Replication/autonomous_replication.py

#!/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