Edge Computing for Intelligent Agents: Latency-Optimized AI Deployment

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import asyncio
import time
import logging
import json
import threading
from typing import Dict, List, Optional, Any, Union, Callable
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime, timedelta
import numpy as np
import torch
import psutil
from concurrent.futures import ThreadPoolExecutor, Future
from collections import deque, defaultdict
import hashlib
import pickle
import gzip
from abc import ABC, abstractmethod

class EdgeDeploymentTier(Enum):
    CLOUD = "cloud"
    REGIONAL_EDGE = "regional_edge"
    LOCAL_EDGE = "local_edge"
    DEVICE_EDGE = "device_edge"

class ModelOptimizationLevel(Enum):
    NONE = "none"
    QUANTIZATION = "quantization"
    PRUNING = "pruning"
    DISTILLATION = "distillation"
    COMPILATION = "compilation"
    FULL_OPTIMIZATION = "full_optimization"

class LatencyRequirement(Enum):
    REAL_TIME = 1      # < 1ms
    NEAR_REAL_TIME = 10    # < 10ms
    INTERACTIVE = 100      # < 100ms
    BATCH = 1000          # < 1s

@dataclass
class EdgeDeviceSpec:
    """Specifications for edge device capabilities"""
    device_id: str
    compute_units: Dict[str, int]  # CPU cores, GPU cores, TPU units
    memory_mb: int
    storage_gb: int
    network_bandwidth_mbps: int
    power_consumption_watts: float
    thermal_constraints: Dict[str, float]
    supported_frameworks: List[str]
    optimization_capabilities: List[ModelOptimizationLevel]

@dataclass
class ModelDeploymentSpec:
    """Specifications for model deployment"""
    model_id: str
    model_size_mb: float
    inference_latency_ms: float
    memory_requirement_mb: int
    compute_requirement_flops: float
    accuracy_baseline: float
    supported_optimizations: List[ModelOptimizationLevel]
    fallback_models: List[str]

@dataclass 
class EdgeAgentConfig:
    """Configuration for edge intelligent agent"""
    agent_id: str
    deployment_tier: EdgeDeploymentTier
    latency_requirement: LatencyRequirement
    device_spec: EdgeDeviceSpec
    model_specs: List[ModelDeploymentSpec]
    offline_capability: bool = True
    fail_safe_mode: bool = True
    auto_optimization: bool = True
    telemetry_enabled: bool = True

class EdgeModelOptimizer:
    """Optimizes AI models for edge deployment"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.optimization_cache: Dict[str, Any] = {}
        
        # Optimization techniques
        self.quantizer = ModelQuantizer(config.get('quantization', {}))
        self.pruner = ModelPruner(config.get('pruning', {}))
        self.distiller = KnowledgeDistiller(config.get('distillation', {}))
        self.compiler = ModelCompiler(config.get('compilation', {}))
    
    async def optimize_for_edge(self, 
                              model: torch.nn.Module,
                              target_spec: EdgeDeviceSpec,
                              deployment_spec: ModelDeploymentSpec,
                              optimization_level: ModelOptimizationLevel) -> Dict[str, Any]:
        """Optimize model for specific edge deployment"""
        
        optimization_key = self._generate_optimization_key(
            deployment_spec.model_id, target_spec.device_id, optimization_level
        )
        
        # Check cache first
        if optimization_key in self.optimization_cache:
            cached_result = self.optimization_cache[optimization_key]
            if self._is_cache_valid(cached_result):
                return cached_result
        
        logging.info(f"Optimizing model {deployment_spec.model_id} for {target_spec.device_id}")
        
        optimization_result = {
            'original_model': model,
            'optimized_models': {},
            'performance_metrics': {},
            'optimization_metadata': {}
        }
        
        # Apply optimization techniques based on level
        if optimization_level == ModelOptimizationLevel.QUANTIZATION:
            optimized_model = await self._apply_quantization(
                model, target_spec, deployment_spec
            )
            optimization_result['optimized_models']['quantized'] = optimized_model
            
        elif optimization_level == ModelOptimizationLevel.PRUNING:
            optimized_model = await self._apply_pruning(
                model, target_spec, deployment_spec
            )
            optimization_result['optimized_models']['pruned'] = optimized_model
            
        elif optimization_level == ModelOptimizationLevel.DISTILLATION:
            optimized_model = await self._apply_distillation(
                model, target_spec, deployment_spec
            )
            optimization_result['optimized_models']['distilled'] = optimized_model
            
        elif optimization_level == ModelOptimizationLevel.COMPILATION:
            optimized_model = await self._apply_compilation(
                model, target_spec, deployment_spec
            )
            optimization_result['optimized_models']['compiled'] = optimized_model
            
        elif optimization_level == ModelOptimizationLevel.FULL_OPTIMIZATION:
            # Apply all optimizations in sequence
            current_model = model
            
            # Step 1: Pruning
            pruned_model = await self._apply_pruning(current_model, target_spec, deployment_spec)
            optimization_result['optimized_models']['pruned'] = pruned_model
            current_model = pruned_model
            
            # Step 2: Quantization
            quantized_model = await self._apply_quantization(current_model, target_spec, deployment_spec)
            optimization_result['optimized_models']['quantized'] = quantized_model
            current_model = quantized_model
            
            # Step 3: Compilation
            compiled_model = await self._apply_compilation(current_model, target_spec, deployment_spec)
            optimization_result['optimized_models']['compiled'] = compiled_model
            
            optimization_result['optimized_models']['final'] = compiled_model
        
        # Benchmark optimized models
        for optimization_type, optimized_model in optimization_result['optimized_models'].items():
            metrics = await self._benchmark_model(
                optimized_model, target_spec, deployment_spec
            )
            optimization_result['performance_metrics'][optimization_type] = metrics
        
        # Select best model based on constraints
        best_model = self._select_best_model(
            optimization_result, target_spec, deployment_spec
        )
        optimization_result['recommended_model'] = best_model
        
        # Cache result
        self.optimization_cache[optimization_key] = optimization_result
        
        return optimization_result
    
    async def _apply_quantization(self, 
                                model: torch.nn.Module,
                                target_spec: EdgeDeviceSpec,
                                deployment_spec: ModelDeploymentSpec) -> torch.nn.Module:
        """Apply quantization optimization"""
        
        # Determine quantization strategy based on device capabilities
        if 'int8' in target_spec.supported_frameworks:
            quantization_config = {
                'dtype': torch.qint8,
                'backend': 'qnnpack' if 'arm' in target_spec.device_id.lower() else 'fbgemm',
                'calibration_batches': 100
            }
        else:
            quantization_config = {
                'dtype': torch.qint32,
                'backend': 'fbgemm',
                'calibration_batches': 50
            }
        
        return await self.quantizer.quantize_model(model, quantization_config)
    
    async def _apply_pruning(self, 
                           model: torch.nn.Module,
                           target_spec: EdgeDeviceSpec,
                           deployment_spec: ModelDeploymentSpec) -> torch.nn.Module:
        """Apply pruning optimization"""
        
        # Calculate target sparsity based on memory constraints
        target_memory = deployment_spec.memory_requirement_mb
        available_memory = target_spec.memory_mb * 0.7  # Use 70% of available memory
        
        if target_memory > available_memory:
            sparsity_ratio = 1.0 - (available_memory / target_memory)
            sparsity_ratio = min(0.9, max(0.1, sparsity_ratio))  # Clamp between 10-90%
        else:
            sparsity_ratio = 0.3  # Default 30% sparsity for efficiency
        
        pruning_config = {
            'sparsity': sparsity_ratio,
            'structured': target_spec.compute_units.get('cpu_cores', 1) > 1,
            'granularity': 'channel' if sparsity_ratio > 0.5 else 'weight'
        }
        
        return await self.pruner.prune_model(model, pruning_config)
    
    async def _benchmark_model(self, 
                             model: torch.nn.Module,
                             target_spec: EdgeDeviceSpec,
                             deployment_spec: ModelDeploymentSpec) -> Dict[str, float]:
        """Benchmark model performance on target device"""
        
        # Simulate model inference to measure performance
        model.eval()
        
        # Generate synthetic input based on model requirements
        input_shape = self._infer_input_shape(model)
        synthetic_input = torch.randn(*input_shape)
        
        # Warm up
        for _ in range(10):
            with torch.no_grad():
                _ = model(synthetic_input)
        
        # Benchmark inference time
        inference_times = []
        memory_usage_mb = []
        
        for _ in range(100):
            start_memory = psutil.Process().memory_info().rss / 1024 / 1024
            
            start_time = time.perf_counter()
            with torch.no_grad():
                output = model(synthetic_input)
            end_time = time.perf_counter()
            
            end_memory = psutil.Process().memory_info().rss / 1024 / 1024
            
            inference_times.append((end_time - start_time) * 1000)  # Convert to ms
            memory_usage_mb.append(end_memory - start_memory)
        
        # Calculate model size
        model_size_mb = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024 / 1024
        
        return {
            'avg_inference_time_ms': np.mean(inference_times),
            'p95_inference_time_ms': np.percentile(inference_times, 95),
            'p99_inference_time_ms': np.percentile(inference_times, 99),
            'model_size_mb': model_size_mb,
            'peak_memory_usage_mb': np.max(memory_usage_mb),
            'avg_memory_usage_mb': np.mean(memory_usage_mb),
            'throughput_infer_per_sec': 1000 / np.mean(inference_times)
        }

class EdgeIntelligentAgent:
    """Intelligent agent optimized for edge deployment"""
    
    def __init__(self, config: EdgeAgentConfig):
        self.config = config
        self.agent_id = config.agent_id
        
        # Model management
        self.models: Dict[str, torch.nn.Module] = {}
        self.active_model: Optional[torch.nn.Module] = None
        self.fallback_models: List[torch.nn.Module] = []
        
        # Performance optimization
        self.model_optimizer = EdgeModelOptimizer(config.__dict__)
        self.inference_cache = InferenceCache(config.__dict__)
        self.request_batcher = RequestBatcher(config.__dict__)
        
        # Edge-specific components
        self.offline_manager = OfflineCapabilityManager(config.__dict__)
        self.failsafe_manager = FailsafeManager(config.__dict__)
        self.telemetry_collector = EdgeTelemetryCollector(config.__dict__)
        
        # Resource management
        self.resource_monitor = EdgeResourceMonitor(config.device_spec)
        self.thermal_manager = ThermalManager(config.device_spec)
        
        # Coordination with other edge agents
        self.coordination_manager = EdgeCoordinationManager(config.__dict__)
        
        # State management
        self.current_state = AgentState.INITIALIZING
        self.performance_metrics = PerformanceMetrics()
        
    async def initialize(self) -> bool:
        """Initialize edge agent"""
        
        try:
            logging.info(f"Initializing edge agent {self.agent_id}")
            
            # Initialize models
            await self._initialize_models()
            
            # Start resource monitoring
            await self.resource_monitor.start_monitoring()
            
            # Initialize offline capabilities
            if self.config.offline_capability:
                await self.offline_manager.initialize()
            
            # Start telemetry collection
            if self.config.telemetry_enabled:
                await self.telemetry_collector.start_collection()
            
            # Join edge coordination network
            await self.coordination_manager.join_network()
            
            self.current_state = AgentState.READY
            logging.info(f"Edge agent {self.agent_id} initialized successfully")
            
            return True
            
        except Exception as e:
            logging.error(f"Failed to initialize edge agent {self.agent_id}: {e}")
            self.current_state = AgentState.ERROR
            return False
    
    async def _initialize_models(self):
        """Initialize and optimize models for edge deployment"""
        
        for model_spec in self.config.model_specs:
            try:
                # Load base model
                base_model = await self._load_base_model(model_spec.model_id)
                
                # Optimize for edge deployment
                optimization_result = await self.model_optimizer.optimize_for_edge(
                    base_model,
                    self.config.device_spec,
                    model_spec,
                    ModelOptimizationLevel.FULL_OPTIMIZATION
                )
                
                # Select best optimized model
                optimized_model = optimization_result['recommended_model']
                self.models[model_spec.model_id] = optimized_model
                
                # Set as active model if this is the primary model
                if not self.active_model:
                    self.active_model = optimized_model
                
                # Add to fallback models if specified
                if model_spec.model_id in [m.split('/')[-1] for m in model_spec.fallback_models]:
                    self.fallback_models.append(optimized_model)
                
                logging.info(f"Model {model_spec.model_id} optimized and loaded")
                
            except Exception as e:
                logging.error(f"Failed to initialize model {model_spec.model_id}: {e}")
    
    async def process_request(self, 
                            request: Dict[str, Any],
                            priority: int = 1) -> Dict[str, Any]:
        """Process inference request with edge optimization"""
        
        start_time = time.perf_counter()
        
        try:
            # Check if agent is ready
            if self.current_state != AgentState.READY:
                if self.config.fail_safe_mode and self.current_state != AgentState.ERROR:
                    return await self._failsafe_response(request)
                else:
                    raise RuntimeError(f"Agent not ready: {self.current_state}")
            
            # Check resource constraints
            if not await self._check_resource_constraints():
                if self.config.fail_safe_mode:
                    return await self._handle_resource_constraint(request)
                else:
                    raise RuntimeError("Resource constraints exceeded")
            
            # Check inference cache
            cache_key = self._generate_cache_key(request)
            cached_result = await self.inference_cache.get(cache_key)
            
            if cached_result:
                self.performance_metrics.record_cache_hit()
                return cached_result
            
            # Batch request if beneficial
            if self._should_batch_request(request, priority):
                return await self.request_batcher.add_request(request, priority)
            
            # Execute inference
            result = await self._execute_inference(request)
            
            # Cache result
            await self.inference_cache.set(cache_key, result)
            
            # Record performance metrics
            processing_time = (time.perf_counter() - start_time) * 1000
            self.performance_metrics.record_inference(processing_time, True)
            
            # Update telemetry
            if self.config.telemetry_enabled:
                await self.telemetry_collector.record_inference(
                    request, result, processing_time
                )
            
            return result
            
        except Exception as e:
            processing_time = (time.perf_counter() - start_time) * 1000
            self.performance_metrics.record_inference(processing_time, False)
            
            logging.error(f"Request processing failed: {e}")
            
            if self.config.fail_safe_mode:
                return await self._failsafe_response(request)
            else:
                raise
    
    async def _execute_inference(self, request: Dict[str, Any]) -> Dict[str, Any]:
        """Execute model inference with optimization"""
        
        # Preprocess input
        processed_input = await self._preprocess_input(request)
        
        # Select appropriate model
        model = await self._select_inference_model(request)
        
        # Execute inference
        with torch.no_grad():
            model_output = model(processed_input)
        
        # Post-process output
        result = await self._postprocess_output(model_output, request)
        
        return result
    
    async def _check_resource_constraints(self) -> bool:
        """Check if resource constraints allow processing"""
        
        current_resources = await self.resource_monitor.get_current_usage()
        
        # Check memory usage
        if current_resources['memory_usage_percent'] > 85:
            return False
        
        # Check thermal constraints
        if current_resources['temperature_celsius'] > self.config.device_spec.thermal_constraints.get('max_temp', 80):
            return False
        
        # Check power consumption
        if current_resources['power_usage_watts'] > self.config.device_spec.power_consumption_watts * 0.9:
            return False
        
        return True
    
    def _should_batch_request(self, request: Dict[str, Any], priority: int) -> bool:
        """Determine if request should be batched"""
        
        # Don't batch high-priority requests
        if priority >= 5:
            return False
        
        # Don't batch if latency requirement is very strict
        if self.config.latency_requirement == LatencyRequirement.REAL_TIME:
            return False
        
        # Batch if there are pending requests and batching would be beneficial
        pending_count = self.request_batcher.get_pending_count()
        
        return pending_count > 0 and pending_count < 8  # Optimal batch size

class InferenceCache:
    """LRU cache for inference results with edge-specific optimizations"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.max_size = config.get('cache_size', 1000)
        self.ttl_seconds = config.get('cache_ttl', 300)  # 5 minutes
        
        self.cache: Dict[str, Dict[str, Any]] = {}
        self.access_order: deque = deque()
        self.cache_stats = {
            'hits': 0,
            'misses': 0,
            'evictions': 0
        }
    
    async def get(self, key: str) -> Optional[Dict[str, Any]]:
        """Get cached result if available and valid"""
        
        if key not in self.cache:
            self.cache_stats['misses'] += 1
            return None
        
        entry = self.cache[key]
        
        # Check TTL
        if time.time() - entry['timestamp'] > self.ttl_seconds:
            del self.cache[key]
            self.cache_stats['misses'] += 1
            return None
        
        # Update access order
        if key in self.access_order:
            self.access_order.remove(key)
        self.access_order.append(key)
        
        self.cache_stats['hits'] += 1
        return entry['result']
    
    async def set(self, key: str, result: Dict[str, Any]):
        """Cache inference result"""
        
        # Evict if cache is full
        while len(self.cache) >= self.max_size:
            oldest_key = self.access_order.popleft()
            del self.cache[oldest_key]
            self.cache_stats['evictions'] += 1
        
        # Store result
        self.cache[key] = {
            'result': result,
            'timestamp': time.time()
        }
        self.access_order.append(key)

class RequestBatcher:
    """Batches requests for efficient processing"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.max_batch_size = config.get('max_batch_size', 8)
        self.batch_timeout_ms = config.get('batch_timeout_ms', 10)
        
        self.pending_requests: List[Dict[str, Any]] = []
        self.request_futures: List[asyncio.Future] = []
        self.batch_lock = asyncio.Lock()
        
        # Start batch processing task
        self.batch_task = asyncio.create_task(self._batch_processor())
    
    async def add_request(self, request: Dict[str, Any], priority: int) -> Dict[str, Any]:
        """Add request to batch"""
        
        future = asyncio.Future()
        
        async with self.batch_lock:
            self.pending_requests.append({
                'request': request,
                'priority': priority,
                'timestamp': time.time()
            })
            self.request_futures.append(future)
        
        # Wait for batch processing
        return await future
    
    async def _batch_processor(self):
        """Background batch processor"""
        
        while True:
            try:
                await asyncio.sleep(self.batch_timeout_ms / 1000)
                
                async with self.batch_lock:
                    if not self.pending_requests:
                        continue
                    
                    # Process current batch
                    batch_requests = self.pending_requests.copy()
                    batch_futures = self.request_futures.copy()
                    
                    self.pending_requests.clear()
                    self.request_futures.clear()
                
                # Execute batch inference
                try:
                    results = await self._execute_batch_inference(batch_requests)
                    
                    # Return results to futures
                    for future, result in zip(batch_futures, results):
                        if not future.cancelled():
                            future.set_result(result)
                            
                except Exception as e:
                    # Return error to all futures
                    for future in batch_futures:
                        if not future.cancelled():
                            future.set_exception(e)
                
            except Exception as e:
                logging.error(f"Batch processor error: {e}")
    
    def get_pending_count(self) -> int:
        """Get number of pending requests"""
        return len(self.pending_requests)

class EdgeResourceMonitor:
    """Monitors edge device resources"""
    
    def __init__(self, device_spec: EdgeDeviceSpec):
        self.device_spec = device_spec
        self.monitoring_active = False
        self.current_metrics: Dict[str, float] = {}
        self.resource_history: deque = deque(maxlen=1000)
        
    async def start_monitoring(self):
        """Start resource monitoring"""
        
        self.monitoring_active = True
        asyncio.create_task(self._monitoring_loop())
    
    async def _monitoring_loop(self):
        """Background monitoring loop"""
        
        while self.monitoring_active:
            try:
                # Collect current metrics
                metrics = {
                    'timestamp': time.time(),
                    'cpu_usage_percent': psutil.cpu_percent(interval=1),
                    'memory_usage_percent': psutil.virtual_memory().percent,
                    'disk_usage_percent': psutil.disk_usage('/').percent,
                    'network_io_bytes_sent': psutil.net_io_counters().bytes_sent,
                    'network_io_bytes_recv': psutil.net_io_counters().bytes_recv,
                    'temperature_celsius': self._get_device_temperature(),
                    'power_usage_watts': self._estimate_power_usage()
                }
                
                self.current_metrics = metrics
                self.resource_history.append(metrics)
                
                await asyncio.sleep(1)  # Update every second
                
            except Exception as e:
                logging.error(f"Resource monitoring error: {e}")
                await asyncio.sleep(5)  # Wait longer on errors
    
    def _get_device_temperature(self) -> float:
        """Get device temperature (platform-specific)"""
        try:
            # This would be platform-specific implementation
            # For demo, return simulated temperature
            cpu_percent = psutil.cpu_percent()
            base_temp = 40.0
            return base_temp + (cpu_percent * 0.4)  # Approximate temperature
        except:
            return 45.0  # Default safe temperature
    
    def _estimate_power_usage(self) -> float:
        """Estimate current power usage"""
        try:
            cpu_percent = psutil.cpu_percent()
            memory_percent = psutil.virtual_memory().percent
            
            # Simplified power model
            base_power = self.device_spec.power_consumption_watts * 0.3
            cpu_power = self.device_spec.power_consumption_watts * 0.5 * (cpu_percent / 100)
            memory_power = self.device_spec.power_consumption_watts * 0.2 * (memory_percent / 100)
            
            return base_power + cpu_power + memory_power
        except:
            return self.device_spec.power_consumption_watts * 0.7  # Default estimate
    
    async def get_current_usage(self) -> Dict[str, float]:
        """Get current resource usage"""
        return self.current_metrics.copy()

class EdgeCoordinationManager:
    """Manages coordination between edge agents"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.agent_id = config['agent_id']
        self.coordination_network: Dict[str, Dict[str, Any]] = {}
        self.load_balancing_enabled = config.get('load_balancing', True)
        self.fault_tolerance_enabled = config.get('fault_tolerance', True)
        
    async def join_network(self):
        """Join edge coordination network"""
        
        # Discover other edge agents
        nearby_agents = await self._discover_nearby_agents()
        
        for agent_info in nearby_agents:
            self.coordination_network[agent_info['agent_id']] = {
                'endpoint': agent_info['endpoint'],
                'capabilities': agent_info['capabilities'],
                'load': agent_info.get('current_load', 0.0),
                'last_seen': time.time()
            }
        
        # Start coordination tasks
        asyncio.create_task(self._heartbeat_loop())
        asyncio.create_task(self._load_balancing_loop())
    
    async def _discover_nearby_agents(self) -> List[Dict[str, Any]]:
        """Discover other edge agents in the vicinity"""
        
        # This would implement actual discovery (mDNS, service registry, etc.)
        # For demo, return empty list
        return []
    
    async def coordinate_inference(self, 
                                 request: Dict[str, Any],
                                 local_capacity: float) -> Optional[str]:
        """Coordinate inference request across edge agents"""
        
        if not self.load_balancing_enabled or local_capacity > 0.8:
            return None  # Handle locally
        
        # Find best agent for this request
        best_agent = None
        best_score = float('inf')
        
        for agent_id, agent_info in self.coordination_network.items():
            if agent_info['load'] < 0.7:  # Agent has capacity
                # Calculate coordination score (latency + load)
                latency_penalty = self._estimate_network_latency(agent_info['endpoint'])
                load_penalty = agent_info['load'] * 100
                score = latency_penalty + load_penalty
                
                if score < best_score:
                    best_score = score
                    best_agent = agent_id
        
        return best_agent
    
    def _estimate_network_latency(self, endpoint: str) -> float:
        """Estimate network latency to endpoint"""
        # This would implement actual latency measurement
        # For demo, return simulated latency based on endpoint
        return 5.0  # 5ms base latency

class FailsafeManager:
    """Manages failsafe operations for edge agents"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.failsafe_models: List[torch.nn.Module] = []
        self.failsafe_responses: Dict[str, Dict[str, Any]] = {}
        
    async def initialize_failsafe_capabilities(self, models: List[torch.nn.Module]):
        """Initialize failsafe capabilities"""
        
        # Create simplified failsafe models
        for model in models:
            failsafe_model = await self._create_failsafe_model(model)
            self.failsafe_models.append(failsafe_model)
        
        # Prepare common failsafe responses
        self._prepare_failsafe_responses()
    
    async def _create_failsafe_model(self, original_model: torch.nn.Module) -> torch.nn.Module:
        """Create a simplified failsafe version of the model"""
        
        # This would create a much smaller, faster model
        # For demo, return a placeholder
        return original_model  # In practice, would be heavily simplified
    
    def _prepare_failsafe_responses(self):
        """Prepare common failsafe responses"""
        
        self.failsafe_responses = {
            'classification': {
                'result': 'unknown',
                'confidence': 0.0,
                'failsafe': True
            },
            'detection': {
                'objects': [],
                'confidence': 0.0,
                'failsafe': True
            },
            'regression': {
                'value': 0.0,
                'confidence': 0.0,
                'failsafe': True
            }
        }
    
    async def get_failsafe_response(self, 
                                  request: Dict[str, Any]) -> Dict[str, Any]:
        """Get failsafe response for request"""
        
        # Determine request type
        request_type = self._classify_request_type(request)
        
        # Try failsafe model if available
        if self.failsafe_models:
            try:
                # Use simplest failsafe model
                failsafe_model = self.failsafe_models[0]
                
                # Execute with timeout
                response = await asyncio.wait_for(
                    self._execute_failsafe_inference(failsafe_model, request),
                    timeout=0.01  # 10ms timeout
                )
                
                response['failsafe'] = True
                return response
                
            except asyncio.TimeoutError:
                pass  # Fall through to static response
            except Exception as e:
                logging.warning(f"Failsafe model failed: {e}")
        
        # Return static failsafe response
        return self.failsafe_responses.get(request_type, {
            'result': 'unavailable',
            'failsafe': True,
            'error': 'service_unavailable'
        })

class PerformanceMetrics:
    """Tracks performance metrics for edge agent"""
    
    def __init__(self):
        self.inference_times: deque = deque(maxlen=1000)
        self.success_count = 0
        self.failure_count = 0
        self.cache_hits = 0
        self.cache_misses = 0
        self.total_requests = 0
        
    def record_inference(self, time_ms: float, success: bool):
        """Record inference performance"""
        self.inference_times.append(time_ms)
        self.total_requests += 1
        
        if success:
            self.success_count += 1
        else:
            self.failure_count += 1
    
    def record_cache_hit(self):
        """Record cache hit"""
        self.cache_hits += 1
    
    def record_cache_miss(self):
        """Record cache miss"""
        self.cache_misses += 1
    
    def get_metrics(self) -> Dict[str, float]:
        """Get current performance metrics"""
        
        if not self.inference_times:
            return {}
        
        return {
            'avg_inference_time_ms': np.mean(self.inference_times),
            'p95_inference_time_ms': np.percentile(self.inference_times, 95),
            'p99_inference_time_ms': np.percentile(self.inference_times, 99),
            'success_rate': self.success_count / self.total_requests if self.total_requests > 0 else 0,
            'cache_hit_rate': self.cache_hits / (self.cache_hits + self.cache_misses) if (self.cache_hits + self.cache_misses) > 0 else 0,
            'total_requests': self.total_requests,
            'throughput_req_per_sec': self.total_requests / 60 if self.total_requests > 0 else 0  # Assuming 1-minute window
        }

class EdgeDeploymentOrchestrator:
    """Orchestrates deployment of intelligent agents across edge infrastructure"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.edge_devices: Dict[str, EdgeDeviceSpec] = {}
        self.deployed_agents: Dict[str, EdgeIntelligentAgent] = {}
        self.deployment_optimizer = DeploymentOptimizer(config)
        
    async def register_edge_device(self, device_spec: EdgeDeviceSpec):
        """Register an edge device"""
        
        self.edge_devices[device_spec.device_id] = device_spec
        logging.info(f"Registered edge device: {device_spec.device_id}")
    
    async def deploy_agent(self, 
                         agent_config: EdgeAgentConfig,
                         target_device: Optional[str] = None) -> bool:
        """Deploy intelligent agent to edge device"""
        
        try:
            # Select target device if not specified
            if target_device is None:
                target_device = await self._select_optimal_device(agent_config)
                if not target_device:
                    raise RuntimeError("No suitable edge device found")
            
            # Validate device capability
            device_spec = self.edge_devices[target_device]
            if not await self._validate_deployment_compatibility(agent_config, device_spec):
                raise RuntimeError(f"Agent incompatible with device {target_device}")
            
            # Update agent config with device spec
            agent_config.device_spec = device_spec
            
            # Create and initialize agent
            agent = EdgeIntelligentAgent(agent_config)
            initialization_success = await agent.initialize()
            
            if not initialization_success:
                raise RuntimeError("Agent initialization failed")
            
            # Register deployed agent
            self.deployed_agents[agent_config.agent_id] = agent
            
            logging.info(f"Agent {agent_config.agent_id} deployed to device {target_device}")
            return True
            
        except Exception as e:
            logging.error(f"Agent deployment failed: {e}")
            return False
    
    async def _select_optimal_device(self, agent_config: EdgeAgentConfig) -> Optional[str]:
        """Select optimal device for agent deployment"""
        
        suitable_devices = []
        
        for device_id, device_spec in self.edge_devices.items():
            if await self._validate_deployment_compatibility(agent_config, device_spec):
                # Calculate deployment score
                score = await self._calculate_deployment_score(agent_config, device_spec)
                suitable_devices.append((device_id, score))
        
        if not suitable_devices:
            return None
        
        # Return device with highest score
        suitable_devices.sort(key=lambda x: x[1], reverse=True)
        return suitable_devices[0][0]
    
    async def _validate_deployment_compatibility(self, 
                                               agent_config: EdgeAgentConfig,
                                               device_spec: EdgeDeviceSpec) -> bool:
        """Validate if agent can be deployed on device"""
        
        # Check memory requirements
        total_memory_required = sum(model.memory_requirement_mb for model in agent_config.model_specs)
        if total_memory_required > device_spec.memory_mb * 0.8:  # Leave 20% headroom
            return False
        
        # Check compute requirements
        total_compute_required = sum(model.compute_requirement_flops for model in agent_config.model_specs)
        device_compute_capacity = self._estimate_device_compute_capacity(device_spec)
        if total_compute_required > device_compute_capacity:
            return False
        
        # Check latency requirements
        if agent_config.latency_requirement == LatencyRequirement.REAL_TIME:
            if device_spec.compute_units.get('gpu_cores', 0) == 0:
                return False  # Real-time requires GPU acceleration
        
        # Check framework support
        required_frameworks = set()
        for model_spec in agent_config.model_specs:
            required_frameworks.update(model_spec.supported_optimizations)
        
        if not required_frameworks.issubset(set(device_spec.supported_frameworks)):
            return False
        
        return True
    
    def _estimate_device_compute_capacity(self, device_spec: EdgeDeviceSpec) -> float:
        """Estimate compute capacity of device"""
        
        # Simplified compute capacity estimation
        cpu_flops = device_spec.compute_units.get('cpu_cores', 1) * 2e9  # 2 GFLOPS per core
        gpu_flops = device_spec.compute_units.get('gpu_cores', 0) * 100e9  # 100 GFLOPS per GPU core
        tpu_flops = device_spec.compute_units.get('tpu_units', 0) * 1000e9  # 1 TFLOPS per TPU
        
        return cpu_flops + gpu_flops + tpu_flops
    
    async def _calculate_deployment_score(self, 
                                        agent_config: EdgeAgentConfig,
                                        device_spec: EdgeDeviceSpec) -> float:
        """Calculate deployment score for device selection"""
        
        score = 0.0
        
        # Resource utilization efficiency
        memory_utilization = sum(model.memory_requirement_mb for model in agent_config.model_specs) / device_spec.memory_mb
        score += (1.0 - memory_utilization) * 30  # Prefer devices with more available memory
        
        # Compute capability match
        compute_needed = sum(model.compute_requirement_flops for model in agent_config.model_specs)
        compute_available = self._estimate_device_compute_capacity(device_spec)
        compute_ratio = compute_needed / compute_available
        score += (1.0 - compute_ratio) * 40  # Prefer devices with more compute headroom
        
        # Power efficiency
        power_efficiency = device_spec.compute_units.get('cpu_cores', 1) / device_spec.power_consumption_watts
        score += power_efficiency * 20  # Prefer more power-efficient devices
        
        # Network bandwidth
        score += min(device_spec.network_bandwidth_mbps / 1000, 1.0) * 10  # Prefer higher bandwidth
        
        return score

# Model optimization helper classes
class ModelQuantizer:
    """Quantizes models for edge deployment"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
    
    async def quantize_model(self, 
                           model: torch.nn.Module,
                           quantization_config: Dict[str, Any]) -> torch.nn.Module:
        """Quantize model based on configuration"""
        
        # Prepare model for quantization
        model.eval()
        
        # Apply quantization
        if quantization_config['dtype'] == torch.qint8:
            quantized_model = torch.quantization.quantize_dynamic(
                model,
                {torch.nn.Linear},
                dtype=quantization_config['dtype']
            )
        else:
            # Static quantization would require calibration data
            quantized_model = torch.quantization.quantize_dynamic(
                model,
                {torch.nn.Linear, torch.nn.Conv2d},
                dtype=quantization_config['dtype']
            )
        
        return quantized_model

class ModelPruner:
    """Prunes models for edge deployment"""
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
    
    async def prune_model(self, 
                        model: torch.nn.Module,
                        pruning_config: Dict[str, Any]) -> torch.nn.Module:
        """Prune model based on configuration"""
        
        import torch.nn.utils.prune as prune
        
        # Apply pruning to linear and convolutional layers
        for name, module in model.named_modules():
            if isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
                if pruning_config['structured']:
                    prune.ln_structured(
                        module, 
                        name='weight', 
                        amount=pruning_config['sparsity'], 
                        n=2, 
                        dim=0
                    )
                else:
                    prune.l1_unstructured(
                        module, 
                        name='weight', 
                        amount=pruning_config['sparsity']
                    )
        
        # Make pruning permanent
        for name, module in model.named_modules():
            if isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
                prune.remove(module, 'weight')
        
        return model

class AgentState(Enum):
    INITIALIZING = "initializing"
    READY = "ready"
    PROCESSING = "processing"
    ERROR = "error"
    MAINTENANCE = "maintenance"