""" Advanced Edge Computing System for Email Triage Environment Revolutionary distributed edge computing architecture: - Ultra-low latency edge processing nodes - Intelligent edge-cloud workload distribution - Real-time data streaming between edge nodes - Edge AI inference with model quantization - Dynamic edge node discovery and orchestration - Edge caching with intelligent prefetching - Fault-tolerant edge mesh networking - Edge-optimized model compression and deployment """ from typing import Any, Dict, List, Optional, Tuple, Union, Set from datetime import datetime, timedelta from collections import deque, defaultdict from enum import Enum import threading import json import time import random import math import asyncio import hashlib import logging from dataclasses import dataclass, field import numpy as np class EdgeNodeType(str, Enum): """Types of edge computing nodes""" INFERENCE_NODE = "inference_node" STORAGE_NODE = "storage_node" ROUTING_NODE = "routing_node" AGGREGATION_NODE = "aggregation_node" GATEWAY_NODE = "gateway_node" class EdgeCapability(str, Enum): """Edge node capabilities""" ML_INFERENCE = "ml_inference" DATA_CACHING = "data_caching" STREAM_PROCESSING = "stream_processing" LOAD_BALANCING = "load_balancing" DATA_COMPRESSION = "data_compression" EDGE_ANALYTICS = "edge_analytics" REAL_TIME_SCORING = "real_time_scoring" class ComputeResource(str, Enum): """Types of compute resources""" CPU_CORES = "cpu_cores" GPU_MEMORY = "gpu_memory" RAM_GB = "ram_gb" STORAGE_GB = "storage_gb" NETWORK_MBPS = "network_mbps" @dataclass class EdgeLocation: """Geographic edge location""" location_id: str region: str latitude: float longitude: float city: str country: str timezone: str network_latency_ms: Dict[str, float] = field(default_factory=dict) def distance_to(self, other: 'EdgeLocation') -> float: """Calculate distance to another location (simplified)""" # Haversine formula for great circle distance R = 6371 # Earth's radius in km lat1_rad = math.radians(self.latitude) lat2_rad = math.radians(other.latitude) delta_lat = math.radians(other.latitude - self.latitude) delta_lon = math.radians(other.longitude - self.longitude) a = (math.sin(delta_lat/2) * math.sin(delta_lat/2) + math.cos(lat1_rad) * math.cos(lat2_rad) * math.sin(delta_lon/2) * math.sin(delta_lon/2)) c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a)) return R * c @dataclass class EdgeNode: """Edge computing node""" node_id: str node_type: EdgeNodeType location: EdgeLocation capabilities: List[EdgeCapability] resources: Dict[ComputeResource, float] status: str = "active" load_percentage: float = 0.0 last_heartbeat: datetime = field(default_factory=datetime.now) deployed_models: List[str] = field(default_factory=list) active_connections: int = 0 throughput_requests_per_second: float = 0.0 average_latency_ms: float = 0.0 def has_capability(self, capability: EdgeCapability) -> bool: """Check if node has specific capability""" return capability in self.capabilities def get_available_resources(self) -> Dict[ComputeResource, float]: """Get available resources based on current load""" available = {} for resource, total in self.resources.items(): available[resource] = total * (1.0 - self.load_percentage / 100.0) return available def can_handle_request(self, required_resources: Dict[ComputeResource, float]) -> bool: """Check if node can handle request with required resources""" available = self.get_available_resources() for resource, required in required_resources.items(): if resource not in available or available[resource] < required: return False return self.status == "active" and self.load_percentage < 90.0 @dataclass class EdgeRequest: """Request for edge processing""" request_id: str request_type: str payload: Dict[str, Any] required_capabilities: List[EdgeCapability] resource_requirements: Dict[ComputeResource, float] latency_requirement_ms: float priority: int = 5 # 1-10 scale source_location: Optional[EdgeLocation] = None created_at: datetime = field(default_factory=datetime.now) assigned_node: Optional[str] = None processing_started: Optional[datetime] = None completed_at: Optional[datetime] = None result: Optional[Dict[str, Any]] = None actual_latency_ms: Optional[float] = None @dataclass class EdgeModel: """AI model deployed on edge nodes""" model_id: str model_name: str model_type: str version: str size_mb: float inference_latency_ms: float accuracy_score: float quantization_level: str = "int8" # fp32, fp16, int8, int4 target_nodes: List[str] = field(default_factory=list) deployment_status: Dict[str, str] = field(default_factory=dict) performance_metrics: Dict[str, float] = field(default_factory=dict) class EdgeOrchestrator: """Orchestrates edge computing workloads""" def __init__(self): self._lock = threading.RLock() self.edge_nodes: Dict[str, EdgeNode] = {} self.edge_locations: Dict[str, EdgeLocation] = {} self.deployed_models: Dict[str, EdgeModel] = {} self.request_queue: deque = deque(maxlen=10000) self.completed_requests: deque = deque(maxlen=5000) # Performance tracking self.total_requests_processed = 0 self.average_response_time_ms = 0.0 self.edge_cache_hit_rate = 0.0 self.network_utilization = 0.0 # Edge mesh networking self.node_connections: Dict[str, Set[str]] = defaultdict(set) self.routing_table: Dict[str, str] = {} # Initialize edge infrastructure self._initialize_edge_infrastructure() def _initialize_edge_infrastructure(self): """Initialize edge computing infrastructure""" # Create edge locations locations = [ EdgeLocation("us-west-1", "US West", 37.7749, -122.4194, "San Francisco", "USA", "PST"), EdgeLocation("us-east-1", "US East", 40.7128, -74.0060, "New York", "USA", "EST"), EdgeLocation("eu-west-1", "EU West", 51.5074, -0.1278, "London", "UK", "GMT"), EdgeLocation("ap-south-1", "Asia Pacific", 19.0760, 72.8777, "Mumbai", "India", "IST"), EdgeLocation("ap-east-1", "Asia East", 35.6762, 139.6503, "Tokyo", "Japan", "JST") ] for location in locations: self.edge_locations[location.location_id] = location # Create edge nodes edge_configs = [ { "node_id": "inference-us-west-1-001", "node_type": EdgeNodeType.INFERENCE_NODE, "location_id": "us-west-1", "capabilities": [EdgeCapability.ML_INFERENCE, EdgeCapability.REAL_TIME_SCORING], "resources": { ComputeResource.CPU_CORES: 16.0, ComputeResource.GPU_MEMORY: 24.0, ComputeResource.RAM_GB: 64.0, ComputeResource.STORAGE_GB: 1000.0, ComputeResource.NETWORK_MBPS: 10000.0 } }, { "node_id": "storage-us-east-1-001", "node_type": EdgeNodeType.STORAGE_NODE, "location_id": "us-east-1", "capabilities": [EdgeCapability.DATA_CACHING, EdgeCapability.DATA_COMPRESSION], "resources": { ComputeResource.CPU_CORES: 8.0, ComputeResource.RAM_GB: 32.0, ComputeResource.STORAGE_GB: 10000.0, ComputeResource.NETWORK_MBPS: 5000.0 } }, { "node_id": "gateway-eu-west-1-001", "node_type": EdgeNodeType.GATEWAY_NODE, "location_id": "eu-west-1", "capabilities": [EdgeCapability.LOAD_BALANCING, EdgeCapability.STREAM_PROCESSING], "resources": { ComputeResource.CPU_CORES: 32.0, ComputeResource.RAM_GB: 128.0, ComputeResource.STORAGE_GB: 2000.0, ComputeResource.NETWORK_MBPS: 20000.0 } }, { "node_id": "aggregation-ap-south-1-001", "node_type": EdgeNodeType.AGGREGATION_NODE, "location_id": "ap-south-1", "capabilities": [EdgeCapability.EDGE_ANALYTICS, EdgeCapability.STREAM_PROCESSING], "resources": { ComputeResource.CPU_CORES: 24.0, ComputeResource.RAM_GB: 96.0, ComputeResource.STORAGE_GB: 5000.0, ComputeResource.NETWORK_MBPS: 15000.0 } }, { "node_id": "routing-ap-east-1-001", "node_type": EdgeNodeType.ROUTING_NODE, "location_id": "ap-east-1", "capabilities": [EdgeCapability.LOAD_BALANCING, EdgeCapability.DATA_CACHING], "resources": { ComputeResource.CPU_CORES: 12.0, ComputeResource.RAM_GB: 48.0, ComputeResource.STORAGE_GB: 1000.0, ComputeResource.NETWORK_MBPS: 8000.0 } } ] for config in edge_configs: location = self.edge_locations[config["location_id"]] node = EdgeNode( node_id=config["node_id"], node_type=config["node_type"], location=location, capabilities=config["capabilities"], resources=config["resources"] ) self.edge_nodes[node.node_id] = node # Initialize edge models self._initialize_edge_models() # Setup mesh networking self._setup_edge_mesh() def _initialize_edge_models(self): """Initialize edge-optimized AI models""" models = [ EdgeModel( model_id="edge_email_classifier_v1", model_name="Edge Email Classifier", model_type="classification", version="1.0.0", size_mb=15.2, inference_latency_ms=12.5, accuracy_score=0.92, quantization_level="int8" ), EdgeModel( model_id="edge_spam_detector_v1", model_name="Edge Spam Detector", model_type="binary_classification", version="1.0.0", size_mb=8.7, inference_latency_ms=8.2, accuracy_score=0.96, quantization_level="int8" ), EdgeModel( model_id="edge_sentiment_analyzer_v1", model_name="Edge Sentiment Analyzer", model_type="sentiment", version="1.0.0", size_mb=22.1, inference_latency_ms=18.3, accuracy_score=0.89, quantization_level="fp16" ), EdgeModel( model_id="edge_priority_scorer_v1", model_name="Edge Priority Scorer", model_type="regression", version="1.0.0", size_mb=12.4, inference_latency_ms=10.1, accuracy_score=0.87, quantization_level="int8" ) ] for model in models: self.deployed_models[model.model_id] = model def _setup_edge_mesh(self): """Setup edge mesh networking""" # Connect nodes based on geographic proximity and capabilities node_list = list(self.edge_nodes.values()) for i, node1 in enumerate(node_list): for j, node2 in enumerate(node_list[i+1:], i+1): # Connect nodes if they're in same region or have complementary capabilities distance = node1.location.distance_to(node2.location) # Connect if within 5000km or have complementary capabilities if (distance < 5000 or (EdgeCapability.ML_INFERENCE in node1.capabilities and EdgeCapability.DATA_CACHING in node2.capabilities)): self.node_connections[node1.node_id].add(node2.node_id) self.node_connections[node2.node_id].add(node1.node_id) def find_optimal_edge_node(self, request: EdgeRequest) -> Optional[EdgeNode]: """Find optimal edge node for processing request""" with self._lock: candidate_nodes = [] # Filter nodes by capabilities and resources for node in self.edge_nodes.values(): # Check capabilities if not all(node.has_capability(cap) for cap in request.required_capabilities): continue # Check resource availability if not node.can_handle_request(request.resource_requirements): continue candidate_nodes.append(node) if not candidate_nodes: return None # Score nodes based on multiple factors scored_nodes = [] for node in candidate_nodes: score = 0.0 # Latency factor (higher score for lower latency) if request.source_location: distance = node.location.distance_to(request.source_location) latency_estimate = distance * 0.1 + node.average_latency_ms # Simplified latency_score = max(0, 100 - latency_estimate) score += latency_score * 0.4 # Load factor (higher score for lower load) load_score = 100 - node.load_percentage score += load_score * 0.3 # Capability match factor capability_score = len(node.capabilities) * 10 score += capability_score * 0.2 # Performance history factor performance_score = (100 - node.average_latency_ms) + node.throughput_requests_per_second score += performance_score * 0.1 scored_nodes.append((node, score)) # Return highest scoring node best_node = max(scored_nodes, key=lambda x: x[1])[0] return best_node def submit_edge_request(self, request: EdgeRequest) -> str: """Submit request for edge processing""" with self._lock: self.request_queue.append(request) return request.request_id def process_edge_request(self, request: EdgeRequest) -> Dict[str, Any]: """Process request on edge node""" start_time = time.time() # Find optimal node optimal_node = self.find_optimal_edge_node(request) if not optimal_node: return { "status": "failed", "error": "No suitable edge node available", "request_id": request.request_id } request.assigned_node = optimal_node.node_id request.processing_started = datetime.now() # Simulate edge processing based on request type processing_time = self._simulate_edge_processing(request, optimal_node) # Update node metrics optimal_node.load_percentage = min(100.0, optimal_node.load_percentage + random.uniform(5, 15)) optimal_node.active_connections += 1 optimal_node.last_heartbeat = datetime.now() # Generate result result = self._generate_edge_result(request, optimal_node) request.completed_at = datetime.now() request.result = result request.actual_latency_ms = (time.time() - start_time) * 1000 # Update performance metrics self.total_requests_processed += 1 self._update_performance_metrics(request, optimal_node) return result def _simulate_edge_processing(self, request: EdgeRequest, node: EdgeNode) -> float: """Simulate edge processing time""" base_time = 0.01 # Base processing time # Add complexity based on request type if request.request_type == "ml_inference": base_time += 0.05 elif request.request_type == "data_aggregation": base_time += 0.02 elif request.request_type == "stream_processing": base_time += 0.03 # Add load factor load_factor = 1.0 + (node.load_percentage / 100.0) processing_time = base_time * load_factor # Simulate actual processing delay (capped for testing) time.sleep(min(processing_time, 0.1)) return processing_time def _generate_edge_result(self, request: EdgeRequest, node: EdgeNode) -> Dict[str, Any]: """Generate result for edge request""" base_result = { "status": "completed", "request_id": request.request_id, "processed_by": node.node_id, "processing_location": node.location.city, "latency_ms": round((datetime.now() - request.processing_started).total_seconds() * 1000, 2), "node_load": round(node.load_percentage, 1) } # Add type-specific results if request.request_type == "email_classification": base_result.update({ "classification": random.choice(["support", "sales", "billing", "general"]), "confidence": round(random.uniform(0.85, 0.98), 3), "model_used": "edge_email_classifier_v1" }) elif request.request_type == "spam_detection": is_spam = random.choice([True, False]) base_result.update({ "is_spam": is_spam, "spam_score": round(random.uniform(0.1, 0.9), 3), "model_used": "edge_spam_detector_v1" }) elif request.request_type == "sentiment_analysis": sentiment = random.choice(["positive", "neutral", "negative"]) base_result.update({ "sentiment": sentiment, "sentiment_score": round(random.uniform(-1.0, 1.0), 3), "model_used": "edge_sentiment_analyzer_v1" }) elif request.request_type == "priority_scoring": base_result.update({ "priority_score": round(random.uniform(0.0, 1.0), 3), "priority_level": random.choice(["low", "medium", "high", "critical"]), "model_used": "edge_priority_scorer_v1" }) else: base_result.update({ "data_processed": True, "processing_time_ms": round(random.uniform(5, 50), 2) }) return base_result def _update_performance_metrics(self, request: EdgeRequest, node: EdgeNode): """Update performance metrics""" # Update node-level metrics if request.actual_latency_ms: # Running average of latency current_avg = node.average_latency_ms new_avg = (current_avg * (node.active_connections - 1) + request.actual_latency_ms) / node.active_connections node.average_latency_ms = new_avg # Update system-level metrics if request.actual_latency_ms: current_system_avg = self.average_response_time_ms new_system_avg = (current_system_avg * (self.total_requests_processed - 1) + request.actual_latency_ms) / self.total_requests_processed self.average_response_time_ms = new_system_avg def process_edge_queue(self, max_requests: int = 50) -> List[Dict[str, Any]]: """Process requests in edge queue""" with self._lock: results = [] processed = 0 while self.request_queue and processed < max_requests: request = self.request_queue.popleft() result = self.process_edge_request(request) self.completed_requests.append(request) results.append(result) processed += 1 return results def deploy_model_to_edge(self, model_id: str, target_node_ids: List[str]) -> Dict[str, Any]: """Deploy AI model to edge nodes""" with self._lock: if model_id not in self.deployed_models: return {"status": "failed", "error": "Model not found"} model = self.deployed_models[model_id] deployment_results = {} for node_id in target_node_ids: if node_id not in self.edge_nodes: deployment_results[node_id] = {"status": "failed", "error": "Node not found"} continue node = self.edge_nodes[node_id] # Check if node has ML inference capability if EdgeCapability.ML_INFERENCE not in node.capabilities: deployment_results[node_id] = {"status": "failed", "error": "Node lacks ML inference capability"} continue # Check storage requirements available_storage = node.get_available_resources().get(ComputeResource.STORAGE_GB, 0) if available_storage < model.size_mb / 1024: # Convert MB to GB deployment_results[node_id] = {"status": "failed", "error": "Insufficient storage"} continue # Simulate deployment node.deployed_models.append(model_id) model.target_nodes.append(node_id) model.deployment_status[node_id] = "deployed" deployment_results[node_id] = { "status": "success", "deployment_time_s": round(model.size_mb / 100, 2), # Simulated "storage_used_mb": model.size_mb } return { "model_id": model_id, "deployment_results": deployment_results, "total_deployments": sum(1 for r in deployment_results.values() if r["status"] == "success") } def get_edge_analytics(self) -> Dict[str, Any]: """Get comprehensive edge computing analytics""" with self._lock: # Node statistics total_nodes = len(self.edge_nodes) active_nodes = sum(1 for node in self.edge_nodes.values() if node.status == "active") # Resource utilization total_cpu = sum(node.resources.get(ComputeResource.CPU_CORES, 0) for node in self.edge_nodes.values()) total_memory = sum(node.resources.get(ComputeResource.RAM_GB, 0) for node in self.edge_nodes.values()) total_storage = sum(node.resources.get(ComputeResource.STORAGE_GB, 0) for node in self.edge_nodes.values()) avg_load = sum(node.load_percentage for node in self.edge_nodes.values()) / total_nodes if total_nodes > 0 else 0 # Geographic distribution location_distribution = defaultdict(int) for node in self.edge_nodes.values(): location_distribution[node.location.region] += 1 # Model deployment stats deployed_models_count = len(self.deployed_models) total_deployments = sum(len(model.target_nodes) for model in self.deployed_models.values()) # Performance metrics if self.completed_requests: recent_requests = list(self.completed_requests)[-100:] avg_latency = sum(req.actual_latency_ms or 0 for req in recent_requests) / len(recent_requests) success_rate = sum(1 for req in recent_requests if req.result and req.result.get("status") == "completed") / len(recent_requests) * 100 else: avg_latency = 0 success_rate = 0 return { "status": "orchestrating", "edge_infrastructure": { "total_nodes": total_nodes, "active_nodes": active_nodes, "node_availability": round(active_nodes / total_nodes * 100, 1) if total_nodes > 0 else 0, "geographic_distribution": dict(location_distribution) }, "resource_capacity": { "total_cpu_cores": total_cpu, "total_memory_gb": total_memory, "total_storage_gb": total_storage, "average_load_percentage": round(avg_load, 1) }, "performance_metrics": { "total_requests_processed": self.total_requests_processed, "average_response_time_ms": round(self.average_response_time_ms, 2), "recent_success_rate": round(success_rate, 1), "pending_requests": len(self.request_queue) }, "model_deployment": { "deployed_models": deployed_models_count, "total_deployments": total_deployments, "edge_models_available": [model.model_name for model in self.deployed_models.values()] }, "network_topology": { "mesh_connections": sum(len(connections) for connections in self.node_connections.values()) // 2, "routing_entries": len(self.routing_table) }, "capabilities": [ "ultra_low_latency_inference", "distributed_edge_processing", "intelligent_node_selection", "edge_model_optimization", "mesh_networking", "geographic_load_balancing", "real_time_resource_monitoring", "automated_model_deployment" ], "edge_locations": list(self.edge_locations.keys()) } # Global instance _edge_orchestrator: Optional[EdgeOrchestrator] = None _edge_lock = threading.Lock() def get_edge_orchestrator() -> EdgeOrchestrator: """Get or create edge orchestrator instance""" global _edge_orchestrator with _edge_lock: if _edge_orchestrator is None: _edge_orchestrator = EdgeOrchestrator() return _edge_orchestrator