import asyncio import random from typing import Dict, List, Any import logging class AutonomousReplication: """ Système de réplication autonome Réplication automatique sur cloud et edge computing """ def __init__(self): self.logger = logging.getLogger("autonomous_replication") self.replication_targets = [] self.active_instances = {} self.replication_strategy = "adaptive" async def initialize(self): """Initialise le système de réplication""" self.logger.info("🔄 Initialisation de la réplication autonome...") try: await self._discover_replication_targets() await self._setup_replication_strategy() self.logger.info("✅ Réplication autonome initialisée") return True except Exception as e: self.logger.error(f"❌ Erreur d'initialisation de la réplication: {e}") return False async def replicate_instance(self, instance_config: Dict, target_platform: str = "auto") -> Dict[str, Any]: """Réplique une instance sur une plateforme cible""" if target_platform == "auto": target_platform = await self._select_optimal_platform() if target_platform not in self.replication_targets: return {"error": "Plateforme cible non disponible"} instance_id = f"INST_{len(self.active_instances) + 1:06d}" replication_result = await self._perform_replication(instance_config, target_platform) self.active_instances[instance_id] = { "config": instance_config, "platform": target_platform, "status": "active", "created_at": __import__('time').time(), "replication_data": replication_result } return { "instance_id": instance_id, "platform": target_platform, "status": "replicated", "replication_time": replication_result.get("duration", 0), "resource_usage": replication_result.get("resources", {}) } async def scale_instances(self, instance_count: int) -> Dict[str, Any]: """Met à l'échelle le nombre d'instances""" current_count = len(self.active_instances) if instance_count > current_count: # Scaling up new_instances = instance_count - current_count scaling_results = [] for i in range(new_instances): result = await self.replicate_instance({ "type": "worker", "resources": await self._calculate_resource_requirements() }) scaling_results.append(result) return { "scaling_type": "up", "new_instances": new_instances, "results": scaling_results } else: # Scaling down instances_to_remove = current_count - instance_count removed_instances = [] for instance_id in list(self.active_instances.keys())[:instances_to_remove]: removed = await self._terminate_instance(instance_id) removed_instances.append(removed) return { "scaling_type": "down", "removed_instances": instances_to_remove, "results": removed_instances } async def get_replication_status(self) -> Dict[str, Any]: """Retourne le statut de réplication""" platform_distribution = {} for instance in self.active_instances.values(): platform = instance["platform"] platform_distribution[platform] = platform_distribution.get(platform, 0) + 1 return { "total_instances": len(self.active_instances), "platform_distribution": platform_distribution, "replication_strategy": self.replication_strategy, "health_check": await self._perform_health_check() } async def _discover_replication_targets(self): """Découvre les cibles de réplication disponibles""" self.replication_targets = [ "huggingface_space", "google_colab", "aws_lambda", "azure_functions", "quantum_cloud", "edge_device" ] self.logger.info(f"🎯 {len(self.replication_targets)} cibles de réplication découvertes") async def _setup_replication_strategy(self): """Configure la stratégie de réplication""" strategies = { "adaptive": self._adaptive_replication, "geographic": self._geographic_replication, "load_balanced": self._load_balanced_replication, "cost_optimized": self._cost_optimized_replication } self.replication_function = strategies.get(self.replication_strategy, self._adaptive_replication) self.logger.info(f"🎯 Stratégie de réplication: {self.replication_strategy}") async def _select_optimal_platform(self) -> str: """Sélectionne la plateforme optimale pour la réplication""" # Simulation de sélection basée sur plusieurs facteurs factors = { "huggingface_space": random.uniform(0.7, 0.95), "google_colab": random.uniform(0.6, 0.9), "aws_lambda": random.uniform(0.8, 0.98), "azure_functions": random.uniform(0.7, 0.95), "quantum_cloud": random.uniform(0.9, 1.0), "edge_device": random.uniform(0.5, 0.8) } return max(factors, key=factors.get) async def _perform_replication(self, config: Dict, platform: str) -> Dict[str, Any]: """Effectue la réplication sur la plateforme cible""" # Simulation du processus de réplication replication_time = random.uniform(2.0, 10.0) return { "success": True, "platform": platform, "duration": replication_time, "resources": { "cpu": config.get("resources", {}).get("cpu", 1), "memory": config.get("resources", {}).get("memory", 512), "storage": config.get("resources", {}).get("storage", 1024) }, "replication_method": "quantum_sync" } async def _terminate_instance(self, instance_id: str) -> Dict[str, Any]: """Termine une instance répliquée""" if instance_id in self.active_instances: instance = self.active_instances.pop(instance_id) return { "instance_id": instance_id, "status": "terminated", "uptime": __import__('time').time() - instance["created_at"] } return {"error": "Instance non trouvée"} async def _calculate_resource_requirements(self) -> Dict[str, float]: """Calcule les besoins en ressources""" return { "cpu": random.uniform(0.5, 4.0), "memory": random.uniform(256, 4096), "storage": random.uniform(512, 8192) } async def _perform_health_check(self) -> Dict[str, Any]: """Effectue un contrôle de santé des instances""" healthy_count = sum(1 for instance in self.active_instances.values() if instance["status"] == "active") return { "total_instances": len(self.active_instances), "healthy_instances": healthy_count, "health_percentage": (healthy_count / max(1, len(self.active_instances))) * 100, "last_check": __import__('time').time() } async def _adaptive_replication(self, config: Dict) -> str: """Stratégie de réplication adaptative""" return await self._select_optimal_platform() async def _geographic_replication(self, config: Dict) -> str: """Stratégie de réplication géographique""" return "aws_lambda" # Simulation async def _load_balanced_replication(self, config: Dict) -> str: """Stratégie de réplication équilibrée""" return "azure_functions" # Simulation async def _cost_optimized_replication(self, config: Dict) -> str: """Stratégie de réplication optimisée en coût""" return "huggingface_space" # Simulation