Edge Intelligence Orchestration

# Edge Intelligence Orchestration System
import numpy as np
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
import asyncio
import torch
import torch.nn as nn
from concurrent.futures import ThreadPoolExecutor

@dataclass
class EdgeNode:
    node_id: str
    compute_capacity: float  # GFLOPS
    memory_capacity: float   # GB
    network_bandwidth: float # Mbps
    location: Tuple[float, float]  # lat, lon
    current_load: float
    model_version: str
    last_update: float

@dataclass
class MLModel:
    model_id: str
    architecture: nn.Module
    size_mb: float
    accuracy: float
    latency_ms: float
    quantized_versions: Dict[str, nn.Module]

class EdgeIntelligenceOrchestrator:
    def __init__(self, config: Dict):
        self.edge_nodes: Dict[str, EdgeNode] = {}
        self.models: Dict[str, MLModel] = {}
        self.federated_aggregator = FederatedAggregator(config)
        self.workload_scheduler = WorkloadScheduler(config)
        self.model_optimizer = ModelOptimizer(config)
        self.monitoring = EdgeMonitoring(config)
        
    async def register_edge_node(self, node: EdgeNode) -> bool:
        """Register new edge node in the orchestration system"""
        
        # Validate node capabilities
        if not self.validate_node_capabilities(node):
            return False
        
        # Add to registry
        self.edge_nodes[node.node_id] = node
        
        # Deploy appropriate model version
        optimal_model = await self.select_optimal_model(node)
        await self.deploy_model_to_node(optimal_model, node)
        
        # Initialize monitoring
        self.monitoring.start_node_monitoring(node.node_id)
        
        return True
    
    async def process_inference_request(
        self,
        data: np.ndarray,
        requirements: Dict
    ) -> Dict:
        """Process inference request with edge-cloud distribution"""
        
        # Determine optimal processing location
        processing_plan = await self.workload_scheduler.create_plan(
            data_size=data.nbytes,
            latency_requirement=requirements.get('max_latency_ms', 100),
            accuracy_requirement=requirements.get('min_accuracy', 0.9)
        )
        
        if processing_plan['location'] == 'edge':
            # Process at edge
            edge_node = self.select_best_edge_node(requirements)
            result = await self.execute_edge_inference(
                edge_node, data, processing_plan['model']
            )
        elif processing_plan['location'] == 'hybrid':
            # Split processing between edge and cloud
            result = await self.execute_hybrid_inference(
                data, processing_plan
            )
        else:
            # Process in cloud
            result = await self.execute_cloud_inference(
                data, processing_plan['model']
            )
        
        # Update metrics
        self.monitoring.record_inference(
            latency=result['latency'],
            accuracy=result['confidence'],
            location=processing_plan['location']
        )
        
        return result
    
    async def execute_federated_learning_round(self) -> Dict:
        """Coordinate federated learning across edge nodes"""
        
        # Select participating nodes
        participants = self.select_federated_participants()
        
        # Distribute current global model
        global_model = self.models['global_model']
        model_updates = []
        
        # Parallel training on edge nodes
        training_tasks = []
        for node in participants:
            task = asyncio.create_task(
                self.train_on_edge_node(node, global_model)
            )
            training_tasks.append(task)
        
        # Collect model updates
        model_updates = await asyncio.gather(*training_tasks)
        
        # Aggregate updates using FedAvg or FedProx
        aggregated_model = await self.federated_aggregator.aggregate(
            global_model,
            model_updates,
            aggregation_method='fedavg'
        )
        
        # Update global model
        self.models['global_model'] = aggregated_model
        
        # Distribute updated model to edge nodes
        await self.distribute_updated_model(aggregated_model)
        
        return {
            'round_complete': True,
            'participants': len(participants),
            'model_improvement': self.calculate_improvement(
                global_model, aggregated_model
            )
        }
    
    async def train_on_edge_node(
        self,
        node: EdgeNode,
        global_model: MLModel
    ) -> Dict:
        """Execute local training on edge node"""
        
        # Get local data (privacy-preserved)
        local_data = await self.get_node_local_data(node.node_id)
        
        # Initialize local model
        local_model = self.create_local_model_copy(global_model)
        
        # Local training loop
        optimizer = torch.optim.SGD(
            local_model.architecture.parameters(),
            lr=0.01
        )
        
        for epoch in range(5):  # Local epochs
            for batch in local_data:
                # Forward pass
                outputs = local_model.architecture(batch['input'])
                loss = nn.CrossEntropyLoss()(outputs, batch['labels'])
                
                # Backward pass
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
        
        # Calculate model delta
        model_delta = self.calculate_model_delta(
            global_model.architecture,
            local_model.architecture
        )
        
        return {
            'node_id': node.node_id,
            'model_delta': model_delta,
            'samples_trained': len(local_data),
            'final_loss': loss.item()
        }
    
    async def optimize_model_for_edge(
        self,
        model: MLModel,
        target_node: EdgeNode
    ) -> MLModel:
        """Optimize model for specific edge device capabilities"""
        
        optimized_model = model
        
        # Quantization based on device capability
        if target_node.compute_capacity < 10:  # Low-end device
            optimized_model = await self.model_optimizer.quantize(
                model,
                bits=8,
                method='dynamic'
            )
        
        # Model pruning for memory constraints
        if target_node.memory_capacity < model.size_mb / 1024:
            optimized_model = await self.model_optimizer.prune(
                optimized_model,
                sparsity=0.5
            )
        
        # Knowledge distillation for extreme constraints
        if target_node.compute_capacity < 5:
            optimized_model = await self.model_optimizer.distill(
                teacher_model=model,
                student_architecture='mobilenet'
            )
        
        return optimized_model
    
    def select_best_edge_node(self, requirements: Dict) -> EdgeNode:
        """Select optimal edge node based on requirements"""
        
        best_node = None
        best_score = float('inf')
        
        for node in self.edge_nodes.values():
            # Calculate scoring based on multiple factors
            latency_score = self.estimate_latency(node)
            load_score = node.current_load
            capability_score = 1.0 / node.compute_capacity
            
            # Weighted scoring
            total_score = (
                latency_score * requirements.get('latency_weight', 0.5) +
                load_score * requirements.get('load_weight', 0.3) +
                capability_score * requirements.get('capability_weight', 0.2)
            )
            
            if total_score < best_score:
                best_score = total_score
                best_node = node
        
        return best_node
    
    async def handle_model_drift(self, node_id: str, drift_metrics: Dict):
        """Handle model drift detected at edge node"""
        
        drift_severity = drift_metrics['severity']
        
        if drift_severity > 0.8:
            # Critical drift - immediate retraining
            await self.trigger_targeted_retraining(node_id)
        elif drift_severity > 0.5:
            # Moderate drift - schedule federated learning
            await self.schedule_federated_round([node_id])
        else:
            # Minor drift - collect more data
            await self.increase_monitoring_frequency(node_id)

class FederatedAggregator:
    def __init__(self, config: Dict):
        self.aggregation_method = config.get('method', 'fedavg')
        self.client_fraction = config.get('client_fraction', 0.1)
        
    async def aggregate(
        self,
        global_model: MLModel,
        client_updates: List[Dict],
        aggregation_method: str = 'fedavg'
    ) -> MLModel:
        """Aggregate model updates from edge nodes"""
        
        if aggregation_method == 'fedavg':
            return await self.federated_averaging(global_model, client_updates)
        elif aggregation_method == 'fedprox':
            return await self.federated_proximal(global_model, client_updates)
        else:
            raise ValueError(f"Unknown aggregation method: {aggregation_method}")
    
    async def federated_averaging(
        self,
        global_model: MLModel,
        client_updates: List[Dict]
    ) -> MLModel:
        """FedAvg aggregation algorithm"""
        
        # Initialize aggregated parameters
        aggregated_state = global_model.architecture.state_dict()
        
        # Calculate total samples
        total_samples = sum(update['samples_trained'] for update in client_updates)
        
        # Weighted averaging of model deltas
        for key in aggregated_state.keys():
            weighted_sum = torch.zeros_like(aggregated_state[key])
            
            for update in client_updates:
                weight = update['samples_trained'] / total_samples
                weighted_sum += update['model_delta'][key] * weight
            
            aggregated_state[key] += weighted_sum
        
        # Create new model with aggregated parameters
        new_model = self.create_model_copy(global_model)
        new_model.architecture.load_state_dict(aggregated_state)
        
        return new_model

class WorkloadScheduler:
    def __init__(self, config: Dict):
        self.scheduling_policy = config.get('policy', 'latency_aware')
        self.edge_threshold = config.get('edge_threshold', 50)  # ms
        
    async def create_plan(
        self,
        data_size: int,
        latency_requirement: float,
        accuracy_requirement: float
    ) -> Dict:
        """Create workload distribution plan"""
        
        # Estimate processing times
        edge_latency = self.estimate_edge_latency(data_size)
        cloud_latency = self.estimate_cloud_latency(data_size)
        
        # Determine optimal location
        if edge_latency < latency_requirement and edge_latency < self.edge_threshold:
            return {
                'location': 'edge',
                'model': 'quantized_model',
                'estimated_latency': edge_latency
            }
        elif cloud_latency < latency_requirement:
            return {
                'location': 'cloud',
                'model': 'full_model',
                'estimated_latency': cloud_latency
            }
        else:
            # Hybrid approach for complex requirements
            return {
                'location': 'hybrid',
                'edge_portion': 0.7,
                'cloud_portion': 0.3,
                'model': 'split_model',
                'estimated_latency': (edge_latency * 0.7 + cloud_latency * 0.3)
            }

class ModelOptimizer:
    def __init__(self, config: Dict):
        self.optimization_level = config.get('level', 'moderate')
        
    async def quantize(
        self,
        model: MLModel,
        bits: int = 8,
        method: str = 'dynamic'
    ) -> MLModel:
        """Quantize model for edge deployment"""
        
        if method == 'dynamic':
            quantized_model = torch.quantization.quantize_dynamic(
                model.architecture,
                {nn.Linear, nn.Conv2d},
                dtype=torch.qint8
            )
        else:
            # Static quantization with calibration
            quantized_model = await self.static_quantization(model, bits)
        
        # Create new model instance
        optimized = MLModel(
            model_id=f"{model.model_id}_q{bits}",
            architecture=quantized_model,
            size_mb=model.size_mb / (32 / bits),  # Approximate size reduction
            accuracy=model.accuracy * 0.98,  # Slight accuracy loss
            latency_ms=model.latency_ms * 0.7,  # Faster inference
            quantized_versions=model.quantized_versions
        )
        
        return optimized
    
    async def prune(
        self,
        model: MLModel,
        sparsity: float = 0.5
    ) -> MLModel:
        """Prune model weights for reduced size"""
        
        import torch.nn.utils.prune as prune
        
        # Apply structured pruning
        for name, module in model.architecture.named_modules():
            if isinstance(module, (nn.Conv2d, nn.Linear)):
                prune.l1_unstructured(
                    module,
                    name='weight',
                    amount=sparsity
                )
        
        # Remove pruning reparameterization
        for name, module in model.architecture.named_modules():
            if isinstance(module, (nn.Conv2d, nn.Linear)):
                prune.remove(module, 'weight')
        
        return model

// Edge Node Management and Coordination
interface EdgeNodeConfig {
  nodeId: string;
  capabilities: NodeCapabilities;
  location: GeographicLocation;
  networkConfig: NetworkConfiguration;
}

interface NodeCapabilities {
  cpuCores: number;
  gpuAvailable: boolean;
  memoryGB: number;
  storageGB: number;
  maxPowerWatts: number;
}

interface InferenceTask {
  taskId: string;
  modelId: string;
  inputData: Float32Array;
  priority: 'low' | 'medium' | 'high' | 'critical';
  maxLatencyMs: number;
  minAccuracy: number;
}

class EdgeNodeManager {
  private nodes: Map<string, EdgeNode>;
  private modelCache: Map<string, DeployedModel>;
  private taskQueue: PriorityQueue<InferenceTask>;
  private federatedCoordinator: FederatedLearningCoordinator;
  
  constructor(config: EdgeNodeConfig) {
    this.nodes = new Map();
    this.modelCache = new Map();
    this.taskQueue = new PriorityQueue();
    this.federatedCoordinator = new FederatedLearningCoordinator(config);
  }

  async deployModel(
    modelId: string,
    targetNodes: string[],
    optimization: 'none' | 'quantized' | 'pruned' | 'distilled'
  ): Promise<DeploymentResult> {
    const model = await this.loadModel(modelId);
    
    // Optimize model based on target node capabilities
    const optimizedModels = await Promise.all(
      targetNodes.map(nodeId => 
        this.optimizeForNode(model, this.nodes.get(nodeId)!, optimization)
      )
    );
    
    // Deploy to nodes in parallel
    const deployments = await Promise.all(
      targetNodes.map((nodeId, index) => 
        this.deployToNode(nodeId, optimizedModels[index])
      )
    );
    
    return {
      successful: deployments.filter(d => d.success).length,
      failed: deployments.filter(d => !d.success).length,
      averageDeploymentTime: this.calculateAverageTime(deployments)
    };
  }

  async executeInference(task: InferenceTask): Promise<InferenceResult> {
    // Find optimal node for inference
    const selectedNode = this.selectNodeForInference(task);
    
    if (!selectedNode) {
      // Fallback to cloud if no suitable edge node
      return this.executeCloudInference(task);
    }
    
    // Check if model is cached on node
    const model = await this.ensureModelDeployed(
      selectedNode.nodeId,
      task.modelId
    );
    
    // Execute inference
    const startTime = performance.now();
    const result = await selectedNode.runInference(model, task.inputData);
    const latency = performance.now() - startTime;
    
    // Update node metrics
    this.updateNodeMetrics(selectedNode.nodeId, {
      lastInference: Date.now(),
      totalInferences: selectedNode.metrics.totalInferences + 1,
      averageLatency: this.updateRunningAverage(
        selectedNode.metrics.averageLatency,
        latency,
        selectedNode.metrics.totalInferences
      )
    });
    
    return {
      output: result.output,
      confidence: result.confidence,
      latencyMs: latency,
      nodeId: selectedNode.nodeId,
      modelVersion: model.version
    };
  }

  async coordinateFederatedLearning(): Promise<FederatedRoundResult> {
    // Select participating nodes
    const participants = this.selectFederatedParticipants();
    
    // Initialize federated round
    const round = await this.federatedCoordinator.initializeRound({
      participants: participants.map(n => n.nodeId),
      globalModel: this.modelCache.get('global_model')!,
      aggregationStrategy: 'fedavg'
    });
    
    // Execute local training on each node
    const localUpdates = await Promise.all(
      participants.map(node => 
        this.executeLocalTraining(node, round.trainingConfig)
      )
    );
    
    // Aggregate updates
    const aggregatedModel = await this.federatedCoordinator.aggregate(
      localUpdates,
      round.aggregationWeights
    );
    
    // Validate aggregated model
    const validation = await this.validateModel(aggregatedModel);
    
    if (validation.improved) {
      // Deploy new global model
      await this.deployGlobalModel(aggregatedModel);
    }
    
    return {
      roundNumber: round.number,
      participantCount: participants.length,
      modelImprovement: validation.improvement,
      nextRoundScheduled: Date.now() + 3600000 // 1 hour
    };
  }

  private selectNodeForInference(task: InferenceTask): EdgeNode | null {
    const eligibleNodes = Array.from(this.nodes.values())
      .filter(node => 
        node.hasModel(task.modelId) &&
        node.isAvailable() &&
        this.estimateLatency(node, task) < task.maxLatencyMs
      );
    
    if (eligibleNodes.length === 0) return null;
    
    // Score nodes based on multiple criteria
    return eligibleNodes.reduce((best, node) => {
      const score = this.calculateNodeScore(node, task);
      return score > this.calculateNodeScore(best, task) ? node : best;
    });
  }

  private calculateNodeScore(node: EdgeNode, task: InferenceTask): number {
    const latencyScore = 1 / this.estimateLatency(node, task);
    const loadScore = 1 - node.getCurrentLoad();
    const proximityScore = 1 / this.calculateNetworkDistance(node);
    const reliabilityScore = node.getReliabilityScore();
    
    // Weighted scoring based on task priority
    const weights = task.priority === 'critical' 
      ? { latency: 0.5, load: 0.2, proximity: 0.2, reliability: 0.1 }
      : { latency: 0.3, load: 0.3, proximity: 0.2, reliability: 0.2 };
    
    return (
      latencyScore * weights.latency +
      loadScore * weights.load +
      proximityScore * weights.proximity +
      reliabilityScore * weights.reliability
    );
  }

  async handleNodeFailure(nodeId: string): Promise<void> {
    const failedNode = this.nodes.get(nodeId);
    if (!failedNode) return;
    
    // Redistribute tasks from failed node
    const pendingTasks = failedNode.getPendingTasks();
    for (const task of pendingTasks) {
      await this.taskQueue.enqueue(task);
    }
    
    // Mark node as unavailable
    failedNode.setStatus('failed');
    
    // Trigger model redistribution if necessary
    if (failedNode.hasUniqueModels()) {
      await this.redistributeModels(failedNode.getModels());
    }
    
    // Notify monitoring system
    await this.notifyNodeFailure(nodeId);
  }

  async optimizeEdgeCloudPartitioning(
    workload: Workload
  ): Promise<PartitioningStrategy> {
    const edgeCapacity = this.calculateTotalEdgeCapacity();
    const workloadComplexity = this.analyzeWorkloadComplexity(workload);
    
    if (workloadComplexity.canRunFullyOnEdge && 
        edgeCapacity > workloadComplexity.requiredCapacity) {
      return {
        strategy: 'edge-only',
        edgePercent: 100,
        cloudPercent: 0,
        estimatedLatency: workloadComplexity.edgeLatency
      };
    }
    
    if (workloadComplexity.requiresCloudResources) {
      return {
        strategy: 'cloud-primary',
        edgePercent: 20, // Preprocessing only
        cloudPercent: 80,
        estimatedLatency: workloadComplexity.cloudLatency
      };
    }
    
    // Hybrid approach
    const optimalSplit = this.calculateOptimalSplit(
      workloadComplexity,
      edgeCapacity
    );
    
    return {
      strategy: 'hybrid',
      edgePercent: optimalSplit.edge,
      cloudPercent: optimalSplit.cloud,
      estimatedLatency: optimalSplit.latency,
      splitPoint: optimalSplit.modelLayer // For model splitting
    };
  }
}