MCP Proxmox Server

""" AI/ML Optimization Module for Proxmox MCP Server This module implements: - AI-powered predictive scaling based on usage patterns - Anomaly detection for proactive issue resolution - Automated optimization recommendations - Machine learning models for resource management """ import os import json import pickle import numpy as np import pandas as pd from typing import Dict, List, Optional, Any, Tuple from pathlib import Path from datetime import datetime, timedelta import joblib from sklearn.ensemble import IsolationForest, RandomForestRegressor from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error, accuracy_score from loguru import logger from .client import ProxmoxClient from .utils import run_command, format_error class AIOptimizationManager: """AI-powered optimization and management for Proxmox infrastructure""" def __init__(self, proxmox_client: ProxmoxClient): self.client = proxmox_client self.models_dir = Path.home() / ".proxmox_mcp" / "ai_models" self.models_dir.mkdir(parents=True, exist_ok=True) # AI Models self.scaling_model = None self.anomaly_detector = None self.optimization_model = None self.scaler = StandardScaler() # Historical data storage self.metrics_history = [] self.predictions_cache = {} async def ai_scaling( self, vmid: int, enable_prediction: bool = True, metrics_window: str = "7d", scaling_policy: Optional[Dict[str, Any]] = None, node: Optional[str] = None, dry_run: bool = False ) -> Dict[str, Any]: """AI-powered predictive scaling based on usage patterns""" try: if dry_run: return { "action": "ai_scaling", "vmid": vmid, "enable_prediction": enable_prediction, "metrics_window": metrics_window, "scaling_policy": scaling_policy or {}, "dry_run": True, "status": "would_execute" } node = node or self.client.get_first_node() # Default scaling policy if scaling_policy is None: scaling_policy = { "cpu_threshold_up": 80, "cpu_threshold_down": 20, "memory_threshold_up": 85, "memory_threshold_down": 30, "scale_up_factor": 1.5, "scale_down_factor": 0.8, "min_cpu": 1, "max_cpu": 16, "min_memory": 1024, # MB "max_memory": 32768 # MB } result = { "vmid": vmid, "node": node, "scaling_policy": scaling_policy, "metrics_window": metrics_window } # Collect historical metrics historical_data = await self._collect_historical_metrics(vmid, metrics_window, node) result["historical_data_points"] = len(historical_data) if len(historical_data) < 10: return { **result, "status": "insufficient_data", "message": "Need at least 10 data points for AI prediction" } # Train or load prediction model model_file = self.models_dir / f"scaling_model_vm_{vmid}.pkl" if enable_prediction: prediction_model = await self._train_scaling_model(historical_data, model_file) result["model_trained"] = True else: prediction_model = await self._load_model(model_file) result["model_loaded"] = prediction_model is not None # Get current VM configuration vm_config = await self.client.nodes(node).qemu(vmid).config.get() current_cpu = vm_config.get("cores", 1) current_memory = vm_config.get("memory", 1024) result["current_resources"] = { "cpu": current_cpu, "memory": current_memory } # Analyze current usage and predict future needs current_metrics = await self._get_current_vm_metrics(vmid, node) if prediction_model and enable_prediction: # Predict future resource usage predictions = await self._predict_resource_usage( prediction_model, historical_data, current_metrics ) result["predictions"] = predictions # Generate scaling recommendations scaling_recommendations = await self._generate_scaling_recommendations( current_metrics, predictions, scaling_policy, current_cpu, current_memory ) result["recommendations"] = scaling_recommendations # Apply scaling if recommended and not in dry run if scaling_recommendations.get("should_scale", False): scaling_result = await self._apply_scaling( vmid, scaling_recommendations, node, dry_run ) result["scaling_applied"] = scaling_result else: # Rule-based scaling without prediction rule_based_scaling = await self._rule_based_scaling( current_metrics, scaling_policy, current_cpu, current_memory ) result["rule_based_recommendations"] = rule_based_scaling logger.info(f"AI scaling analysis completed for VM {vmid}") result["status"] = "completed" return result except Exception as e: error_msg = f"AI scaling failed: {str(e)}" logger.error(error_msg) return format_error(error_msg, {"vmid": vmid}) async def _collect_historical_metrics( self, vmid: int, time_window: str, node: str ) -> List[Dict[str, Any]]: """Collect historical metrics for AI training""" try: # Parse time window if time_window.endswith('d'): days = int(time_window[:-1]) elif time_window.endswith('h'): days = int(time_window[:-1]) / 24 else: days = 7 # Default to 7 days # Generate synthetic historical data for demonstration # In a real implementation, this would query actual metrics storage historical_data = [] for i in range(int(days * 24)): # Hourly data points timestamp = datetime.now() - timedelta(hours=i) # Generate realistic usage patterns with some randomness base_cpu = 30 + 20 * np.sin(i * 2 * np.pi / 24) # Daily pattern base_memory = 50 + 15 * np.sin(i * 2 * np.pi / 24) cpu_usage = max(0, min(100, base_cpu + np.random.normal(0, 10))) memory_usage = max(0, min(100, base_memory + np.random.normal(0, 8))) # Add weekly patterns if timestamp.weekday() < 5: # Weekdays cpu_usage *= 1.2 memory_usage *= 1.1 historical_data.append({ "timestamp": timestamp.isoformat(), "cpu_usage": cpu_usage, "memory_usage": memory_usage, "disk_iops": np.random.uniform(100, 1000), "network_rx": np.random.uniform(1000000, 10000000), # bytes "network_tx": np.random.uniform(1000000, 10000000), "hour_of_day": timestamp.hour, "day_of_week": timestamp.weekday(), "is_weekend": 1 if timestamp.weekday() >= 5 else 0 }) return historical_data except Exception as e: logger.error(f"Historical metrics collection failed: {e}") return [] async def _train_scaling_model( self, historical_data: List[Dict[str, Any]], model_file: Path ) -> Optional[Any]: """Train machine learning model for resource prediction""" try: if len(historical_data) < 20: logger.warning("Insufficient data for model training") return None # Convert to DataFrame df = pd.DataFrame(historical_data) # Prepare features features = [ 'hour_of_day', 'day_of_week', 'is_weekend', 'cpu_usage', 'memory_usage', 'disk_iops' ] X = df[features].values # Create targets (predict next hour's usage) y_cpu = df['cpu_usage'].shift(-1).fillna(method='ffill').values y_memory = df['memory_usage'].shift(-1).fillna(method='ffill').values # Split data X_train, X_test, y_cpu_train, y_cpu_test, y_memory_train, y_memory_test = train_test_split( X, y_cpu, y_memory, test_size=0.2, random_state=42 ) # Scale features X_train_scaled = self.scaler.fit_transform(X_train) X_test_scaled = self.scaler.transform(X_test) # Train models cpu_model = RandomForestRegressor(n_estimators=100, random_state=42) memory_model = RandomForestRegressor(n_estimators=100, random_state=42) cpu_model.fit(X_train_scaled, y_cpu_train) memory_model.fit(X_train_scaled, y_memory_train) # Evaluate models cpu_pred = cpu_model.predict(X_test_scaled) memory_pred = memory_model.predict(X_test_scaled) cpu_mse = mean_squared_error(y_cpu_test, cpu_pred) memory_mse = mean_squared_error(y_memory_test, memory_pred) # Save models model_data = { "cpu_model": cpu_model, "memory_model": memory_model, "scaler": self.scaler, "features": features, "cpu_mse": cpu_mse, "memory_mse": memory_mse, "trained_at": datetime.now().isoformat() } with open(model_file, 'wb') as f: pickle.dump(model_data, f) logger.info(f"Scaling model trained and saved. CPU MSE: {cpu_mse:.2f}, Memory MSE: {memory_mse:.2f}") return model_data except Exception as e: logger.error(f"Model training failed: {e}") return None async def _load_model(self, model_file: Path) -> Optional[Any]: """Load trained model from file""" try: if not model_file.exists(): return None with open(model_file, 'rb') as f: model_data = pickle.load(f) # Check if model is recent (less than 7 days old) trained_at = datetime.fromisoformat(model_data.get("trained_at", "2020-01-01")) if datetime.now() - trained_at > timedelta(days=7): logger.info("Model is outdated, will retrain") return None return model_data except Exception as e: logger.error(f"Model loading failed: {e}") return None async def _get_current_vm_metrics(self, vmid: int, node: str) -> Dict[str, Any]: """Get current VM metrics""" try: vm_status = await self.client.nodes(node).qemu(vmid).status.current.get() current_time = datetime.now() return { "cpu_usage": vm_status.get("cpu", 0) * 100, "memory_usage": (vm_status.get("mem", 0) / vm_status.get("maxmem", 1)) * 100, "disk_iops": vm_status.get("diskread", 0) + vm_status.get("diskwrite", 0), "network_rx": vm_status.get("netin", 0), "network_tx": vm_status.get("netout", 0), "hour_of_day": current_time.hour, "day_of_week": current_time.weekday(), "is_weekend": 1 if current_time.weekday() >= 5 else 0, "timestamp": current_time.isoformat() } except Exception as e: logger.error(f"Current metrics collection failed: {e}") return {} async def _predict_resource_usage( self, model_data: Dict[str, Any], historical_data: List[Dict[str, Any]], current_metrics: Dict[str, Any] ) -> Dict[str, Any]: """Predict future resource usage""" try: cpu_model = model_data["cpu_model"] memory_model = model_data["memory_model"] scaler = model_data["scaler"] features = model_data["features"] # Prepare current metrics for prediction current_features = [current_metrics.get(feature, 0) for feature in features] current_scaled = scaler.transform([current_features]) # Predict next hour cpu_prediction = cpu_model.predict(current_scaled)[0] memory_prediction = memory_model.predict(current_scaled)[0] # Predict next 24 hours future_predictions = [] current_time = datetime.now() for i in range(1, 25): # Next 24 hours future_time = current_time + timedelta(hours=i) future_features = current_features.copy() future_features[0] = future_time.hour # hour_of_day future_features[1] = future_time.weekday() # day_of_week future_features[2] = 1 if future_time.weekday() >= 5 else 0 # is_weekend future_scaled = scaler.transform([future_features]) future_cpu = cpu_model.predict(future_scaled)[0] future_memory = memory_model.predict(future_scaled)[0] future_predictions.append({ "hour": i, "timestamp": future_time.isoformat(), "predicted_cpu": max(0, min(100, future_cpu)), "predicted_memory": max(0, min(100, future_memory)) }) return { "next_hour_cpu": max(0, min(100, cpu_prediction)), "next_hour_memory": max(0, min(100, memory_prediction)), "future_24h": future_predictions, "model_accuracy": { "cpu_mse": model_data.get("cpu_mse", 0), "memory_mse": model_data.get("memory_mse", 0) } } except Exception as e: logger.error(f"Resource prediction failed: {e}") return {} async def _generate_scaling_recommendations( self, current_metrics: Dict[str, Any], predictions: Dict[str, Any], scaling_policy: Dict[str, Any], current_cpu: int, current_memory: int ) -> Dict[str, Any]: """Generate scaling recommendations based on predictions""" try: recommendations = { "should_scale": False, "scale_type": None, "recommended_cpu": current_cpu, "recommended_memory": current_memory, "confidence": 0.0, "reasoning": [] } # Analyze current usage current_cpu_usage = current_metrics.get("cpu_usage", 0) current_memory_usage = current_metrics.get("memory_usage", 0) # Analyze predictions predicted_cpu = predictions.get("next_hour_cpu", current_cpu_usage) predicted_memory = predictions.get("next_hour_memory", current_memory_usage) # Check for scale up conditions scale_up_reasons = [] scale_down_reasons = [] # CPU scaling if (predicted_cpu > scaling_policy["cpu_threshold_up"] or current_cpu_usage > scaling_policy["cpu_threshold_up"]): new_cpu = min( scaling_policy["max_cpu"], int(current_cpu * scaling_policy["scale_up_factor"]) ) if new_cpu > current_cpu: recommendations["recommended_cpu"] = new_cpu scale_up_reasons.append(f"CPU usage predicted to be {predicted_cpu:.1f}%") elif (predicted_cpu < scaling_policy["cpu_threshold_down"] and current_cpu_usage < scaling_policy["cpu_threshold_down"]): new_cpu = max( scaling_policy["min_cpu"], int(current_cpu * scaling_policy["scale_down_factor"]) ) if new_cpu < current_cpu: recommendations["recommended_cpu"] = new_cpu scale_down_reasons.append(f"CPU usage consistently low: {predicted_cpu:.1f}%") # Memory scaling if (predicted_memory > scaling_policy["memory_threshold_up"] or current_memory_usage > scaling_policy["memory_threshold_up"]): new_memory = min( scaling_policy["max_memory"], int(current_memory * scaling_policy["scale_up_factor"]) ) if new_memory > current_memory: recommendations["recommended_memory"] = new_memory scale_up_reasons.append(f"Memory usage predicted to be {predicted_memory:.1f}%") elif (predicted_memory < scaling_policy["memory_threshold_down"] and current_memory_usage < scaling_policy["memory_threshold_down"]): new_memory = max( scaling_policy["min_memory"], int(current_memory * scaling_policy["scale_down_factor"]) ) if new_memory < current_memory: recommendations["recommended_memory"] = new_memory scale_down_reasons.append(f"Memory usage consistently low: {predicted_memory:.1f}%") # Determine scaling action if scale_up_reasons: recommendations["should_scale"] = True recommendations["scale_type"] = "up" recommendations["reasoning"] = scale_up_reasons recommendations["confidence"] = 0.8 elif scale_down_reasons: recommendations["should_scale"] = True recommendations["scale_type"] = "down" recommendations["reasoning"] = scale_down_reasons recommendations["confidence"] = 0.6 return recommendations except Exception as e: logger.error(f"Scaling recommendations generation failed: {e}") return {"should_scale": False, "error": str(e)} async def _apply_scaling( self, vmid: int, recommendations: Dict[str, Any], node: str, dry_run: bool ) -> Dict[str, Any]: """Apply scaling recommendations""" try: if dry_run: return { "action": "apply_scaling", "vmid": vmid, "recommendations": recommendations, "dry_run": True, "status": "would_apply" } scaling_result = { "vmid": vmid, "node": node, "applied_changes": [] } # Get current VM status vm_status = await self.client.nodes(node).qemu(vmid).status.current.get() is_running = vm_status.get("status") == "running" # Apply CPU scaling recommended_cpu = recommendations.get("recommended_cpu") if recommended_cpu: try: await self.client.nodes(node).qemu(vmid).config.put(cores=recommended_cpu) scaling_result["applied_changes"].append({ "resource": "cpu", "new_value": recommended_cpu, "status": "applied" }) # Hot-plug CPU if VM is running if is_running: # Note: Hot CPU scaling may require guest support scaling_result["applied_changes"][-1]["hot_plugged"] = True except Exception as e: scaling_result["applied_changes"].append({ "resource": "cpu", "status": "failed", "error": str(e) }) # Apply Memory scaling recommended_memory = recommendations.get("recommended_memory") if recommended_memory: try: await self.client.nodes(node).qemu(vmid).config.put(memory=recommended_memory) scaling_result["applied_changes"].append({ "resource": "memory", "new_value": recommended_memory, "status": "applied" }) # Hot-plug memory if VM is running and supports it if is_running: scaling_result["applied_changes"][-1]["hot_plugged"] = True except Exception as e: scaling_result["applied_changes"].append({ "resource": "memory", "status": "failed", "error": str(e) }) scaling_result["status"] = "completed" return scaling_result except Exception as e: logger.error(f"Scaling application failed: {e}") return {"status": "failed", "error": str(e)} async def _rule_based_scaling( self, current_metrics: Dict[str, Any], scaling_policy: Dict[str, Any], current_cpu: int, current_memory: int ) -> Dict[str, Any]: """Simple rule-based scaling without AI prediction""" try: current_cpu_usage = current_metrics.get("cpu_usage", 0) current_memory_usage = current_metrics.get("memory_usage", 0) recommendations = { "should_scale": False, "scale_type": None, "recommended_cpu": current_cpu, "recommended_memory": current_memory, "reasoning": [] } # CPU scaling rules if current_cpu_usage > scaling_policy["cpu_threshold_up"]: new_cpu = min( scaling_policy["max_cpu"], int(current_cpu * scaling_policy["scale_up_factor"]) ) if new_cpu > current_cpu: recommendations["recommended_cpu"] = new_cpu recommendations["should_scale"] = True recommendations["scale_type"] = "up" recommendations["reasoning"].append(f"High CPU usage: {current_cpu_usage:.1f}%") elif current_cpu_usage < scaling_policy["cpu_threshold_down"]: new_cpu = max( scaling_policy["min_cpu"], int(current_cpu * scaling_policy["scale_down_factor"]) ) if new_cpu < current_cpu: recommendations["recommended_cpu"] = new_cpu recommendations["should_scale"] = True recommendations["scale_type"] = "down" recommendations["reasoning"].append(f"Low CPU usage: {current_cpu_usage:.1f}%") # Memory scaling rules if current_memory_usage > scaling_policy["memory_threshold_up"]: new_memory = min( scaling_policy["max_memory"], int(current_memory * scaling_policy["scale_up_factor"]) ) if new_memory > current_memory: recommendations["recommended_memory"] = new_memory recommendations["should_scale"] = True if not recommendations["scale_type"]: recommendations["scale_type"] = "up" recommendations["reasoning"].append(f"High memory usage: {current_memory_usage:.1f}%") elif current_memory_usage < scaling_policy["memory_threshold_down"]: new_memory = max( scaling_policy["min_memory"], int(current_memory * scaling_policy["scale_down_factor"]) ) if new_memory < current_memory: recommendations["recommended_memory"] = new_memory recommendations["should_scale"] = True if not recommendations["scale_type"]: recommendations["scale_type"] = "down" recommendations["reasoning"].append(f"Low memory usage: {current_memory_usage:.1f}%") return recommendations except Exception as e: logger.error(f"Rule-based scaling failed: {e}") return {"should_scale": False, "error": str(e)} async def anomaly_detection( self, detection_type: str = "performance", sensitivity: str = "medium", alert_threshold: float = 0.85, auto_remediation: bool = False, dry_run: bool = False ) -> Dict[str, Any]: """AI-powered anomaly detection for proactive issue resolution""" try: if dry_run: return { "action": "anomaly_detection", "detection_type": detection_type, "sensitivity": sensitivity, "alert_threshold": alert_threshold, "auto_remediation": auto_remediation, "dry_run": True, "status": "would_execute" } result = { "detection_type": detection_type, "sensitivity": sensitivity, "alert_threshold": alert_threshold, "auto_remediation": auto_remediation } # Collect current system metrics system_metrics = await self._collect_system_metrics() result["metrics_collected"] = len(system_metrics) # Load or train anomaly detection model model_file = self.models_dir / f"anomaly_model_{detection_type}.pkl" anomaly_model = await self._load_or_train_anomaly_model( model_file, detection_type, sensitivity ) if not anomaly_model: return { **result, "status": "model_unavailable", "message": "Could not load or train anomaly detection model" } # Detect anomalies anomalies = await self._detect_anomalies( anomaly_model, system_metrics, alert_threshold ) result["anomalies_detected"] = len(anomalies) result["anomalies"] = anomalies # Generate remediation suggestions if anomalies: remediation_suggestions = await self._generate_remediation_suggestions(anomalies) result["remediation_suggestions"] = remediation_suggestions # Apply auto-remediation if enabled if auto_remediation: remediation_results = await self._apply_auto_remediation( remediation_suggestions, dry_run ) result["auto_remediation_results"] = remediation_results logger.info(f"Anomaly detection completed. Found {len(anomalies)} anomalies") result["status"] = "completed" return result except Exception as e: error_msg = f"Anomaly detection failed: {str(e)}" logger.error(error_msg) return format_error(error_msg, {"detection_type": detection_type}) async def _collect_system_metrics(self) -> List[Dict[str, Any]]: """Collect system-wide metrics for anomaly detection""" try: metrics = [] # Get all nodes nodes = await self.client.nodes.get() for node_info in nodes: node_name = node_info["node"] try: # Node metrics node_status = await self.client.nodes(node_name).status.get() # VM metrics vms = await self.client.nodes(node_name).qemu.get() for vm in vms: vmid = vm["vmid"] try: vm_status = await self.client.nodes(node_name).qemu(vmid).status.current.get() metrics.append({ "timestamp": datetime.now().isoformat(), "node": node_name, "vmid": vmid, "vm_name": vm.get("name", f"vm-{vmid}"), "vm_status": vm_status.get("status", "unknown"), "cpu_usage": vm_status.get("cpu", 0) * 100, "memory_usage": (vm_status.get("mem", 0) / vm_status.get("maxmem", 1)) * 100, "disk_read": vm_status.get("diskread", 0), "disk_write": vm_status.get("diskwrite", 0), "network_in": vm_status.get("netin", 0), "network_out": vm_status.get("netout", 0), "uptime": vm_status.get("uptime", 0), "node_cpu": node_status.get("cpu", 0) * 100, "node_memory": (node_status.get("memory", {}).get("used", 0) / node_status.get("memory", {}).get("total", 1)) * 100 }) except Exception as e: logger.warning(f"Failed to collect metrics for VM {vmid}: {e}") except Exception as e: logger.warning(f"Failed to collect metrics for node {node_name}: {e}") return metrics except Exception as e: logger.error(f"System metrics collection failed: {e}") return [] async def _load_or_train_anomaly_model( self, model_file: Path, detection_type: str, sensitivity: str ) -> Optional[Any]: """Load existing or train new anomaly detection model""" try: # Try to load existing model if model_file.exists(): with open(model_file, 'rb') as f: model_data = pickle.load(f) # Check if model is recent trained_at = datetime.fromisoformat(model_data.get("trained_at", "2020-01-01")) if datetime.now() - trained_at < timedelta(days=3): return model_data # Train new model with synthetic data # In production, use actual historical data training_data = await self._generate_training_data_for_anomaly_detection() if len(training_data) < 100: logger.warning("Insufficient training data for anomaly detection") return None # Prepare features df = pd.DataFrame(training_data) features = ['cpu_usage', 'memory_usage', 'disk_read', 'disk_write', 'network_in', 'network_out', 'node_cpu', 'node_memory'] X = df[features].fillna(0).values # Configure contamination based on sensitivity contamination_map = { "low": 0.05, "medium": 0.1, "high": 0.15 } contamination = contamination_map.get(sensitivity, 0.1) # Train Isolation Forest model model = IsolationForest( contamination=contamination, random_state=42, n_estimators=100 ) model.fit(X) # Create scaler for normalization scaler = StandardScaler() X_scaled = scaler.fit_transform(X) model_data = { "model": model, "scaler": scaler, "features": features, "contamination": contamination, "detection_type": detection_type, "sensitivity": sensitivity, "trained_at": datetime.now().isoformat() } # Save model with open(model_file, 'wb') as f: pickle.dump(model_data, f) logger.info(f"Anomaly detection model trained for {detection_type}") return model_data except Exception as e: logger.error(f"Anomaly model loading/training failed: {e}") return None async def _generate_training_data_for_anomaly_detection(self) -> List[Dict[str, Any]]: """Generate synthetic training data for anomaly detection""" try: training_data = [] # Generate normal operating patterns for i in range(500): # Normal patterns with some variation cpu_usage = np.random.normal(40, 15) memory_usage = np.random.normal(60, 20) disk_read = np.random.exponential(1000000) disk_write = np.random.exponential(500000) network_in = np.random.exponential(5000000) network_out = np.random.exponential(3000000) node_cpu = np.random.normal(30, 10) node_memory = np.random.normal(50, 15) training_data.append({ "cpu_usage": max(0, min(100, cpu_usage)), "memory_usage": max(0, min(100, memory_usage)), "disk_read": max(0, disk_read), "disk_write": max(0, disk_write), "network_in": max(0, network_in), "network_out": max(0, network_out), "node_cpu": max(0, min(100, node_cpu)), "node_memory": max(0, min(100, node_memory)) }) # Add some anomalous patterns for i in range(50): # Anomalous patterns if i % 3 == 0: # High CPU anomaly cpu_usage = np.random.uniform(90, 100) memory_usage = np.random.normal(60, 20) elif i % 3 == 1: # High memory anomaly cpu_usage = np.random.normal(40, 15) memory_usage = np.random.uniform(90, 100) else: # Network anomaly cpu_usage = np.random.normal(40, 15) memory_usage = np.random.normal(60, 20) disk_read = np.random.exponential(10000000) if i % 4 == 0 else np.random.exponential(1000000) disk_write = np.random.exponential(5000000) if i % 4 == 0 else np.random.exponential(500000) network_in = np.random.exponential(50000000) if i % 5 == 0 else np.random.exponential(5000000) network_out = np.random.exponential(30000000) if i % 5 == 0 else np.random.exponential(3000000) node_cpu = np.random.uniform(80, 100) if i % 6 == 0 else np.random.normal(30, 10) node_memory = np.random.uniform(85, 100) if i % 6 == 0 else np.random.normal(50, 15) training_data.append({ "cpu_usage": max(0, min(100, cpu_usage)), "memory_usage": max(0, min(100, memory_usage)), "disk_read": max(0, disk_read), "disk_write": max(0, disk_write), "network_in": max(0, network_in), "network_out": max(0, network_out), "node_cpu": max(0, min(100, node_cpu)), "node_memory": max(0, min(100, node_memory)) }) return training_data except Exception as e: logger.error(f"Training data generation failed: {e}") return [] async def _detect_anomalies( self, model_data: Dict[str, Any], current_metrics: List[Dict[str, Any]], threshold: float ) -> List[Dict[str, Any]]: """Detect anomalies in current metrics""" try: if not current_metrics: return [] model = model_data["model"] scaler = model_data["scaler"] features = model_data["features"] anomalies = [] for metric in current_metrics: # Prepare features feature_values = [metric.get(feature, 0) for feature in features] X = np.array([feature_values]) X_scaled = scaler.transform(X) # Predict anomaly anomaly_score = model.decision_function(X_scaled)[0] is_anomaly = model.predict(X_scaled)[0] == -1 # Convert score to probability-like value anomaly_probability = 1 / (1 + np.exp(anomaly_score)) if is_anomaly and anomaly_probability >= threshold: anomalies.append({ "node": metric.get("node"), "vmid": metric.get("vmid"), "vm_name": metric.get("vm_name"), "anomaly_score": float(anomaly_score), "anomaly_probability": float(anomaly_probability), "detected_at": datetime.now().isoformat(), "metrics": {feature: metric.get(feature, 0) for feature in features}, "severity": "high" if anomaly_probability > 0.9 else "medium" }) return anomalies except Exception as e: logger.error(f"Anomaly detection failed: {e}") return [] async def _generate_remediation_suggestions( self, anomalies: List[Dict[str, Any]] ) -> List[Dict[str, Any]]: """Generate remediation suggestions for detected anomalies""" try: suggestions = [] for anomaly in anomalies: metrics = anomaly.get("metrics", {}) suggestion = { "anomaly_id": f"{anomaly.get('node')}_{anomaly.get('vmid')}_{int(datetime.now().timestamp())}", "node": anomaly.get("node"), "vmid": anomaly.get("vmid"), "vm_name": anomaly.get("vm_name"), "severity": anomaly.get("severity"), "actions": [] } # CPU-related remediation if metrics.get("cpu_usage", 0) > 90: suggestion["actions"].append({ "type": "scale_cpu", "description": "Increase CPU allocation", "priority": "high", "estimated_impact": "Reduce CPU bottleneck" }) # Memory-related remediation if metrics.get("memory_usage", 0) > 90: suggestion["actions"].append({ "type": "scale_memory", "description": "Increase memory allocation", "priority": "high", "estimated_impact": "Prevent memory pressure" }) # Disk I/O remediation if metrics.get("disk_read", 0) + metrics.get("disk_write", 0) > 10000000: suggestion["actions"].append({ "type": "optimize_storage", "description": "Optimize disk I/O or migrate to faster storage", "priority": "medium", "estimated_impact": "Improve disk performance" }) # Network remediation if metrics.get("network_in", 0) + metrics.get("network_out", 0) > 50000000: suggestion["actions"].append({ "type": "network_optimization", "description": "Check network configuration and bandwidth", "priority": "medium", "estimated_impact": "Reduce network bottleneck" }) # Node-level issues if metrics.get("node_cpu", 0) > 85 or metrics.get("node_memory", 0) > 85: suggestion["actions"].append({ "type": "migrate_vm", "description": "Consider migrating VM to less loaded node", "priority": "medium", "estimated_impact": "Distribute load across cluster" }) suggestions.append(suggestion) return suggestions except Exception as e: logger.error(f"Remediation suggestions generation failed: {e}") return [] async def _apply_auto_remediation( self, suggestions: List[Dict[str, Any]], dry_run: bool ) -> List[Dict[str, Any]]: """Apply automatic remediation actions""" try: results = [] for suggestion in suggestions: vmid = suggestion.get("vmid") node = suggestion.get("node") for action in suggestion.get("actions", []): action_result = { "anomaly_id": suggestion.get("anomaly_id"), "vmid": vmid, "action_type": action.get("type"), "description": action.get("description"), "status": "pending" } if dry_run: action_result["status"] = "dry_run" results.append(action_result) continue try: if action["type"] == "scale_cpu": # Get current config and scale up vm_config = await self.client.nodes(node).qemu(vmid).config.get() current_cpu = vm_config.get("cores", 1) new_cpu = min(16, int(current_cpu * 1.5)) await self.client.nodes(node).qemu(vmid).config.put(cores=new_cpu) action_result["status"] = "applied" action_result["details"] = f"CPU scaled from {current_cpu} to {new_cpu}" elif action["type"] == "scale_memory": # Get current config and scale up vm_config = await self.client.nodes(node).qemu(vmid).config.get() current_memory = vm_config.get("memory", 1024) new_memory = min(32768, int(current_memory * 1.5)) await self.client.nodes(node).qemu(vmid).config.put(memory=new_memory) action_result["status"] = "applied" action_result["details"] = f"Memory scaled from {current_memory}MB to {new_memory}MB" else: action_result["status"] = "not_implemented" action_result["details"] = f"Auto-remediation for {action['type']} not yet implemented" except Exception as e: action_result["status"] = "failed" action_result["error"] = str(e) results.append(action_result) return results except Exception as e: logger.error(f"Auto-remediation failed: {e}") return [] async def auto_optimize( self, optimization_scope: str = "all", learning_period: int = 7, apply_recommendations: bool = False, rollback_enabled: bool = True, dry_run: bool = False ) -> Dict[str, Any]: """Automatically optimize VM configurations based on usage patterns""" try: if dry_run: return { "action": "auto_optimize", "optimization_scope": optimization_scope, "learning_period": learning_period, "apply_recommendations": apply_recommendations, "rollback_enabled": rollback_enabled, "dry_run": True, "status": "would_execute" } result = { "optimization_scope": optimization_scope, "learning_period": learning_period, "apply_recommendations": apply_recommendations, "rollback_enabled": rollback_enabled } # Collect optimization data optimization_data = await self._collect_optimization_data( optimization_scope, learning_period ) result["data_collected"] = len(optimization_data) if not optimization_data: return { **result, "status": "no_data", "message": "No data available for optimization" } # Generate optimization recommendations recommendations = await self._generate_optimization_recommendations( optimization_data ) result["recommendations"] = recommendations result["recommendations_count"] = len(recommendations) # Apply recommendations if requested if apply_recommendations and recommendations: application_results = await self._apply_optimization_recommendations( recommendations, rollback_enabled, dry_run ) result["application_results"] = application_results logger.info(f"Auto-optimization completed with {len(recommendations)} recommendations") result["status"] = "completed" return result except Exception as e: error_msg = f"Auto-optimization failed: {str(e)}" logger.error(error_msg) return format_error(error_msg, {"optimization_scope": optimization_scope}) async def _collect_optimization_data( self, scope: str, learning_period: int ) -> List[Dict[str, Any]]: """Collect data for optimization analysis""" try: # This would collect actual historical data # For now, generate synthetic data optimization_data = [] nodes = await self.client.nodes.get() for node_info in nodes: node_name = node_info["node"] if scope in ["all", "vm"]: vms = await self.client.nodes(node_name).qemu.get() for vm in vms: vmid = vm["vmid"] # Generate historical usage patterns for day in range(learning_period): date = datetime.now() - timedelta(days=day) # Simulate daily patterns for hour in range(24): timestamp = date.replace(hour=hour, minute=0, second=0) # Generate realistic usage with patterns base_cpu = 30 + 20 * np.sin(hour * 2 * np.pi / 24) base_memory = 50 + 15 * np.sin(hour * 2 * np.pi / 24) cpu_usage = max(0, min(100, base_cpu + np.random.normal(0, 10))) memory_usage = max(0, min(100, base_memory + np.random.normal(0, 8))) optimization_data.append({ "timestamp": timestamp.isoformat(), "node": node_name, "vmid": vmid, "vm_name": vm.get("name", f"vm-{vmid}"), "cpu_usage": cpu_usage, "memory_usage": memory_usage, "disk_iops": np.random.uniform(100, 1000), "network_throughput": np.random.uniform(1000000, 10000000), "hour_of_day": hour, "day_of_week": timestamp.weekday(), "is_weekend": 1 if timestamp.weekday() >= 5 else 0 }) return optimization_data except Exception as e: logger.error(f"Optimization data collection failed: {e}") return [] async def _generate_optimization_recommendations( self, optimization_data: List[Dict[str, Any]] ) -> List[Dict[str, Any]]: """Generate optimization recommendations based on usage patterns""" try: recommendations = [] # Group data by VM vm_data = {} for data_point in optimization_data: vmid = data_point["vmid"] if vmid not in vm_data: vm_data[vmid] = [] vm_data[vmid].append(data_point) # Analyze each VM for vmid, vm_points in vm_data.items(): if len(vm_points) < 10: continue df = pd.DataFrame(vm_points) # Calculate statistics cpu_stats = { "mean": df["cpu_usage"].mean(), "max": df["cpu_usage"].max(), "min": df["cpu_usage"].min(), "std": df["cpu_usage"].std(), "p95": df["cpu_usage"].quantile(0.95), "p99": df["cpu_usage"].quantile(0.99) } memory_stats = { "mean": df["memory_usage"].mean(), "max": df["memory_usage"].max(), "min": df["memory_usage"].min(), "std": df["memory_usage"].std(), "p95": df["memory_usage"].quantile(0.95), "p99": df["memory_usage"].quantile(0.99) } # Generate recommendations vm_recommendations = { "vmid": vmid, "vm_name": vm_points[0]["vm_name"], "node": vm_points[0]["node"], "analysis_period": f"{len(vm_points)} data points", "recommendations": [] } # CPU recommendations if cpu_stats["p95"] < 30: vm_recommendations["recommendations"].append({ "type": "cpu_downsize", "current_usage": f"P95: {cpu_stats['p95']:.1f}%", "recommendation": "Consider reducing CPU allocation", "potential_savings": "20-30% resource cost", "confidence": 0.8 }) elif cpu_stats["p95"] > 80: vm_recommendations["recommendations"].append({ "type": "cpu_upsize", "current_usage": f"P95: {cpu_stats['p95']:.1f}%", "recommendation": "Consider increasing CPU allocation", "performance_impact": "Reduce CPU bottlenecks", "confidence": 0.9 }) # Memory recommendations if memory_stats["p95"] < 40: vm_recommendations["recommendations"].append({ "type": "memory_downsize", "current_usage": f"P95: {memory_stats['p95']:.1f}%", "recommendation": "Consider reducing memory allocation", "potential_savings": "15-25% resource cost", "confidence": 0.7 }) elif memory_stats["p95"] > 85: vm_recommendations["recommendations"].append({ "type": "memory_upsize", "current_usage": f"P95: {memory_stats['p95']:.1f}%", "recommendation": "Consider increasing memory allocation", "performance_impact": "Prevent memory pressure", "confidence": 0.9 }) # Usage pattern recommendations if cpu_stats["std"] < 5 and memory_stats["std"] < 5: vm_recommendations["recommendations"].append({ "type": "steady_workload", "pattern": "Stable resource usage", "recommendation": "Good candidate for resource reservation", "optimization": "Enable resource guarantees", "confidence": 0.8 }) if len(vm_recommendations["recommendations"]) > 0: recommendations.append(vm_recommendations) return recommendations except Exception as e: logger.error(f"Optimization recommendations generation failed: {e}") return [] async def _apply_optimization_recommendations( self, recommendations: List[Dict[str, Any]], rollback_enabled: bool, dry_run: bool ) -> List[Dict[str, Any]]: """Apply optimization recommendations""" try: application_results = [] for vm_rec in recommendations: vmid = vm_rec["vmid"] node = vm_rec["node"] # Store original configuration for rollback if rollback_enabled: try: original_config = await self.client.nodes(node).qemu(vmid).config.get() rollback_file = self.models_dir / f"rollback_vm_{vmid}_{int(datetime.now().timestamp())}.json" with open(rollback_file, 'w') as f: json.dump(original_config, f, default=str) except Exception as e: logger.warning(f"Could not save rollback config for VM {vmid}: {e}") for recommendation in vm_rec["recommendations"]: result = { "vmid": vmid, "recommendation_type": recommendation["type"], "status": "pending" } if dry_run: result["status"] = "dry_run" result["would_apply"] = recommendation["recommendation"] application_results.append(result) continue try: rec_type = recommendation["type"] if rec_type in ["cpu_upsize", "cpu_downsize"]: # Get current CPU and apply recommendation vm_config = await self.client.nodes(node).qemu(vmid).config.get() current_cpu = vm_config.get("cores", 1) if rec_type == "cpu_upsize": new_cpu = min(16, int(current_cpu * 1.5)) else: new_cpu = max(1, int(current_cpu * 0.8)) await self.client.nodes(node).qemu(vmid).config.put(cores=new_cpu) result["status"] = "applied" result["details"] = f"CPU changed from {current_cpu} to {new_cpu}" elif rec_type in ["memory_upsize", "memory_downsize"]: # Get current memory and apply recommendation vm_config = await self.client.nodes(node).qemu(vmid).config.get() current_memory = vm_config.get("memory", 1024) if rec_type == "memory_upsize": new_memory = min(32768, int(current_memory * 1.5)) else: new_memory = max(512, int(current_memory * 0.8)) await self.client.nodes(node).qemu(vmid).config.put(memory=new_memory) result["status"] = "applied" result["details"] = f"Memory changed from {current_memory}MB to {new_memory}MB" else: result["status"] = "not_implemented" result["details"] = f"Optimization for {rec_type} not yet implemented" except Exception as e: result["status"] = "failed" result["error"] = str(e) application_results.append(result) return application_results except Exception as e: logger.error(f"Optimization application failed: {e}") return []