feat: implement AITBC mesh network deployment infrastructure
✅ Phase 0: Pre-implementation checklist completed - Environment configurations (dev/staging/production) - Directory structure setup (logs, backups, monitoring) - Virtual environment with dependencies ✅ Master deployment script created - Single command deployment with validation - Progress tracking and rollback capability - Health checks and deployment reporting ✅ Validation script created - Module import validation - Basic functionality testing - Configuration and script verification ✅ Implementation fixes - Fixed dataclass import in consensus keys - Fixed async function syntax in tests - Updated deployment script for virtual environment 🚀 Ready for deployment: ./scripts/deploy-mesh-network.sh dev
This commit is contained in:
aitbc
452
apps/blockchain-node/src/aitbc_chain/network/topology.py
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452
apps/blockchain-node/src/aitbc_chain/network/topology.py
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"""
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Network Topology Optimization
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Optimizes peer connection strategies for network performance
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"""
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import asyncio
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import networkx as nx
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import time
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from typing import Dict, List, Set, Tuple, Optional
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from dataclasses import dataclass
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from enum import Enum
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from .discovery import PeerNode, P2PDiscovery
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from .health import PeerHealthMonitor, HealthStatus
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class TopologyStrategy(Enum):
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SMALL_WORLD = "small_world"
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SCALE_FREE = "scale_free"
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MESH = "mesh"
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HYBRID = "hybrid"
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@dataclass
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class ConnectionWeight:
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source: str
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target: str
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weight: float
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latency: float
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bandwidth: float
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reliability: float
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class NetworkTopology:
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"""Manages and optimizes network topology"""
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def __init__(self, discovery: P2PDiscovery, health_monitor: PeerHealthMonitor):
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self.discovery = discovery
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self.health_monitor = health_monitor
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self.graph = nx.Graph()
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self.strategy = TopologyStrategy.HYBRID
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self.optimization_interval = 300 # 5 minutes
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self.max_degree = 8
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self.min_degree = 3
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self.running = False
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# Topology metrics
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self.avg_path_length = 0
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self.clustering_coefficient = 0
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self.network_efficiency = 0
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async def start_optimization(self):
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"""Start topology optimization service"""
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self.running = True
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log_info("Starting network topology optimization")
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# Initialize graph
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await self._build_initial_graph()
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while self.running:
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try:
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await self._optimize_topology()
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await self._calculate_metrics()
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await asyncio.sleep(self.optimization_interval)
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except Exception as e:
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log_error(f"Topology optimization error: {e}")
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await asyncio.sleep(30)
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async def stop_optimization(self):
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"""Stop topology optimization service"""
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self.running = False
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log_info("Stopping network topology optimization")
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async def _build_initial_graph(self):
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"""Build initial network graph from current peers"""
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self.graph.clear()
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# Add all peers as nodes
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for peer in self.discovery.get_peer_list():
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self.graph.add_node(peer.node_id, **{
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'address': peer.address,
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'port': peer.port,
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'reputation': peer.reputation,
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'capabilities': peer.capabilities
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})
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# Add edges based on current connections
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await self._add_connection_edges()
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async def _add_connection_edges(self):
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"""Add edges for current peer connections"""
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peers = self.discovery.get_peer_list()
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# In a real implementation, this would use actual connection data
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# For now, create a mesh topology
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for i, peer1 in enumerate(peers):
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for peer2 in peers[i+1:]:
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if self._should_connect(peer1, peer2):
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weight = await self._calculate_connection_weight(peer1, peer2)
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self.graph.add_edge(peer1.node_id, peer2.node_id, weight=weight)
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def _should_connect(self, peer1: PeerNode, peer2: PeerNode) -> bool:
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"""Determine if two peers should be connected"""
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# Check degree constraints
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if (self.graph.degree(peer1.node_id) >= self.max_degree or
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self.graph.degree(peer2.node_id) >= self.max_degree):
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return False
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# Check strategy-specific rules
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if self.strategy == TopologyStrategy.SMALL_WORLD:
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return self._small_world_should_connect(peer1, peer2)
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elif self.strategy == TopologyStrategy.SCALE_FREE:
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return self._scale_free_should_connect(peer1, peer2)
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elif self.strategy == TopologyStrategy.MESH:
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return self._mesh_should_connect(peer1, peer2)
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elif self.strategy == TopologyStrategy.HYBRID:
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return self._hybrid_should_connect(peer1, peer2)
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return False
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def _small_world_should_connect(self, peer1: PeerNode, peer2: PeerNode) -> bool:
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"""Small world topology connection logic"""
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# Connect to nearby peers and some random long-range connections
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import random
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if random.random() < 0.1: # 10% random connections
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return True
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# Connect based on geographic or network proximity (simplified)
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return random.random() < 0.3 # 30% of nearby connections
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def _scale_free_should_connect(self, peer1: PeerNode, peer2: PeerNode) -> bool:
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"""Scale-free topology connection logic"""
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# Prefer connecting to high-degree nodes (rich-get-richer)
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degree1 = self.graph.degree(peer1.node_id)
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degree2 = self.graph.degree(peer2.node_id)
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# Higher probability for nodes with higher degree
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connection_probability = (degree1 + degree2) / (2 * self.max_degree)
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return random.random() < connection_probability
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def _mesh_should_connect(self, peer1: PeerNode, peer2: PeerNode) -> bool:
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"""Full mesh topology connection logic"""
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# Connect to all peers (within degree limits)
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return True
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def _hybrid_should_connect(self, peer1: PeerNode, peer2: PeerNode) -> bool:
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"""Hybrid topology connection logic"""
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# Combine multiple strategies
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import random
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# 40% small world, 30% scale-free, 30% mesh
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strategy_choice = random.random()
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if strategy_choice < 0.4:
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return self._small_world_should_connect(peer1, peer2)
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elif strategy_choice < 0.7:
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return self._scale_free_should_connect(peer1, peer2)
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else:
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return self._mesh_should_connect(peer1, peer2)
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async def _calculate_connection_weight(self, peer1: PeerNode, peer2: PeerNode) -> float:
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"""Calculate connection weight between two peers"""
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# Get health metrics
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health1 = self.health_monitor.get_health_status(peer1.node_id)
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health2 = self.health_monitor.get_health_status(peer2.node_id)
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# Calculate weight based on health, reputation, and performance
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weight = 1.0
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if health1 and health2:
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# Factor in health scores
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weight *= (health1.health_score + health2.health_score) / 2
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# Factor in reputation
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weight *= (peer1.reputation + peer2.reputation) / 2
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# Factor in latency (inverse relationship)
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if health1 and health1.latency_ms > 0:
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weight *= min(1.0, 1000 / health1.latency_ms)
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return max(0.1, weight) # Minimum weight of 0.1
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async def _optimize_topology(self):
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"""Optimize network topology"""
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log_info("Optimizing network topology")
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# Analyze current topology
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await self._analyze_topology()
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# Identify optimization opportunities
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improvements = await self._identify_improvements()
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# Apply improvements
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for improvement in improvements:
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await self._apply_improvement(improvement)
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async def _analyze_topology(self):
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"""Analyze current network topology"""
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if len(self.graph.nodes()) == 0:
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return
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# Calculate basic metrics
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if nx.is_connected(self.graph):
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self.avg_path_length = nx.average_shortest_path_length(self.graph, weight='weight')
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else:
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self.avg_path_length = float('inf')
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self.clustering_coefficient = nx.average_clustering(self.graph)
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# Calculate network efficiency
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self.network_efficiency = nx.global_efficiency(self.graph)
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log_info(f"Topology metrics - Path length: {self.avg_path_length:.2f}, "
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f"Clustering: {self.clustering_coefficient:.2f}, "
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f"Efficiency: {self.network_efficiency:.2f}")
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async def _identify_improvements(self) -> List[Dict]:
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"""Identify topology improvements"""
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improvements = []
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# Check for disconnected nodes
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if not nx.is_connected(self.graph):
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components = list(nx.connected_components(self.graph))
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if len(components) > 1:
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improvements.append({
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'type': 'connect_components',
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'components': components
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})
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# Check degree distribution
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degrees = dict(self.graph.degree())
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low_degree_nodes = [node for node, degree in degrees.items() if degree < self.min_degree]
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high_degree_nodes = [node for node, degree in degrees.items() if degree > self.max_degree]
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if low_degree_nodes:
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improvements.append({
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'type': 'increase_degree',
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'nodes': low_degree_nodes
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})
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if high_degree_nodes:
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improvements.append({
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'type': 'decrease_degree',
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'nodes': high_degree_nodes
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})
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# Check for inefficient paths
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if self.avg_path_length > 6: # Too many hops
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improvements.append({
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'type': 'add_shortcuts',
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'target_path_length': 4
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})
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return improvements
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async def _apply_improvement(self, improvement: Dict):
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"""Apply topology improvement"""
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improvement_type = improvement['type']
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if improvement_type == 'connect_components':
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await self._connect_components(improvement['components'])
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elif improvement_type == 'increase_degree':
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await self._increase_node_degree(improvement['nodes'])
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elif improvement_type == 'decrease_degree':
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await self._decrease_node_degree(improvement['nodes'])
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elif improvement_type == 'add_shortcuts':
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await self._add_shortcuts(improvement['target_path_length'])
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async def _connect_components(self, components: List[Set[str]]):
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"""Connect disconnected components"""
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log_info(f"Connecting {len(components)} disconnected components")
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# Connect components by adding edges between representative nodes
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for i in range(len(components) - 1):
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component1 = list(components[i])
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component2 = list(components[i + 1])
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# Select best nodes to connect
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node1 = self._select_best_connection_node(component1)
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node2 = self._select_best_connection_node(component2)
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# Add connection
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if node1 and node2:
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peer1 = self.discovery.peers.get(node1)
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peer2 = self.discovery.peers.get(node2)
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if peer1 and peer2:
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await self._establish_connection(peer1, peer2)
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async def _increase_node_degree(self, nodes: List[str]):
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"""Increase degree of low-degree nodes"""
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for node_id in nodes:
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peer = self.discovery.peers.get(node_id)
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if not peer:
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continue
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# Find best candidates for connection
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candidates = await self._find_connection_candidates(peer, max_connections=2)
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for candidate_peer in candidates:
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await self._establish_connection(peer, candidate_peer)
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async def _decrease_node_degree(self, nodes: List[str]):
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"""Decrease degree of high-degree nodes"""
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for node_id in nodes:
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# Remove lowest quality connections
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edges = list(self.graph.edges(node_id, data=True))
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# Sort by weight (lowest first)
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edges.sort(key=lambda x: x[2].get('weight', 1.0))
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# Remove excess connections
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excess_count = self.graph.degree(node_id) - self.max_degree
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for i in range(min(excess_count, len(edges))):
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edge = edges[i]
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await self._remove_connection(edge[0], edge[1])
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async def _add_shortcuts(self, target_path_length: float):
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"""Add shortcut connections to reduce path length"""
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# Find pairs of nodes with long shortest paths
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all_pairs = dict(nx.all_pairs_shortest_path_length(self.graph))
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long_paths = []
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for node1, paths in all_pairs.items():
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for node2, distance in paths.items():
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if node1 != node2 and distance > target_path_length:
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long_paths.append((node1, node2, distance))
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# Sort by path length (longest first)
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long_paths.sort(key=lambda x: x[2], reverse=True)
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# Add shortcuts for longest paths
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for node1_id, node2_id, _ in long_paths[:5]: # Limit to 5 shortcuts
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peer1 = self.discovery.peers.get(node1_id)
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peer2 = self.discovery.peers.get(node2_id)
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if peer1 and peer2 and not self.graph.has_edge(node1_id, node2_id):
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await self._establish_connection(peer1, peer2)
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def _select_best_connection_node(self, nodes: List[str]) -> Optional[str]:
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"""Select best node for inter-component connection"""
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best_node = None
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best_score = 0
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for node_id in nodes:
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peer = self.discovery.peers.get(node_id)
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if not peer:
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continue
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# Score based on reputation and health
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health = self.health_monitor.get_health_status(node_id)
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score = peer.reputation
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if health:
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score *= health.health_score
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if score > best_score:
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best_score = score
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best_node = node_id
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return best_node
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async def _find_connection_candidates(self, peer: PeerNode, max_connections: int = 3) -> List[PeerNode]:
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"""Find best candidates for new connections"""
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candidates = []
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for candidate_peer in self.discovery.get_peer_list():
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if (candidate_peer.node_id == peer.node_id or
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self.graph.has_edge(peer.node_id, candidate_peer.node_id)):
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continue
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# Score candidate
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score = await self._calculate_connection_weight(peer, candidate_peer)
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candidates.append((candidate_peer, score))
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# Sort by score and return top candidates
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candidates.sort(key=lambda x: x[1], reverse=True)
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return [candidate for candidate, _ in candidates[:max_connections]]
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async def _establish_connection(self, peer1: PeerNode, peer2: PeerNode):
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"""Establish connection between two peers"""
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try:
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# In a real implementation, this would establish actual network connection
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weight = await self._calculate_connection_weight(peer1, peer2)
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self.graph.add_edge(peer1.node_id, peer2.node_id, weight=weight)
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log_info(f"Established connection between {peer1.node_id} and {peer2.node_id}")
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except Exception as e:
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log_error(f"Failed to establish connection between {peer1.node_id} and {peer2.node_id}: {e}")
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async def _remove_connection(self, node1_id: str, node2_id: str):
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"""Remove connection between two nodes"""
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try:
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if self.graph.has_edge(node1_id, node2_id):
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self.graph.remove_edge(node1_id, node2_id)
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log_info(f"Removed connection between {node1_id} and {node2_id}")
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except Exception as e:
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log_error(f"Failed to remove connection between {node1_id} and {node2_id}: {e}")
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def get_topology_metrics(self) -> Dict:
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"""Get current topology metrics"""
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return {
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'node_count': len(self.graph.nodes()),
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'edge_count': len(self.graph.edges()),
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'avg_degree': sum(dict(self.graph.degree()).values()) / len(self.graph.nodes()) if self.graph.nodes() else 0,
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'avg_path_length': self.avg_path_length,
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'clustering_coefficient': self.clustering_coefficient,
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'network_efficiency': self.network_efficiency,
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'is_connected': nx.is_connected(self.graph),
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'strategy': self.strategy.value
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}
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def get_visualization_data(self) -> Dict:
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"""Get data for network visualization"""
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nodes = []
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edges = []
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for node_id in self.graph.nodes():
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node_data = self.graph.nodes[node_id]
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peer = self.discovery.peers.get(node_id)
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nodes.append({
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'id': node_id,
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'address': node_data.get('address', ''),
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'reputation': node_data.get('reputation', 0),
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'degree': self.graph.degree(node_id)
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})
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for edge in self.graph.edges(data=True):
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edges.append({
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'source': edge[0],
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'target': edge[1],
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'weight': edge[2].get('weight', 1.0)
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})
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return {
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'nodes': nodes,
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'edges': edges
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}
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# Global topology manager
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topology_manager: Optional[NetworkTopology] = None
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def get_topology_manager() -> Optional[NetworkTopology]:
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"""Get global topology manager"""
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return topology_manager
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def create_topology_manager(discovery: P2PDiscovery, health_monitor: PeerHealthMonitor) -> NetworkTopology:
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"""Create and set global topology manager"""
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global topology_manager
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topology_manager = NetworkTopology(discovery, health_monitor)
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return topology_manager
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