug(f"Processing item: {item['id']}")
        return {'id': item['id'], 'status': 'processed'}

    def batch_commit_results(self, results):
        """批量提交处理结果"""
        # 实现批量数据库操作或结果分发
        success_count = sum(1 for r in results if r['status'] == 'processed')
        logging.info(f"Batch processed: {success_count} succeeded, {len(results) - success_count} failed")

    def flush(self):
        """处理剩余的批量数据"""
        if self.current_batch:
            self.process_batch()

# 使用示例
batch_processor = BatchProcessor()

# 模拟添加多个项目
for i in range(1050):
    item = {
        'id': f'item-{i}',
        'data': f'Sample data for item {i}'.encode('utf-8'),
        'timestamp': time.time()
    }
    batch_processor.add_to_batch(item)

# 处理剩余批量
batch_processor.flush()

import hmac
import hashlib
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.primitives import padding
from cryptography.hazmat.backends import default_backend
import os

class MCPDataProtector:
    def __init__(self, encryption_key, signing_key):
        self.encryption_key = encryption_key
        self.signing_key = signing_key

    def encrypt_and_sign(self, data):
        """加密并签名数据"""
        # 生成随机 IV
        iv = os.urandom(16)
        
        # 加密数据
        cipher = Cipher(algorithms.AES(self.encryption_key), modes.CBC(iv), backend=default_backend())
        encryptor = cipher.encryptor()
        
        # PKCS7 填充
        padder = padding.PKCS7(128).padder()
        padded_data = padder.update(data) + padder.finalize()
        
        # 加密
        encrypted = encryptor.update(padded_data) + encryptor.finalize()
        
        # 签名
        signature = hmac.new(self.signing_key, iv + encrypted, hashlib.sha256).digest()
        
        return {
            'iv': iv,
            'encrypted_data': encrypted,
            'signature': signature
        }

    def verify_and_decrypt(self, encrypted_data):
        """验证并解密数据"""
        iv = encrypted_data.get('iv')
        encrypted = encrypted_data.get('encrypted_data')
        signature = encrypted_data.get('signature')

        if not (iv and encrypted and signature):
            raise ValueError("Invalid encrypted data format")

        # 验证签名
        expected_signature = hmac.new(self.signing_key, iv + encrypted, hashlib.sha256).digest()
        if not hmac.compare_digest(signature, expected_signature):
            raise ValueError("Data integrity check failed")

        # 解密
        cipher = Cipher(algorithms.AES(self.encryption_key), modes.CBC(iv), backend=default_backend())
        decryptor = cipher.decryptor()
        
        decrypted_padded = decryptor.update(encrypted) + decryptor.finalize()
        
        # 去除填充
        unpadder = padding.PKCS7(128).unpadder()
        decrypted = unpadder.update(decrypted_padded) + unpadder.finalize()
        
        return decrypted

# 使用示例
encryption_key = os.urandom(32)  # 256 位加密密钥
signing_key = os.urandom(32)     # 256 位签名密钥

protector = MCPDataProtector(encryption_key, signing_key)

# 加密和签名数据
original_data = b"Sensitive user data that needs protection"
protected = protector.encrypt_and_sign(original_data)

print(f"Original data: {original_data}")
print(f"Encrypted data size: {len(protected['encrypted_data'])} bytes")
print(f"IV: {protected['iv'].hex()}")
print(f"Signature: {protected['signature'].hex()}")

# 验证和解密数据
decrypted_data = protector.verify_and_decrypt(protected)
print(f"\nDecrypted data: {decrypted_data}")

import jwt
import time

class AuthorizationManager:
    def __init__(self, secret_key):
        self.secret_key = secret_key
        self.algorithms = ['HS256']

    def generate_token(self, user_id, roles, expiration_minutes=60):
        """生成 JWT 令牌"""
        payload = {
            'user_id': user_id,
            'roles': roles,
            'exp': time.time() + expiration_minutes * 60
        }
        return jwt.encode(payload, self.secret_key, algorithm='HS256')

    def validate_token(self, token):
        """验证 JWT 令牌"""
        try:
            payload = jwt.decode(token, self.secret_key, algorithms=self.algorithms)
            return payload
        except jwt.ExpiredSignatureError:
            raise PermissionError("Token expired")
        except jwt.InvalidTokenError:
            raise PermissionError("Invalid token")

    def check_permission(self, token, required_role):
        """检查用户是否有指定权限"""
        payload = self.validate_token(token)
        user_roles = payload.get('roles', [])
        return required_role in user_roles

# 在边缘节点中集成授权管理
class EdgeNode:
    def __init__(self, node_id, ip, port, data_center_url):
        # ... 现有初始化代码 ...

        # 添加授权管理器
        self.auth_manager = AuthorizationManager(secret_key='your-256-bit-secret')  # 生产环境中应使用安全存储的密钥

    def handle_request(self, client_socket):
        try:
            request = client_socket.recv(1024).decode()
            print(f"Received request: {request}")

            # 解析请求中的 JWT 令牌（简化示例）
            auth_header = request.split('\n').pop(0)  # 假设令牌在请求头部
            if not auth_header.startswith('Bearer '):
                raise PermissionError("Missing or invalid Authorization header")

            token = auth_header[7:]  # 获取令牌部分

            # 验证令牌并检查权限
            payload = self.auth_manager.validate_token(token)
            if not self.auth_manager.check_permission(token, 'data_access'):
                raise PermissionError("User lacks required permission")

            # 继续处理请求
            # ... 现有处理逻辑 ...

        except PermissionError as e:
            print(f"Authorization failed: {e}")
            client_socket.send(f"HTTP/1.1 403 Forbidden\r\nContent-Length: 0\r\n\r\n".encode())
        except Exception as e:
            print(f"Error handling request: {e}")
        finally:
            client_socket.close()

# 使用示例：生成令牌
auth_manager = AuthorizationManager(secret_key='your-256-bit-secret')
token = auth_manager.generate_token(user_id=1001, roles=['data_access', 'user'], expiration_minutes=30)
print(f"Generated token: {token}")

# 验证令牌
try:
    payload = auth_manager.validate_token(token)
    print(f"Token valid for user {payload['user_id']} with roles {payload['roles']}")
except PermissionError as e:
    print(f"Token validation failed: {e}")

class PrivacyManager:
    def __init__(self):
        self.user_consents = {}  # 存储用户同意状态
        self.anonymization_rules = {
            'ip_address': self.anonymize_ip,
            'geo_location': self.anonymize_geo_location,
            'user_id': self.anonymize_user_id
        }

    def request_consent(self, user_id, context):
        """请求用户同意（简化示例）"""
        # 实际应用中，这应该是一个交互式流程
        consents = {
            'data_collection': True,
            'marketing': False,
            'third_party_sharing': False
        }
        self.user_consents[user_id] = consents
        return consents

    def check_consent(self, user_id, purpose):
        """检查用户是否同意特定用途"""
        user_consents = self.user_consents.get(user_id, {})
        return user_consents.get(purpose, False)

    def anonymize_data(self, data, user_id=None):
        """匿名化数据"""
        anonymized = {}

        for key, value in data.items():
            # 检查是否需要匿名化
            if key in self.anonymization_rules:
                anonymized[key] = self.anonymization_rules[key](value)
            else:
                anonymized[key] = value

        # 如果提供用户 ID，检查同意状态并进一步处理
        if user_id and not self.check_consent(user_id, 'data_collection'):
            # 用户不同意数据收集，进一步匿名化处理
            anonymized = self.further_anonymization(anonymized)

        return anonymized

    def anonymize_ip(self, ip):
        """匿名化 IP 地址"""
        if '.' in ip:  # IPv4
            parts = ip.split('.')
            return f"{parts[0]}.{parts[1]}.x.x"
        elif ':' in ip:  # IPv6
            return ip[:ip.rfind(':')] + ':x:x'
        return 'x.x.x.x'

    def anonymize_geo_location(self, location):
        """匿名化地理位置"""
        if isinstance(location, dict) and 'latitude' in location and 'longitude' in location:
            # 粗粒度位置信息（精确到城市级别）
            return {
                'latitude': round(location['latitude'] / 10) * 10,  # 四舍五入到最近的 10 度
                'longitude': round(location['longitude'] / 10) * 10,
                'city': location.get('city', 'Unknown'),
                'country': location.get('country', 'Unknown')
            }
        return location

    def anonymize_user_id(self, user_id):
        """匿名化用户 ID"""
        # 简单哈希（实际应用中应使用更安全的不可逆哈希）
        return hash(user_id) % 2**32

    def further_anonymization(self, data):
        """当用户不同意数据收集时的进一步匿名化"""
        # 删除敏感字段
        sensitive_fields = ['user_id', 'device_model', 'session_duration']
        for field in sensitive_fields:
            if field in data:
                del data[field]

        # 重置地理位置为更粗粒度
        if 'geo_location' in data:
            data['geo_location'] = {
                'country': data['geo_location'].get('country', 'Unknown')
            }

        return data

# 在数据处理流程中集成隐私管理
privacy_manager = PrivacyManager()

# 模拟用户同意请求
user_id = 1001
context = {'platform': 'web', 'language': 'en'}
consents = privacy_manager.request_consent(user_id, context)

# 准备数据
original_data = {
    'ip_address': '192.168.1.100',
    'geo_location': {
        'latitude': 39.9042,
        'longitude': 116.4074,
        'city': 'Beijing',
        'country': 'CN'
    },
    'user_id': user_id,
    'device_model': 'iPhone 14 Pro',
    'session_duration': 180
}

# 匿名化数据
anonymized_data = privacy_manager.anonymize_data(original_data, user_id=user_id)

print("\nOriginal data:")
print(json.dumps(original_data, indent=2))
print("\nAnonymized data:")
print(json.dumps(anonymized_data, indent=2))