Create Replication/autonomous_replication.py

Replication/autonomous_replication.py

@@ -0,0 +1,218 @@
+#!/usr/bin/env python3
+"""
+Réplication Autonome
+Système de réplication automatique sur multiples plateformes
+"""
+
+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