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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 |