核心架构与特性

系统架构概览

LLMESH 采用分层的去中心化架构，确保网络的可扩展性、安全性和高可用性。

┌─────────────────────────────────────┐
│          应用层 (Application Layer)   │
│  Web UI │ API Gateway │ Mobile App  │
├─────────────────────────────────────┤
│        服务层 (Service Layer)        │
│  路由服务 │ 负载均衡 │ 模型管理       │
├─────────────────────────────────────┤
│        网络层 (Network Layer)        │
│  P2P 协议 │ 消息传递 │ 节点发现       │
├─────────────────────────────────────┤
│        存储层 (Storage Layer)        │
│  模型存储 │ 元数据 │ 缓存管理         │
└─────────────────────────────────────┘

核心特性

🌐 真正的 P2P 架构

LLMESH 实现了完全去中心化的点对点架构，每个节点都具有以下能力：

• 模型托管：运行和提供 LLM 推理服务

• 请求路由：智能转发用户请求到最优节点

• 网络维护：参与网络拓扑的维护和优化

节点类型

class NodeType(Enum):
    FULL_NODE = "full"      # 完整节点：提供所有服务
    LIGHT_NODE = "light"    # 轻量节点：仅路由和缓存
    COMPUTE_NODE = "compute" # 计算节点：专注模型推理
    STORAGE_NODE = "storage" # 存储节点：专注模型存储

🔄 自愈网络机制

网络具备强大的自愈能力，能够自动应对各种故障情况：

故障检测算法

class HealthChecker:
    def __init__(self, check_interval=30):
        self.check_interval = check_interval
        self.failed_nodes = set()
    
    async def monitor_network(self):
        """持续监控网络健康状态"""
        while True:
            await self.check_all_peers()
            await asyncio.sleep(self.check_interval)
    
    async def check_node_health(self, node_id):
        """检查单个节点健康状态"""
        try:
            response = await self.ping_node(node_id, timeout=5)
            if response.status == "healthy":
                self.failed_nodes.discard(node_id)
                return True
            else:
                self.failed_nodes.add(node_id)
                await self.trigger_rerouting(node_id)
                return False
        except TimeoutError:
            self.failed_nodes.add(node_id)
            await self.trigger_rerouting(node_id)
            return False

自动重路由机制

class RoutingManager:
    def __init__(self):
        self.routing_table = {}
        self.backup_routes = {}
    
    async def find_alternative_route(self, failed_node, target_service):
        """寻找替代路由"""
        candidates = []
        for node_id, node_info in self.routing_table.items():
            if (node_id != failed_node 
                and target_service in node_info.services
                and node_info.load < 0.8):
                candidates.append((node_id, node_info.latency))
        
        # 按延迟排序，选择最优节点
        candidates.sort(key=lambda x: x[1])
        return candidates[0][0] if candidates else None

⚖️ 动态负载均衡

LLMESH 实现了智能的负载均衡机制，确保请求能够高效分发：

负载均衡算法

class LoadBalancer:
    def __init__(self):
        self.nodes = {}
        self.request_history = []
    
    def select_node(self, request_type, model_name):
        """基于多因素选择最优节点"""
        eligible_nodes = self.filter_capable_nodes(request_type, model_name)
        
        if not eligible_nodes:
            raise NoAvailableNodeError()
        
        # 综合评分算法
        best_node = None
        best_score = float('inf')
        
        for node in eligible_nodes:
            score = self.calculate_node_score(node)
            if score < best_score:
                best_score = score
                best_node = node
        
        return best_node
    
    def calculate_node_score(self, node):
        """计算节点综合评分（越低越好）"""
        latency_weight = 0.4
        load_weight = 0.3
        cost_weight = 0.2
        reputation_weight = 0.1
        
        score = (
            node.avg_latency * latency_weight +
            node.current_load * load_weight +
            node.service_cost * cost_weight +
            (1 - node.reputation_score) * reputation_weight
        )
        
        return score

🔒 隐私优先设计

所有通信都采用端到端加密，确保用户数据安全：

加密通信实现

import cryptography
from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC

class SecureCommunication:
    def __init__(self):
        self.private_key = self.generate_private_key()
        self.public_key = self.private_key.public_key()
    
    def encrypt_message(self, message: str, recipient_public_key) -> bytes:
        """加密消息"""
        # 生成会话密钥
        session_key = Fernet.generate_key()
        fernet = Fernet(session_key)
        
        # 加密消息内容
        encrypted_message = fernet.encrypt(message.encode())
        
        # 使用接收方公钥加密会话密钥
        encrypted_session_key = recipient_public_key.encrypt(
            session_key,
            padding.OAEP(
                mgf=padding.MGF1(algorithm=hashes.SHA256()),
                algorithm=hashes.SHA256(),
                label=None
            )
        )
        
        return {
            'encrypted_session_key': encrypted_session_key,
            'encrypted_message': encrypted_message
        }
    
    def decrypt_message(self, encrypted_data: dict) -> str:
        """解密消息"""
        # 解密会话密钥
        session_key = self.private_key.decrypt(
            encrypted_data['encrypted_session_key'],
            padding.OAEP(
                mgf=padding.MGF1(algorithm=hashes.SHA256()),
                algorithm=hashes.SHA256(),
                label=None
            )
        )
        
        # 解密消息内容
        fernet = Fernet(session_key)
        decrypted_message = fernet.decrypt(encrypted_data['encrypted_message'])
        
        return decrypted_message.decode()

网络协议

节点发现协议

class NodeDiscovery:
    def __init__(self, node_id):
        self.node_id = node_id
        self.known_peers = set()
        self.discovery_interval = 60
    
    async def bootstrap(self, bootstrap_nodes):
        """从引导节点开始发现网络"""
        for bootstrap_node in bootstrap_nodes:
            try:
                peers = await self.request_peer_list(bootstrap_node)
                self.known_peers.update(peers)
            except ConnectionError:
                continue
    
    async def periodic_discovery(self):
        """定期发现新节点"""
        while True:
            await self.discover_new_peers()
            await asyncio.sleep(self.discovery_interval)
    
    async def discover_new_peers(self):
        """通过现有节点发现新节点"""
        for peer in list(self.known_peers):
            try:
                new_peers = await self.request_peer_list(peer)
                self.known_peers.update(new_peers)
            except Exception:
                self.known_peers.discard(peer)

消息传递协议

class MessageProtocol:
    def __init__(self):
        self.message_handlers = {}
        self.pending_responses = {}
    
    def register_handler(self, message_type, handler):
        """注册消息处理器"""
        self.message_handlers[message_type] = handler
    
    async def send_request(self, target_node, message_type, payload):
        """发送请求消息"""
        message_id = self.generate_message_id()
        message = {
            'id': message_id,
            'type': message_type,
            'payload': payload,
            'timestamp': time.time(),
            'sender': self.node_id
        }
        
        await self.send_message(target_node, message)
        
        # 等待响应
        response = await self.wait_for_response(message_id, timeout=30)
        return response
    
    async def handle_incoming_message(self, message):
        """处理收到的消息"""
        message_type = message.get('type')
        
        if message_type in self.message_handlers:
            handler = self.message_handlers[message_type]
            response = await handler(message)
            
            if response:
                await self.send_response(message, response)

性能优化

缓存机制

class ModelCache:
    def __init__(self, max_size=1000):
        self.cache = {}
        self.max_size = max_size
        self.access_times = {}
    
    def get(self, cache_key):
        """获取缓存结果"""
        if cache_key in self.cache:
            self.access_times[cache_key] = time.time()
            return self.cache[cache_key]
        return None
    
    def set(self, cache_key, result):
        """设置缓存结果"""
        if len(self.cache) >= self.max_size:
            self.evict_oldest()
        
        self.cache[cache_key] = result
        self.access_times[cache_key] = time.time()
    
    def evict_oldest(self):
        """移除最久未使用的缓存项"""
        oldest_key = min(self.access_times.keys(), 
                        key=lambda k: self.access_times[k])
        del self.cache[oldest_key]
        del self.access_times[oldest_key]

通过这些核心特性和架构设计，LLMESH 网络能够提供高可用、高性能、安全可靠的去中心化 AI 服务。