Resource Management Service

import asyncio
import boto3
from typing import Dict, List, Any, Optional, Union
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import json
import numpy as np
from kubernetes import client, config
import pandas as pd
 
class ResourceType(Enum):
    COMPUTE = "compute"
    STORAGE = "storage"
    NETWORK = "network"
    DATABASE = "database"
    CACHE = "cache"
 
class ScalingAction(Enum):
    SCALE_UP = "scale_up"
    SCALE_DOWN = "scale_down"
    SCALE_OUT = "scale_out"
    SCALE_IN = "scale_in"
    NO_ACTION = "no_action"
 
@dataclass
class ResourceRequirement:
    cpu: float
    memory: str  # e.g., "2Gi"
    storage: str = "10Gi"
    network_bandwidth: str = "1Gbps"
    gpu: int = 0
    constraints: Dict[str, Any] = field(default_factory=dict)
 
@dataclass
class ResourceMetrics:
    timestamp: datetime
    cpu_utilization: float
    memory_utilization: float
    storage_utilization: float
    network_utilization: float
    cost_per_hour: float
    performance_score: float
 
@dataclass
class ScalingRecommendation:
    action: ScalingAction
    target_replicas: int
    confidence: float
    reasoning: str
    cost_impact: float
    performance_impact: float
 
class ResourceManager:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.cloud_providers = self._initialize_cloud_providers(config)
        self.k8s_client = self._initialize_k8s_client()
        self.cost_tracker = CostTracker(config.get('cost_tracking'))
        self.performance_analyzer = PerformanceAnalyzer(config.get('performance'))
        self.predictive_scaler = PredictiveScaler(config.get('ml_models'))
        
        # Resource management policies
        self.scaling_policies = config.get('scaling_policies', {})
        self.cost_policies = config.get('cost_policies', {})
        self.performance_thresholds = config.get('performance_thresholds', {})
    
    def _initialize_cloud_providers(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Initialize cloud provider clients"""
        providers = {}
        
        if 'aws' in config:
            providers['aws'] = boto3.Session(
                aws_access_key_id=config['aws']['access_key'],
                aws_secret_access_key=config['aws']['secret_key'],
                region_name=config['aws']['region']
            )
        
        if 'azure' in config:
            # Azure SDK initialization
            pass
        
        if 'gcp' in config:
            # GCP SDK initialization
            pass
        
        return providers
    
    def _initialize_k8s_client(self):
        """Initialize Kubernetes client"""
        try:
            config.load_incluster_config()
        except:
            config.load_kube_config()
        
        return client.ApiClient()
    
    async def provision_resources(self, resource_spec: Dict[str, Any]) -> Dict[str, Any]:
        """Provision resources based on specification"""
        
        # Analyze resource requirements
        requirements = self._analyze_resource_requirements(resource_spec)
        
        # Find optimal cloud provider and region
        optimal_placement = await self._find_optimal_placement(requirements)
        
        # Provision resources
        provisioning_result = await self._provision_infrastructure(
            optimal_placement, requirements
        )
        
        # Track provisioned resources
        await self._track_provisioned_resources(provisioning_result)
        
        return provisioning_result
    
    async def auto_scale_workload(self, workload_name: str, 
                                 namespace: str = "default") -> ScalingRecommendation:
        """Automatically scale workload based on metrics and policies"""
        
        # Get current workload metrics
        current_metrics = await self._get_workload_metrics(workload_name, namespace)
        
        # Get historical metrics for prediction
        historical_metrics = await self._get_historical_metrics(
            workload_name, namespace, hours=24
        )
        
        # Generate scaling recommendation
        recommendation = await self.predictive_scaler.recommend_scaling(
            current_metrics, historical_metrics, self.scaling_policies
        )
        
        # Execute scaling if confidence is high enough
        if recommendation.confidence > 0.8:
            await self._execute_scaling(workload_name, namespace, recommendation)
        
        return recommendation
    
    async def optimize_costs(self, optimization_scope: str = "cluster") -> Dict[str, Any]:
        """Optimize resource costs through rightsizing and efficiency improvements"""
        
        optimization_results = {
            'current_cost': 0,
            'projected_savings': 0,
            'recommendations': [],
            'actions_taken': []
        }
        
        # Get current cost breakdown
        current_costs = await self.cost_tracker.get_cost_breakdown(optimization_scope)
        optimization_results['current_cost'] = current_costs['total']
        
        # Identify underutilized resources
        underutilized = await self._identify_underutilized_resources()
        
        # Generate rightsizing recommendations
        rightsizing_recs = await self._generate_rightsizing_recommendations(
            underutilized
        )
        optimization_results['recommendations'].extend(rightsizing_recs)
        
        # Identify zombie resources
        zombie_resources = await self._identify_zombie_resources()
        
        # Generate cleanup recommendations
        cleanup_recs = await self._generate_cleanup_recommendations(
            zombie_resources
        )
        optimization_results['recommendations'].extend(cleanup_recs)
        
        # Calculate projected savings
        projected_savings = sum(
            rec['monthly_savings'] for rec in optimization_results['recommendations']
        )
        optimization_results['projected_savings'] = projected_savings
        
        # Auto-execute low-risk optimizations
        auto_actions = await self._execute_auto_optimizations(
            optimization_results['recommendations']
        )
        optimization_results['actions_taken'] = auto_actions
        
        return optimization_results
    
    async def _get_workload_metrics(self, workload_name: str, 
                                   namespace: str) -> ResourceMetrics:
        """Get current metrics for a workload"""
        
        # Query Prometheus for metrics
        metrics_query = f"""
        avg(rate(container_cpu_usage_seconds_total{{
            pod=~"{workload_name}.*",
            namespace="{namespace}"
        }}[5m])) * 100
        """
        
        cpu_utilization = await self._query_prometheus(metrics_query)
        
        # Get memory utilization
        memory_query = f"""
        avg(container_memory_usage_bytes{{
            pod=~"{workload_name}.*",
            namespace="{namespace}"
        }}) / avg(container_spec_memory_limit_bytes{{
            pod=~"{workload_name}.*",
            namespace="{namespace}"
        }}) * 100
        """
        
        memory_utilization = await self._query_prometheus(memory_query)
        
        # Get cost information
        cost_per_hour = await self.cost_tracker.get_workload_cost(
            workload_name, namespace
        )
        
        return ResourceMetrics(
            timestamp=datetime.utcnow(),
            cpu_utilization=cpu_utilization or 0,
            memory_utilization=memory_utilization or 0,
            storage_utilization=0,  # TODO: Implement storage metrics
            network_utilization=0,  # TODO: Implement network metrics
            cost_per_hour=cost_per_hour,
            performance_score=await self.performance_analyzer.calculate_score(
                workload_name, namespace
            )
        )
    
    async def _identify_underutilized_resources(self) -> List[Dict[str, Any]]:
        """Identify resources with low utilization"""
        underutilized = []
        
        # Get all workloads
        apps_v1 = client.AppsV1Api(self.k8s_client)
        deployments = apps_v1.list_deployment_for_all_namespaces()
        
        for deployment in deployments.items:
            workload_name = deployment.metadata.name
            namespace = deployment.metadata.namespace
            
            # Get metrics for the last 7 days
            metrics_history = await self._get_historical_metrics(
                workload_name, namespace, hours=168  # 7 days
            )
            
            if metrics_history:
                avg_cpu = np.mean([m.cpu_utilization for m in metrics_history])
                avg_memory = np.mean([m.memory_utilization for m in metrics_history])
                
                # Check if underutilized (< 20% CPU or < 30% memory)
                if avg_cpu < 20 or avg_memory < 30:
                    underutilized.append({
                        'name': workload_name,
                        'namespace': namespace,
                        'avg_cpu_utilization': avg_cpu,
                        'avg_memory_utilization': avg_memory,
                        'replicas': deployment.spec.replicas,
                        'cost_per_hour': await self.cost_tracker.get_workload_cost(
                            workload_name, namespace
                        )
                    })
        
        return underutilized
    
    async def _generate_rightsizing_recommendations(self, 
                                                   underutilized: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Generate rightsizing recommendations"""
        recommendations = []
        
        for workload in underutilized:
            current_cost = workload['cost_per_hour'] * 24 * 30  # Monthly cost
            
            # Calculate recommended resource allocation
            recommended_cpu = max(workload['avg_cpu_utilization'] * 1.2, 10)  # 20% buffer, min 10%
            recommended_memory = max(workload['avg_memory_utilization'] * 1.2, 20)  # 20% buffer, min 20%
            
            # Calculate cost savings
            cpu_reduction = max(0, 100 - recommended_cpu) / 100
            memory_reduction = max(0, 100 - recommended_memory) / 100
            estimated_savings = current_cost * max(cpu_reduction, memory_reduction) * 0.6  # Conservative estimate
            
            if estimated_savings > 10:  # Only recommend if savings > $10/month
                recommendations.append({
                    'type': 'rightsizing',
                    'workload': workload['name'],
                    'namespace': workload['namespace'],
                    'current_cpu_utilization': workload['avg_cpu_utilization'],
                    'current_memory_utilization': workload['avg_memory_utilization'],
                    'recommended_cpu_allocation': recommended_cpu,
                    'recommended_memory_allocation': recommended_memory,
                    'monthly_savings': estimated_savings,
                    'confidence': 0.8,
                    'risk_level': 'low'
                })
        
        return recommendations
 
class PredictiveScaler:
    """AI-powered predictive scaling engine"""
    
    def __init__(self, ml_config: Dict[str, Any]):
        self.ml_config = ml_config or {}
        self.models = {}
        self._load_models()
    
    def _load_models(self):
        """Load pre-trained ML models for prediction"""
        # Load time series forecasting model
        # Load anomaly detection model
        # Load workload classification model
        pass
    
    async def recommend_scaling(self, current_metrics: ResourceMetrics,
                               historical_metrics: List[ResourceMetrics],
                               policies: Dict[str, Any]) -> ScalingRecommendation:
        """Generate intelligent scaling recommendation"""
        
        if not historical_metrics:
            return ScalingRecommendation(
                action=ScalingAction.NO_ACTION,
                target_replicas=1,
                confidence=0.5,
                reasoning="Insufficient historical data",
                cost_impact=0,
                performance_impact=0
            )
        
        # Predict future resource usage
        predicted_metrics = await self._predict_future_usage(
            current_metrics, historical_metrics
        )
        
        # Determine scaling action based on predictions and policies
        scaling_decision = await self._make_scaling_decision(
            current_metrics, predicted_metrics, policies
        )
        
        return scaling_decision
    
    async def _predict_future_usage(self, current: ResourceMetrics,
                                   history: List[ResourceMetrics]) -> ResourceMetrics:
        """Predict future resource usage using time series analysis"""
        
        # Extract time series data
        timestamps = [m.timestamp for m in history]
        cpu_values = [m.cpu_utilization for m in history]
        memory_values = [m.memory_utilization for m in history]
        
        # Simple linear trend prediction (in production, use more sophisticated models)
        if len(cpu_values) >= 2:
            cpu_trend = np.polyfit(range(len(cpu_values)), cpu_values, 1)[0]
            predicted_cpu = current.cpu_utilization + cpu_trend * 12  # 1 hour ahead
        else:
            predicted_cpu = current.cpu_utilization
        
        if len(memory_values) >= 2:
            memory_trend = np.polyfit(range(len(memory_values)), memory_values, 1)[0]
            predicted_memory = current.memory_utilization + memory_trend * 12
        else:
            predicted_memory = current.memory_utilization
        
        return ResourceMetrics(
            timestamp=datetime.utcnow() + timedelta(hours=1),
            cpu_utilization=max(0, min(100, predicted_cpu)),
            memory_utilization=max(0, min(100, predicted_memory)),
            storage_utilization=current.storage_utilization,
            network_utilization=current.network_utilization,
            cost_per_hour=current.cost_per_hour,
            performance_score=current.performance_score
        )
    
    async def _make_scaling_decision(self, current: ResourceMetrics,
                                    predicted: ResourceMetrics,
                                    policies: Dict[str, Any]) -> ScalingRecommendation:
        """Make scaling decision based on current state, predictions, and policies"""
        
        # Get scaling thresholds from policies
        scale_up_cpu_threshold = policies.get('scale_up_cpu_threshold', 80)
        scale_down_cpu_threshold = policies.get('scale_down_cpu_threshold', 20)
        scale_up_memory_threshold = policies.get('scale_up_memory_threshold', 85)
        scale_down_memory_threshold = policies.get('scale_down_memory_threshold', 30)
        
        # Determine scaling action
        action = ScalingAction.NO_ACTION
        target_replicas = 1
        confidence = 0.9
        reasoning = "No scaling needed"
        
        # Check if scale up is needed
        if (predicted.cpu_utilization > scale_up_cpu_threshold or 
            predicted.memory_utilization > scale_up_memory_threshold):
            action = ScalingAction.SCALE_OUT
            target_replicas = min(10, int(predicted.cpu_utilization / 50) + 1)  # Simple scaling logic
            reasoning = f"Predicted high utilization: CPU {predicted.cpu_utilization:.1f}%, Memory {predicted.memory_utilization:.1f}%"
        
        # Check if scale down is possible
        elif (current.cpu_utilization < scale_down_cpu_threshold and 
              current.memory_utilization < scale_down_memory_threshold and
              predicted.cpu_utilization < scale_down_cpu_threshold):
            action = ScalingAction.SCALE_IN
            target_replicas = max(1, target_replicas - 1)
            reasoning = f"Low utilization detected: CPU {current.cpu_utilization:.1f}%, Memory {current.memory_utilization:.1f}%"
        
        # Calculate cost and performance impact
        cost_impact = self._calculate_cost_impact(action, target_replicas, current.cost_per_hour)
        performance_impact = self._calculate_performance_impact(action, predicted)
        
        return ScalingRecommendation(
            action=action,
            target_replicas=target_replicas,
            confidence=confidence,
            reasoning=reasoning,
            cost_impact=cost_impact,
            performance_impact=performance_impact
        )
 
# Usage example
resource_config = {
    'aws': {
        'access_key': 'your-access-key',
        'secret_key': 'your-secret-key',
        'region': 'us-west-2'
    },
    'kubernetes': {
        'config_path': '/etc/kubernetes/config'
    },
    'scaling_policies': {
        'scale_up_cpu_threshold': 75,
        'scale_down_cpu_threshold': 25,
        'scale_up_memory_threshold': 80,
        'scale_down_memory_threshold': 35,
        'min_replicas': 1,
        'max_replicas': 20
    },
    'cost_tracking': {
        'prometheus_url': 'http://prometheus.monitoring:9090'
    },
    'performance': {
        'sla_targets': {
            'response_time_p95': 500,  # ms
            'availability': 99.9  # percentage
        }
    }
}
 
# Initialize resource manager
resource_manager = ResourceManager(resource_config)
 
# Auto-scale a workload
scaling_recommendation = await resource_manager.auto_scale_workload(
    workload_name='user-service',
    namespace='production'
)
 
print(f"Scaling recommendation: {scaling_recommendation.action.value}")
print(f"Target replicas: {scaling_recommendation.target_replicas}")
print(f"Confidence: {scaling_recommendation.confidence:.2f}")
print(f"Reasoning: {scaling_recommendation.reasoning}")
 
# Optimize costs
cost_optimization = await resource_manager.optimize_costs('cluster')
print(f"Current monthly cost: ${cost_optimization['current_cost']:.2f}")
print(f"Projected savings: ${cost_optimization['projected_savings']:.2f}")
print(f"Recommendations: {len(cost_optimization['recommendations'])}")