Sustainable Computing Ecosystems

# Sustainable Computing Ecosystem Implementation
import asyncio
import numpy as np
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
from datetime import datetime, timedelta
from enum import Enum
import aiohttp
import json

class EnergySource(Enum):
    SOLAR = "solar"
    WIND = "wind"
    HYDRO = "hydro"
    NUCLEAR = "nuclear"
    NATURAL_GAS = "natural_gas"
    COAL = "coal"
    GRID_MIXED = "grid_mixed"

class WorkloadPriority(Enum):
    CRITICAL = "critical"
    HIGH = "high"
    NORMAL = "normal"
    LOW = "low"
    BATCH = "batch"

@dataclass
class EnergyMetrics:
    source: EnergySource
    carbon_intensity: float  # gCO2/kWh
    availability: float      # 0-1 scale
    cost_per_kwh: float     # USD per kWh
    forecast_hours: int     # Hours of reliable forecast

@dataclass
class ComputeResource:
    resource_id: str
    location: str
    cpu_cores: int
    memory_gb: int
    power_consumption_watts: int
    carbon_efficiency_score: float  # 0-100
    availability: bool
    current_utilization: float  # 0-1 scale

@dataclass
class SustainableWorkload:
    workload_id: str
    priority: WorkloadPriority
    estimated_duration_hours: float
    required_cores: int
    required_memory_gb: int
    max_carbon_budget: float  # gCO2 maximum acceptable
    deadline: Optional[datetime]
    location_flexibility: List[str]  # Acceptable regions

class CarbonAwareScheduler:
    def __init__(self, config: Dict):
        self.energy_apis = config.get('energy_apis', {})
        self.compute_resources: Dict[str, ComputeResource] = {}
        self.energy_forecasts: Dict[str, List[EnergyMetrics]] = {}
        self.workload_queue: List[SustainableWorkload] = []
        self.carbon_budget_daily = config.get('daily_carbon_budget_kg', 1000)
        self.carbon_used_today = 0.0
        
        # Sustainability targets
        self.renewable_energy_target = config.get('renewable_target_percent', 80)
        self.carbon_intensity_threshold = config.get('carbon_threshold_g_kwh', 200)
        
    async def register_compute_resource(self, resource: ComputeResource):
        """Register a compute resource in the sustainable ecosystem"""
        self.compute_resources[resource.resource_id] = resource
        
        # Initialize energy monitoring for resource location
        await self.initialize_energy_monitoring(resource.location)
    
    async def initialize_energy_monitoring(self, location: str):
        """Set up energy monitoring for a geographic location"""
        try:
            # Fetch current energy mix and carbon intensity
            energy_data = await self.fetch_energy_data(location)
            self.energy_forecasts[location] = energy_data
            
            print(f"Energy monitoring initialized for {location}")
            print(f"Current carbon intensity: {energy_data[0].carbon_intensity} gCO2/kWh")
            
        except Exception as e:
            print(f"Failed to initialize energy monitoring for {location}: {e}")
            # Use fallback data
            self.energy_forecasts[location] = [
                EnergyMetrics(
                    source=EnergySource.GRID_MIXED,
                    carbon_intensity=400,  # Conservative estimate
                    availability=1.0,
                    cost_per_kwh=0.12,
                    forecast_hours=24
                )
            ]
    
    async def fetch_energy_data(self, location: str) -> List[EnergyMetrics]:
        """Fetch real-time energy data and forecasts"""
        # Simulate API calls to energy data providers
        # In production, integrate with APIs like:
        # - WattTime API for carbon intensity
        # - Grid operators (CAISO, PJM, etc.)
        # - Weather APIs for renewable forecasts
        
        await asyncio.sleep(0.1)  # Simulate API call
        
        # Generate realistic energy mix data
        current_time = datetime.now().hour
        
        # Solar availability peaks during day
        solar_availability = max(0, np.sin((current_time - 6) * np.pi / 12)) if 6 <= current_time <= 18 else 0
        
        # Wind is more variable
        wind_availability = np.random.uniform(0.3, 0.8)
        
        # Create 24-hour forecast
        forecasts = []
        for hour in range(24):
            future_hour = (current_time + hour) % 24
            
            # Calculate renewable mix
            solar_factor = max(0, np.sin((future_hour - 6) * np.pi / 12)) if 6 <= future_hour <= 18 else 0
            wind_factor = np.random.uniform(0.2, 0.9)
            
            renewable_mix = (solar_factor * 0.4 + wind_factor * 0.3) * 0.7  # 70% renewable potential
            
            carbon_intensity = 50 + (1 - renewable_mix) * 350  # 50-400 gCO2/kWh range
            
            forecasts.append(EnergyMetrics(
                source=EnergySource.GRID_MIXED,
                carbon_intensity=carbon_intensity,
                availability=1.0,
                cost_per_kwh=0.08 + renewable_mix * 0.06,  # Renewables slightly more expensive
                forecast_hours=24 - hour
            ))
        
        return forecasts
    
    async def schedule_workload(self, workload: SustainableWorkload) -> Dict:
        """Schedule workload using carbon-aware algorithms"""
        
        # Find optimal time and location for workload
        best_schedule = await self.find_optimal_schedule(workload)
        
        if not best_schedule:
            return {
                'scheduled': False,
                'reason': 'No suitable schedule found within carbon budget',
                'suggested_actions': [
                    'Increase carbon budget',
                    'Reduce resource requirements',
                    'Add location flexibility',
                    'Defer deadline'
                ]
            }
        
        # Execute the schedule
        execution_result = await self.execute_workload_schedule(workload, best_schedule)
        
        return {
            'scheduled': True,
            'execution_plan': best_schedule,
            'estimated_carbon_impact': best_schedule['estimated_carbon_g'],
            'estimated_cost': best_schedule['estimated_cost_usd'],
            'execution_result': execution_result
        }
    
    async def find_optimal_schedule(
        self, 
        workload: SustainableWorkload
    ) -> Optional[Dict]:
        """Find the most sustainable schedule for a workload"""
        
        best_schedule = None
        min_carbon_impact = float('inf')
        
        # Evaluate all possible resource and time combinations
        for resource_id, resource in self.compute_resources.items():
            
            # Check if resource can handle workload
            if (resource.cpu_cores < workload.required_cores or
                resource.memory_gb < workload.required_memory_gb or
                not resource.availability):
                continue
            
            # Check location flexibility
            if (workload.location_flexibility and 
                resource.location not in workload.location_flexibility):
                continue
            
            # Evaluate different start times
            for start_hour in range(24):
                schedule = await self.evaluate_schedule_option(
                    workload, resource, start_hour
                )
                
                if (schedule and 
                    schedule['estimated_carbon_g'] < min_carbon_impact and
                    schedule['estimated_carbon_g'] <= workload.max_carbon_budget):
                    
                    min_carbon_impact = schedule['estimated_carbon_g']
                    best_schedule = schedule
        
        return best_schedule
    
    async def evaluate_schedule_option(
        self,
        workload: SustainableWorkload,
        resource: ComputeResource,
        start_hour: int
    ) -> Optional[Dict]:
        """Evaluate a specific schedule option"""
        
        location = resource.location
        energy_forecasts = self.energy_forecasts.get(location, [])
        
        if not energy_forecasts or start_hour >= len(energy_forecasts):
            return None
        
        # Calculate energy consumption
        power_watts = resource.power_consumption_watts * (workload.required_cores / resource.cpu_cores)
        energy_kwh = (power_watts / 1000) * workload.estimated_duration_hours
        
        # Get carbon intensity for execution period
        execution_hours = int(np.ceil(workload.estimated_duration_hours))
        total_carbon_g = 0
        total_cost = 0
        
        for hour in range(start_hour, min(start_hour + execution_hours, len(energy_forecasts))):
            forecast = energy_forecasts[hour]
            hour_fraction = min(1.0, workload.estimated_duration_hours - (hour - start_hour))
            
            hour_energy_kwh = energy_kwh * hour_fraction / workload.estimated_duration_hours
            hour_carbon_g = hour_energy_kwh * forecast.carbon_intensity
            hour_cost = hour_energy_kwh * forecast.cost_per_kwh
            
            total_carbon_g += hour_carbon_g
            total_cost += hour_cost
        
        # Check deadline constraint
        start_time = datetime.now() + timedelta(hours=start_hour)
        completion_time = start_time + timedelta(hours=workload.estimated_duration_hours)
        
        if workload.deadline and completion_time > workload.deadline:
            return None
        
        # Apply sustainability scoring
        sustainability_score = self.calculate_sustainability_score(
            total_carbon_g, resource.carbon_efficiency_score, location
        )
        
        return {
            'resource_id': resource.resource_id,
            'location': location,
            'start_time': start_time.isoformat(),
            'estimated_duration_hours': workload.estimated_duration_hours,
            'estimated_carbon_g': total_carbon_g,
            'estimated_cost_usd': total_cost,
            'sustainability_score': sustainability_score,
            'energy_breakdown': self.get_energy_breakdown(
                energy_forecasts[start_hour:start_hour + execution_hours]
            )
        }
    
    def calculate_sustainability_score(
        self,
        carbon_impact_g: float,
        resource_efficiency: float,
        location: str
    ) -> float:
        """Calculate overall sustainability score (0-100)"""
        
        # Carbon impact score (lower is better)
        carbon_score = max(0, 100 - (carbon_impact_g / 1000) * 10)
        
        # Resource efficiency score
        efficiency_score = resource_efficiency
        
        # Location renewable energy score
        location_forecasts = self.energy_forecasts.get(location, [])
        if location_forecasts:
            avg_carbon_intensity = np.mean([f.carbon_intensity for f in location_forecasts[:12]])
            location_score = max(0, 100 - (avg_carbon_intensity / 500) * 100)
        else:
            location_score = 50
        
        # Weighted combination
        overall_score = (
            carbon_score * 0.4 +
            efficiency_score * 0.3 +
            location_score * 0.3
        )
        
        return round(overall_score, 1)
    
    def get_energy_breakdown(self, forecasts: List[EnergyMetrics]) -> Dict:
        """Get energy source breakdown for execution period"""
        if not forecasts:
            return {}
        
        avg_carbon_intensity = np.mean([f.carbon_intensity for f in forecasts])
        
        # Estimate renewable percentage based on carbon intensity
        renewable_percent = max(0, min(100, (500 - avg_carbon_intensity) / 4))
        
        return {
            'average_carbon_intensity_g_kwh': round(avg_carbon_intensity, 1),
            'estimated_renewable_percent': round(renewable_percent, 1),
            'grid_stability_score': np.mean([f.availability for f in forecasts]) * 100
        }
    
    async def execute_workload_schedule(
        self,
        workload: SustainableWorkload,
        schedule: Dict
    ) -> Dict:
        """Execute a scheduled workload with sustainability monitoring"""
        
        start_time = datetime.now()
        resource_id = schedule['resource_id']
        
        try:
            # Update resource utilization
            resource = self.compute_resources[resource_id]
            resource.current_utilization = min(1.0, 
                resource.current_utilization + (workload.required_cores / resource.cpu_cores)
            )
            
            # Start carbon footprint monitoring
            carbon_monitor = CarbonFootprintMonitor(
                resource_id=resource_id,
                estimated_carbon_g=schedule['estimated_carbon_g']
            )
            
            # Simulate workload execution
            await self.simulate_workload_execution(workload, resource)
            
            # Calculate actual carbon impact
            actual_carbon_g = await carbon_monitor.get_actual_carbon_impact()
            
            # Update daily carbon usage
            self.carbon_used_today += actual_carbon_g / 1000  # Convert to kg
            
            # Release resource
            resource.current_utilization = max(0.0,
                resource.current_utilization - (workload.required_cores / resource.cpu_cores)
            )
            
            execution_time = (datetime.now() - start_time).total_seconds() / 3600  # hours
            
            return {
                'success': True,
                'actual_execution_time_hours': execution_time,
                'estimated_carbon_g': schedule['estimated_carbon_g'],
                'actual_carbon_g': actual_carbon_g,
                'carbon_efficiency': schedule['estimated_carbon_g'] / max(actual_carbon_g, 1),
                'daily_carbon_usage_percent': (self.carbon_used_today / self.carbon_budget_daily) * 100
            }
            
        except Exception as e:
            # Release resource on failure
            resource.current_utilization = max(0.0,
                resource.current_utilization - (workload.required_cores / resource.cpu_cores)
            )
            
            return {
                'success': False,
                'error': str(e),
                'execution_time_hours': (datetime.now() - start_time).total_seconds() / 3600
            }
    
    async def simulate_workload_execution(
        self,
        workload: SustainableWorkload,
        resource: ComputeResource
    ):
        """Simulate workload execution with energy-aware optimizations"""
        
        # Simulate different execution phases
        phases = [
            ('initialization', 0.1),
            ('main_processing', 0.7),
            ('cleanup', 0.2)
        ]
        
        for phase_name, duration_fraction in phases:
            phase_duration = workload.estimated_duration_hours * duration_fraction
            
            # Apply energy optimizations based on current grid carbon intensity
            current_location = resource.location
            current_forecast = self.energy_forecasts[current_location][0]  # Current hour
            
            if current_forecast.carbon_intensity > self.carbon_intensity_threshold:
                # High carbon intensity - optimize for efficiency
                await self.apply_high_carbon_optimizations(phase_duration)
            else:
                # Low carbon intensity - normal execution
                await self.apply_normal_execution(phase_duration)
            
            print(f"Completed {phase_name} phase ({duration_fraction*100:.1f}% of workload)")
    
    async def apply_high_carbon_optimizations(self, duration_hours: float):
        """Apply optimizations during high carbon intensity periods"""
        # Simulate CPU frequency scaling, memory compression, etc.
        optimization_delay = duration_hours * 3600 * 0.1  # 10% overhead for optimizations
        await asyncio.sleep(min(optimization_delay, 5))  # Cap simulation delay
        
    async def apply_normal_execution(self, duration_hours: float):
        """Normal execution during low carbon intensity periods"""
        # Simulate normal processing
        await asyncio.sleep(min(duration_hours * 3600 * 0.05, 2))  # 5% simulation time
    
    async def get_sustainability_report(self) -> Dict:
        """Generate comprehensive sustainability report"""
        
        total_resources = len(self.compute_resources)
        active_resources = sum(1 for r in self.compute_resources.values() if r.current_utilization > 0)
        
        avg_efficiency = np.mean([r.carbon_efficiency_score for r in self.compute_resources.values()])
        
        # Calculate renewable energy usage
        renewable_usage = 0
        total_locations = len(self.energy_forecasts)
        
        for location, forecasts in self.energy_forecasts.items():
            if forecasts:
                avg_carbon_intensity = np.mean([f.carbon_intensity for f in forecasts[:1]])
                location_renewable = max(0, (500 - avg_carbon_intensity) / 4)
                renewable_usage += location_renewable
        
        avg_renewable_percent = renewable_usage / max(total_locations, 1)
        
        return {
            'timestamp': datetime.now().isoformat(),
            'resource_utilization': {
                'total_resources': total_resources,
                'active_resources': active_resources,
                'average_utilization_percent': np.mean([r.current_utilization * 100 for r in self.compute_resources.values()]),
                'average_efficiency_score': round(avg_efficiency, 1)
            },
            'energy_metrics': {
                'average_renewable_percent': round(avg_renewable_percent, 1),
                'renewable_energy_target': self.renewable_energy_target,
                'target_achievement_percent': round((avg_renewable_percent / self.renewable_energy_target) * 100, 1)
            },
            'carbon_footprint': {
                'daily_carbon_budget_kg': self.carbon_budget_daily,
                'carbon_used_today_kg': round(self.carbon_used_today, 2),
                'budget_utilization_percent': round((self.carbon_used_today / self.carbon_budget_daily) * 100, 1),
                'estimated_monthly_carbon_kg': round(self.carbon_used_today * 30, 2)
            },
            'sustainability_score': self.calculate_overall_sustainability_score(
                avg_renewable_percent, avg_efficiency, self.carbon_used_today
            ),
            'recommendations': self.generate_sustainability_recommendations(
                avg_renewable_percent, avg_efficiency, self.carbon_used_today
            )
        }
    
    def calculate_overall_sustainability_score(
        self,
        renewable_percent: float,
        avg_efficiency: float,
        carbon_used_kg: float
    ) -> float:
        """Calculate overall ecosystem sustainability score"""
        
        renewable_score = renewable_percent
        efficiency_score = avg_efficiency
        carbon_score = max(0, 100 - (carbon_used_kg / self.carbon_budget_daily) * 50)
        
        overall_score = (
            renewable_score * 0.4 +
            efficiency_score * 0.3 +
            carbon_score * 0.3
        )
        
        return round(overall_score, 1)
    
    def generate_sustainability_recommendations(
        self,
        renewable_percent: float,
        avg_efficiency: float,
        carbon_used_kg: float
    ) -> List[str]:
        """Generate actionable sustainability recommendations"""
        
        recommendations = []
        
        if renewable_percent < self.renewable_energy_target:
            recommendations.append(
                f"Increase renewable energy usage from {renewable_percent:.1f}% to {self.renewable_energy_target}%"
            )
        
        if avg_efficiency < 75:
            recommendations.append(
                "Upgrade to more energy-efficient hardware or optimize resource utilization"
            )
        
        carbon_usage_percent = (carbon_used_kg / self.carbon_budget_daily) * 100
        if carbon_usage_percent > 80:
            recommendations.append(
                "Carbon budget utilization high - consider workload rescheduling or efficiency improvements"
            )
        
        if len(self.energy_forecasts) < 3:
            recommendations.append(
                "Consider geographic distribution to access more renewable energy sources"
            )
        
        return recommendations

class CarbonFootprintMonitor:
    """Monitor actual carbon footprint during workload execution"""
    
    def __init__(self, resource_id: str, estimated_carbon_g: float):
        self.resource_id = resource_id
        self.estimated_carbon_g = estimated_carbon_g
        self.start_time = datetime.now()
        self.actual_power_readings = []
    
    async def get_actual_carbon_impact(self) -> float:
        """Calculate actual carbon impact based on real power consumption"""
        
        # Simulate getting actual power readings from hardware monitors
        # In production, integrate with:
        # - Intel RAPL (Running Average Power Limit)
        # - NVIDIA GPU monitoring
        # - BMC (Baseboard Management Controller) data
        # - Smart PDUs (Power Distribution Units)
        
        execution_time_hours = (datetime.now() - self.start_time).total_seconds() / 3600
        
        # Simulate power variance (±10% from estimate)
        power_variance = np.random.uniform(0.9, 1.1)
        actual_carbon_g = self.estimated_carbon_g * power_variance
        
        return actual_carbon_g

# Renewable Energy Integration System
class RenewableEnergyIntegrator:
    """Integrate with renewable energy sources and grid systems"""
    
    def __init__(self, config: Dict):
        self.solar_capacity_kw = config.get('solar_capacity_kw', 1000)
        self.wind_capacity_kw = config.get('wind_capacity_kw', 500)
        self.battery_capacity_kwh = config.get('battery_capacity_kwh', 2000)
        self.current_battery_charge = config.get('initial_battery_charge', 0.8)
        
    async def get_renewable_availability(self) -> Dict:
        """Get current renewable energy availability"""
        
        current_hour = datetime.now().hour
        
        # Solar availability (realistic curve)
        if 6 <= current_hour <= 18:
            solar_factor = max(0, np.sin((current_hour - 6) * np.pi / 12))
            solar_available_kw = self.solar_capacity_kw * solar_factor * 0.85  # 85% efficiency
        else:
            solar_available_kw = 0
        
        # Wind availability (more variable)
        wind_factor = np.random.uniform(0.2, 0.9)  # Wind variability
        wind_available_kw = self.wind_capacity_kw * wind_factor * 0.9  # 90% efficiency
        
        # Battery availability
        battery_available_kwh = self.battery_capacity_kwh * self.current_battery_charge
        
        total_renewable_kw = solar_available_kw + wind_available_kw
        
        return {
            'solar_available_kw': round(solar_available_kw, 1),
            'wind_available_kw': round(wind_available_kw, 1),
            'battery_available_kwh': round(battery_available_kwh, 1),
            'total_renewable_kw': round(total_renewable_kw, 1),
            'grid_carbon_intensity_g_kwh': await self.get_grid_carbon_intensity(),
            'renewable_percentage': self.calculate_renewable_percentage(total_renewable_kw)
        }
    
    async def get_grid_carbon_intensity(self) -> float:
        """Get current grid carbon intensity"""
        # Simulate API call to grid operator
        base_intensity = 400  # gCO2/kWh baseline
        time_factor = np.sin((datetime.now().hour - 12) * np.pi / 12) * 100  # Daily variation
        return max(50, base_intensity + time_factor)
    
    def calculate_renewable_percentage(self, renewable_kw: float) -> float:
        """Calculate percentage of power from renewables"""
        total_capacity = self.solar_capacity_kw + self.wind_capacity_kw
        if total_capacity == 0:
            return 0
        return min(100, (renewable_kw / total_capacity) * 100)
    
    async def optimize_energy_mix(self, required_power_kw: float) -> Dict:
        """Optimize energy mix for required power demand"""
        
        renewable_status = await self.get_renewable_availability()
        renewable_available = renewable_status['total_renewable_kw']
        
        if renewable_available >= required_power_kw:
            # Fully renewable
            return {
                'renewable_kw': required_power_kw,
                'grid_kw': 0,
                'battery_discharge_kwh': 0,
                'carbon_intensity_g_kwh': 20,  # Near zero for renewables
                'cost_per_kwh': 0.05  # Lower cost for renewables
            }
        else:
            # Mixed energy sources
            grid_needed = required_power_kw - renewable_available
            
            # Use battery if available
            battery_available_kw = min(
                renewable_status['battery_available_kwh'],
                grid_needed * 0.5  # Use battery for half the shortfall
            )
            
            final_grid_kw = max(0, grid_needed - battery_available_kw)
            
            # Calculate weighted carbon intensity
            grid_carbon = await self.get_grid_carbon_intensity()
            total_power = required_power_kw
            
            weighted_carbon = (
                (renewable_available * 20 + 
                 battery_available_kw * 50 +  # Battery has some carbon footprint
                 final_grid_kw * grid_carbon) / total_power
            )
            
            return {
                'renewable_kw': renewable_available,
                'grid_kw': final_grid_kw,
                'battery_discharge_kwh': battery_available_kw,
                'carbon_intensity_g_kwh': round(weighted_carbon, 1),
                'cost_per_kwh': 0.08  # Blended cost
            }

// Sustainable Hardware Lifecycle Management System
interface HardwareAsset {
  id: string;
  type: 'server' | 'storage' | 'network' | 'cooling';
  manufacturer: string;
  model: string;
  purchaseDate: Date;
  warrantyEndDate: Date;
  carbonFootprintKg: number;
  powerConsumptionWatts: number;
  currentLocation: string;
  utilizationPercent: number;
  healthScore: number; // 0-100
  repairHistory: RepairRecord[];
  sustainabilityMetrics: SustainabilityMetrics;
}

interface RepairRecord {
  date: Date;
  description: string;
  costUSD: number;
  carbonImpactKg: number;
  partsReplaced: string[];
  extendedLifeMonths: number;
}

interface SustainabilityMetrics {
  energyEfficiencyRating: string; // A+ to F
  recyclabilityScore: number; // 0-100
  repairabilityIndex: number; // 0-10
  embodiedCarbonKg: number;
  totalLifetimeCarbonKg: number;
  circularityScore: number; // 0-100
}

class SustainableHardwareManager {
  private assets: Map<string, HardwareAsset> = new Map();
  private repairPartners: Map<string, RepairPartner> = new Map();
  private recyclingPartners: Map<string, RecyclingPartner> = new Map();
  private carbonTracker: CarbonFootprintTracker;
  private predictiveAnalytics: PredictiveMaintenanceEngine;

  constructor(config: HardwareManagerConfig) {
    this.carbonTracker = new CarbonFootprintTracker();
    this.predictiveAnalytics = new PredictiveMaintenanceEngine(config);
    this.initializePartners(config.partners);
  }

  async assessHardwareLifecycle(assetId: string): Promise<LifecycleAssessment> {
    const asset = this.assets.get(assetId);
    if (!asset) throw new Error(`Asset ${assetId} not found`);

    const currentAge = this.calculateAssetAge(asset.purchaseDate);
    const remainingLife = await this.predictiveAnalytics.estimateRemainingLife(asset);
    const repairHistory = this.analyzeRepairHistory(asset.repairHistory);
    
    // Calculate total cost of ownership including environmental costs
    const tco = await this.calculateSustainableTCO(asset);
    
    // Assess different end-of-life scenarios
    const scenarios = await this.evaluateEndOfLifeScenarios(asset);
    
    return {
      assetId,
      currentAge: currentAge.years,
      estimatedRemainingLife: remainingLife,
      totalCostOfOwnership: tco,
      sustainabilityScore: this.calculateSustainabilityScore(asset),
      repairRecommendations: repairHistory.recommendations,
      endOfLifeScenarios: scenarios,
      carbonFootprint: {
        embodied: asset.sustainabilityMetrics.embodiedCarbonKg,
        operational: await this.calculateOperationalCarbon(asset),
        endOfLife: await this.estimateEndOfLifeCarbon(asset)
      }
    };
  }

  async optimizeHardwareUtilization(): Promise<OptimizationPlan> {
    const utilizationAnalysis = await this.analyzeCurrentUtilization();
    const consolidationOpportunities = this.identifyConsolidationOpportunities();
    const rightSizingRecommendations = this.generateRightSizingRecommendations();
    
    return {
      currentEfficiency: utilizationAnalysis.averageUtilization,
      consolidationOpportunities: consolidationOpportunities.map(opp => ({
        sourceAssets: opp.assets,
        targetCapacity: opp.requiredCapacity,
        carbonSavingKg: opp.estimatedCarbonSaving,
        costSavingUSD: opp.estimatedCostSaving,
        riskAssessment: opp.risk
      })),
      rightSizingActions: rightSizingRecommendations,
      predictedImpact: {
        carbonReductionPercent: consolidationOpportunities.reduce((sum, opp) => 
          sum + opp.carbonReductionPercent, 0),
        costReductionPercent: consolidationOpportunities.reduce((sum, opp) => 
          sum + opp.costReductionPercent, 0),
        efficiencyImprovement: this.calculateEfficiencyImprovement(
          consolidationOpportunities, rightSizingRecommendations
        )
      }
    };
  }

  async planHardwareRefresh(
    criteria: RefreshCriteria
  ): Promise<RefreshPlan> {
    const assetsToRefresh = this.identifyRefreshCandidates(criteria);
    const sustainableOptions = await this.evaluateSustainableOptions(assetsToRefresh);
    
    // Prioritize based on sustainability impact
    const prioritizedPlan = this.prioritizeRefreshActions(sustainableOptions);
    
    return {
      refreshCandidates: assetsToRefresh.length,
      totalBudgetRequired: prioritizedPlan.reduce((sum, item) => sum + item.cost, 0),
      phasing: this.createRefreshPhasing(prioritizedPlan),
      sustainabilityImpact: {
        carbonReduction: prioritizedPlan.reduce((sum, item) => 
          sum + item.carbonImpact, 0),
        energyEfficiencyImprovement: this.calculateEnergyEfficiencyGain(prioritizedPlan),
        circularityImprovement: this.calculateCircularityGain(prioritizedPlan)
      },
      alternatives: {
        repairAndExtend: await this.evaluateRepairOptions(assetsToRefresh),
        leasing: await this.evaluateLeasingOptions(assetsToRefresh),
        refurbished: await this.evaluateRefurbishedOptions(assetsToRefresh)
      }
    };
  }

  async implementCircularEconomy(): Promise<CircularEconomyPlan> {
    const currentAssets = Array.from(this.assets.values());
    
    // Identify reuse opportunities within organization
    const internalReuse = this.identifyInternalReuseOpportunities(currentAssets);
    
    // Partner opportunities for component harvesting
    const componentHarvesting = await this.evaluateComponentHarvesting(currentAssets);
    
    // Refurbishment program opportunities  
    const refurbishmentProgram = await this.designRefurbishmentProgram(currentAssets);
    
    // Recycling and material recovery
    const materialRecovery = await this.optimizeMaterialRecovery(currentAssets);
    
    return {
      internalReuse: {
        opportunities: internalReuse,
        estimatedValue: this.calculateReuseValue(internalReuse),
        carbonAvoidance: this.calculateCarbonAvoidance(internalReuse)
      },
      componentHarvesting: {
        viableComponents: componentHarvesting.components,
        partnerships: componentHarvesting.partners,
        revenueOpportunity: componentHarvesting.revenue
      },
      refurbishmentProgram: {
        candidateAssets: refurbishmentProgram.assets,
        marketDemand: refurbishmentProgram.demand,
        businessCase: refurbishmentProgram.businessCase
      },
      materialRecovery: {
        recoverableMaterials: materialRecovery.materials,
        recyclingPartners: materialRecovery.partners,
        recoveryValue: materialRecovery.value
      }
    };
  }

  private calculateSustainableTCO(asset: HardwareAsset): Promise<SustainableTCO> {
    return new Promise(resolve => {
      const ageInYears = this.calculateAssetAge(asset.purchaseDate).years;
      
      // Direct costs
      const acquisitionCost = asset.carbonFootprintKg * 1000; // Simplified
      const operationalCost = this.calculateOperationalCosts(asset, ageInYears);
      const maintenanceCost = this.calculateMaintenanceCosts(asset);
      const disposalCost = this.calculateDisposalCosts(asset);
      
      // Environmental costs (carbon pricing)
      const carbonPrice = 50; // $50 per ton CO2
      const embodiedCarbonCost = asset.sustainabilityMetrics.embodiedCarbonKg * carbonPrice / 1000;
      const operationalCarbonCost = this.calculateOperationalCarbonCost(asset, carbonPrice);
      
      resolve({
        directCosts: {
          acquisition: acquisitionCost,
          operational: operationalCost,
          maintenance: maintenanceCost,
          disposal: disposalCost,
          total: acquisitionCost + operationalCost + maintenanceCost + disposalCost
        },
        environmentalCosts: {
          embodiedCarbon: embodiedCarbonCost,
          operationalCarbon: operationalCarbonCost,
          total: embodiedCarbonCost + operationalCarbonCost
        },
        totalTCO: acquisitionCost + operationalCost + maintenanceCost + 
                  disposalCost + embodiedCarbonCost + operationalCarbonCost
      });
    });
  }

  private async evaluateEndOfLifeScenarios(
    asset: HardwareAsset
  ): Promise<EndOfLifeScenario[]> {
    const scenarios: EndOfLifeScenario[] = [];
    
    // Scenario 1: Continue operation with repairs
    scenarios.push({
      name: 'Continue with Repairs',
      description: 'Extend life through preventive maintenance and repairs',
      additionalYears: 2,
      carbonImpact: await this.calculateRepairCarbonImpact(asset),
      costImpact: await this.calculateRepairCosts(asset),
      riskLevel: 'Medium',
      sustainability: this.assessRepairSustainability(asset)
    });
    
    // Scenario 2: Refurbish and redeploy
    scenarios.push({
      name: 'Refurbish and Redeploy',
      description: 'Major refurbishment for internal reuse',
      additionalYears: 3,
      carbonImpact: await this.calculateRefurbishmentCarbonImpact(asset),
      costImpact: await this.calculateRefurbishmentCosts(asset),
      riskLevel: 'Low',
      sustainability: this.assessRefurbishmentSustainability(asset)
    });
    
    // Scenario 3: Sell/donate for external reuse
    scenarios.push({
      name: 'External Reuse',
      description: 'Sell or donate to extend useful life elsewhere',
      additionalYears: 4,
      carbonImpact: -asset.sustainabilityMetrics.embodiedCarbonKg * 0.7, // Avoided manufacturing
      costImpact: await this.calculateResaleValue(asset),
      riskLevel: 'Low',
      sustainability: this.assessExternalReuseSustainability(asset)
    });
    
    // Scenario 4: Component harvesting
    scenarios.push({
      name: 'Component Harvesting',
      description: 'Harvest valuable components for spare parts',
      additionalYears: 0,
      carbonImpact: -asset.sustainabilityMetrics.embodiedCarbonKg * 0.3,
      costImpact: await this.calculateComponentValue(asset),
      riskLevel: 'Low',
      sustainability: this.assessHarvestingSustainability(asset)
    });
    
    // Scenario 5: Responsible recycling
    scenarios.push({
      name: 'Responsible Recycling',
      description: 'Material recovery through certified recycling',
      additionalYears: 0,
      carbonImpact: await this.calculateRecyclingCarbonImpact(asset),
      costImpact: await this.calculateRecyclingCosts(asset),
      riskLevel: 'Low',
      sustainability: this.assessRecyclingSustainability(asset)
    });
    
    return scenarios.sort((a, b) => b.sustainability.score - a.sustainability.score);
  }

  private calculateAssetAge(purchaseDate: Date): { years: number, months: number } {
    const now = new Date();
    const diffTime = now.getTime() - purchaseDate.getTime();
    const diffDays = diffTime / (1000 * 60 * 60 * 24);
    const years = Math.floor(diffDays / 365);
    const months = Math.floor((diffDays % 365) / 30);
    return { years, months };
  }

  private calculateSustainabilityScore(asset: HardwareAsset): SustainabilityScore {
    const metrics = asset.sustainabilityMetrics;
    
    // Energy efficiency (30%)
    const energyScore = this.convertEnergyRatingToScore(metrics.energyEfficiencyRating);
    
    // Longevity (25%)
    const age = this.calculateAssetAge(asset.purchaseDate).years;
    const longevityScore = Math.min(100, (age / 6) * 100); // 6 years = 100%
    
    // Repairability (20%)
    const repairabilityScore = metrics.repairabilityIndex * 10;
    
    // Recyclability (15%)
    const recyclabilityScore = metrics.recyclabilityScore;
    
    // Circularity (10%)
    const circularityScore = metrics.circularityScore;
    
    const overallScore = (
      energyScore * 0.3 +
      longevityScore * 0.25 +
      repairabilityScore * 0.2 +
      recyclabilityScore * 0.15 +
      circularityScore * 0.1
    );
    
    return {
      overall: Math.round(overallScore),
      breakdown: {
        energy: Math.round(energyScore),
        longevity: Math.round(longevityScore),
        repairability: Math.round(repairabilityScore),
        recyclability: Math.round(recyclabilityScore),
        circularity: Math.round(circularityScore)
      }
    };
  }

  private async generateSustainabilityReport(): Promise<SustainabilityReport> {
    const allAssets = Array.from(this.assets.values());
    
    // Fleet overview
    const fleetMetrics = this.calculateFleetMetrics(allAssets);
    
    // Carbon footprint analysis
    const carbonFootprint = await this.calculateFleetCarbonFootprint(allAssets);
    
    // Circular economy metrics
    const circularMetrics = this.calculateCircularEconomyMetrics(allAssets);
    
    // Improvement opportunities
    const opportunities = await this.identifyImprovementOpportunities(allAssets);
    
    return {
      reportDate: new Date(),
      fleetOverview: {
        totalAssets: allAssets.length,
        averageAge: fleetMetrics.averageAge,
        averageUtilization: fleetMetrics.averageUtilization,
        averageSustainabilityScore: fleetMetrics.averageSustainabilityScore
      },
      carbonFootprint: {
        totalEmbodiedCarbon: carbonFootprint.embodied,
        annualOperationalCarbon: carbonFootprint.operational,
        carbonIntensityPerTB: carbonFootprint.intensityPerTB,
        trendAnalysis: carbonFootprint.trends
      },
      circularEconomy: {
        reuseRate: circularMetrics.reuseRate,
        recyclingRate: circularMetrics.recyclingRate,
        repairRate: circularMetrics.repairRate,
        wasteReduction: circularMetrics.wasteReduction
      },
      improvementOpportunities: opportunities,
      recommendations: this.generateActionableRecommendations(
        fleetMetrics, carbonFootprint, circularMetrics, opportunities
      )
    };
  }
}

// Supporting Classes and Interfaces
class CarbonFootprintTracker {
  async trackAssetCarbon(asset: HardwareAsset): Promise<CarbonTrackingData> {
    // Track carbon footprint throughout asset lifecycle
    return {
      embodiedCarbon: asset.sustainabilityMetrics.embodiedCarbonKg,
      operationalCarbon: await this.calculateCurrentOperationalCarbon(asset),
      cumulativeCarbon: asset.sustainabilityMetrics.totalLifetimeCarbonKg,
      carbonIntensity: this.calculateCarbonIntensity(asset),
      trends: await this.analyzeCarbonTrends(asset.id)
    };
  }

  private async calculateCurrentOperationalCarbon(asset: HardwareAsset): Promise<number> {
    // Calculate operational carbon based on power consumption and grid mix
    const annualEnergyKwh = (asset.powerConsumptionWatts / 1000) * 24 * 365;
    const gridCarbonIntensity = 400; // gCO2/kWh average
    return (annualEnergyKwh * gridCarbonIntensity) / 1000; // Convert to kg
  }

  private calculateCarbonIntensity(asset: HardwareAsset): number {
    // Carbon intensity per unit of computing capacity
    const computeUnits = this.estimateComputeUnits(asset);
    return asset.sustainabilityMetrics.totalLifetimeCarbonKg / computeUnits;
  }

  private estimateComputeUnits(asset: HardwareAsset): number {
    // Simplified compute unit estimation
    return asset.utilizationPercent * 1000; // Placeholder calculation
  }
}

class PredictiveMaintenanceEngine {
  constructor(private config: any) {}

  async estimateRemainingLife(asset: HardwareAsset): Promise<number> {
    // Use machine learning to predict remaining useful life
    const baseLifeYears = this.getBaseLifeExpectancy(asset.type);
    const currentAge = new Date().getFullYear() - asset.purchaseDate.getFullYear();
    const healthFactor = asset.healthScore / 100;
    const utilizationFactor = 1 - (asset.utilizationPercent / 100) * 0.3;
    
    const adjustedLife = baseLifeYears * healthFactor * utilizationFactor;
    return Math.max(0, adjustedLife - currentAge);
  }

  private getBaseLifeExpectancy(type: string): number {
    const lifespans = {
      server: 6,
      storage: 8,
      network: 10,
      cooling: 15
    };
    return lifespans[type] || 5;
  }
}

// Type Definitions
interface LifecycleAssessment {
  assetId: string;
  currentAge: number;
  estimatedRemainingLife: number;
  totalCostOfOwnership: SustainableTCO;
  sustainabilityScore: SustainabilityScore;
  repairRecommendations: RepairRecommendation[];
  endOfLifeScenarios: EndOfLifeScenario[];
  carbonFootprint: {
    embodied: number;
    operational: number;
    endOfLife: number;
  };
}

interface SustainableTCO {
  directCosts: {
    acquisition: number;
    operational: number;
    maintenance: number;
    disposal: number;
    total: number;
  };
  environmentalCosts: {
    embodiedCarbon: number;
    operationalCarbon: number;
    total: number;
  };
  totalTCO: number;
}

interface SustainabilityScore {
  overall: number;
  breakdown: {
    energy: number;
    longevity: number;
    repairability: number;
    recyclability: number;
    circularity: number;
  };
}

interface EndOfLifeScenario {
  name: string;
  description: string;
  additionalYears: number;
  carbonImpact: number;
  costImpact: number;
  riskLevel: 'Low' | 'Medium' | 'High';
  sustainability: {
    score: number;
    rationale: string;
  };
}