interface VDIUsagePredictor {
  predictDemand(timeWindow: TimeRange): ResourceDemand;
  optimizeAllocation(currentLoad: SystemLoad): AllocationPlan;
  detectAnomalies(metrics: PerformanceMetrics): Anomaly[];
}
 
class SmartVDIOrchestrator implements VDIUsagePredictor {
  private readonly mlModel: UsagePredictionModel;
  private readonly resourcePool: ResourcePool;
 
  async predictDemand(timeWindow: TimeRange): Promise<ResourceDemand> {
    const historicalData = await this.getHistoricalUsage(timeWindow);
    const externalFactors = await this.getExternalFactors(); // holidays, events, etc.
    
    return this.mlModel.predict({
      historical: historicalData,
      factors: externalFactors,
      seasonality: this.detectSeasonality(historicalData)
    });
  }
 
  async optimizeAllocation(currentLoad: SystemLoad): Promise<AllocationPlan> {
    const prediction = await this.predictDemand({ 
      start: new Date(), 
      duration: '4h' 
    });
    
    return {
      scaleUp: this.calculateScaleUp(prediction, currentLoad),
      scaleDown: this.identifyIdleInstances(currentLoad),
      redistribute: this.optimizeResourceDistribution(currentLoad),
      preWarm: this.calculatePreWarmTargets(prediction)
    };
  }
}

# Kubernetes-based VDI Auto-scaling Configuration
apiVersion: astro.ai/v1
kind: VDIOrchestrator
metadata:
  name: enterprise-vdi-orchestrator
spec:
  prediction:
    model: 'vdi-usage-forecaster'
    lookbackHours: 336  # 2 weeks
    forecastHours: 8
    updateInterval: 15m
 
  scaling:
    pools:
      - name: development-pool
        template: dev-desktop-template
        minInstances: 10
        maxInstances: 200
        scaleMetrics:
          - cpu: 70%
          - memory: 80%
          - queueLength: 5
        
      - name: design-pool
        template: gpu-desktop-template
        minInstances: 5
        maxInstances: 50
        resources:
          gpu: "nvidia-rtx-4090"
          cpu: "8 cores"
          memory: "32Gi"
 
  lifecycle:
    idleTimeout: 30m
    shutdownGracePeriod: 5m
    snapshotBeforeShutdown: true
    preWarmTargets:
      - time: "08:00"
        instances: 150
      - time: "13:00"  # lunch hour scale-down
        instances: 80

import logging
from dataclasses import dataclass
from typing import Dict, List
import asyncio
 
@dataclass
class VDIMetrics:
    instance_id: str
    cpu_usage: float
    memory_usage: float
    network_io: float
    user_session_active: bool
    last_activity: datetime
    application_usage: Dict[str, float]
 
class VDIMonitoringAgent:
    def __init__(self, vdi_provider: VDIProvider):
        self.provider = vdi_provider
        self.metrics_store = MetricsStore()
        
    async def collect_metrics(self) -> List[VDIMetrics]:
        """Collect comprehensive VDI metrics."""
        instances = await self.provider.list_instances()
        metrics = []
        
        for instance in instances:
            metric = VDIMetrics(
                instance_id=instance.id,
                cpu_usage=await self.get_cpu_usage(instance),
                memory_usage=await self.get_memory_usage(instance),
                network_io=await self.get_network_metrics(instance),
                user_session_active=await self.is_user_active(instance),
                last_activity=await self.get_last_activity(instance),
                application_usage=await self.get_app_metrics(instance)
            )
            metrics.append(metric)
            
        await self.metrics_store.store_batch(metrics)
        return metrics
    
    async def analyze_usage_patterns(self, days: int = 30) -> UsageAnalysis:
        """Analyze historical usage to identify patterns."""
        raw_data = await self.metrics_store.get_historical_data(days)
        
        return UsageAnalysis(
            peak_hours=self.identify_peak_hours(raw_data),
            idle_patterns=self.identify_idle_periods(raw_data),
            resource_utilization=self.analyze_resource_usage(raw_data),
            user_behavior=self.analyze_user_patterns(raw_data),
            cost_breakdown=self.calculate_cost_breakdown(raw_data)
        )

interface ProvisioningEngine {
  predictiveProvision(demand: ResourceDemand): Promise<ProvisionPlan>;
  executePlan(plan: ProvisionPlan): Promise<ExecutionResult>;
  rollbackIfNeeded(result: ExecutionResult): Promise<void>;
}
 
class AIProvisioningEngine implements ProvisioningEngine {
  async predictiveProvision(demand: ResourceDemand): Promise<ProvisionPlan> {
    const currentCapacity = await this.assessCurrentCapacity();
    const gap = this.calculateCapacityGap(demand, currentCapacity);
    
    if (gap.shortage > 0) {
      return this.createScaleUpPlan(gap);
    } else if (gap.excess > 0.3) { // 30% excess capacity
      return this.createScaleDownPlan(gap);
    }
    
    return { action: 'maintain', instances: [] };
  }
 
  private createScaleUpPlan(gap: CapacityGap): ProvisionPlan {
    return {
      action: 'scale_up',
      instances: [
        {
          template: this.selectOptimalTemplate(gap.requirements),
          count: gap.shortage,
          priority: this.calculatePriority(gap.urgency),
          placement: this.optimizePlacement(gap.regions)
        }
      ],
      timeline: {
        startTime: new Date(),
        estimatedCompletion: this.estimateProvisionTime(gap.shortage)
      },
      costImpact: this.calculateCostImpact(gap.shortage)
    };
  }
}

#!/bin/bash
# VDI Health Check and Auto-Remediation Script
 
check_vdi_health() {
    local instance_id=$1
    
    # Check system resources
    cpu_usage=$(kubectl exec $instance_id -- top -bn1 | grep "Cpu(s)" | awk '{print $2}' | cut -d'%' -f1)
    memory_usage=$(kubectl exec $instance_id -- free | grep Mem | awk '{printf "%.2f", $3/$2 * 100.0}')
    
    # Check user session
    session_active=$(kubectl exec $instance_id -- who -u | wc -l)
    
    # Check application responsiveness
    app_response=$(kubectl exec $instance_id -- curl -s -o /dev/null -w "%{http_code}" http://localhost:8080/health)
    
    if (( $(echo "$cpu_usage > 95" | bc -l) )) && [ $session_active -eq 0 ]; then
        remediate_high_cpu $instance_id
    fi
    
    if [ "$app_response" != "200" ]; then
        remediate_application $instance_id
    fi
}
 
remediate_high_cpu() {
    local instance_id=$1
    echo "Detected high CPU with no active session on $instance_id"
    
    # Attempt graceful remediation
    kubectl exec $instance_id -- systemctl restart problem-service
    sleep 30
    
    # If still problematic, restart the instance
    if ! check_cpu_normal $instance_id; then
        kubectl delete pod $instance_id --grace-period=60
        log_incident "VDI_AUTO_RESTART" $instance_id "High CPU usage remediation"
    fi
}

// Performance metrics after automation
const automationResults = {
  provisioning: {
    timeReduction: '93%',
    errorRate: '0.2%',
    userSatisfaction: 4.3
  },
  resourceOptimization: {
    utilizationImprovement: '123%',
    costSavings: '$900K/year',
    energyReduction: '31%'
  },
  operations: {
    incidentReduction: '87%',
    mttr: '12 minutes',
    automatedResolution: '94%'
  }
};

class VDIMetricsCollector:
    def collect_comprehensive_metrics(self):
        return {
            'infrastructure': self.collect_infrastructure_metrics(),
            'user_behavior': self.collect_user_metrics(),
            'application_performance': self.collect_app_metrics(),
            'cost_attribution': self.collect_cost_metrics(),
            'security_compliance': self.collect_security_metrics()
        }

safety_mechanisms:
  max_scale_rate: "20% per hour"
  rollback_triggers:
    - user_complaints > 5
    - error_rate > 1%
    - cost_spike > 20%
  human_approval_required:
    - production_changes
    - cost_impact > $1000
    - new_template_deployments

class UserExperienceOptimizer {
  async optimizeForUser(userId: string): Promise<VDIConfiguration> {
    const userProfile = await this.getUserProfile(userId);
    const workloadPatterns = await this.analyzeWorkloadPatterns(userId);
    
    return {
      resources: this.calculateOptimalResources(userProfile, workloadPatterns),
      applications: this.preinstallRequiredApps(userProfile),
      placement: this.selectOptimalDatacenter(userProfile.location),
      storage: this.configurePersonalizedStorage(userProfile)
    };
  }
}

#!/bin/bash
# Automated security patching with zero-downtime
 
perform_security_updates() {
    local template_id=$1
    
    # Create updated template
    new_template=$(create_patched_template $template_id)
    
    # Gradually migrate instances
    instances=$(get_instances_using_template $template_id)
    
    for instance in $instances; do
        if [ $(get_active_sessions $instance) -eq 0 ]; then
            # Safe to migrate
            migrate_instance $instance $new_template
        else
            # Schedule for maintenance window
            schedule_maintenance $instance $new_template
        fi
    done
}

class ComplianceOrchestrator:
    def ensure_compliance(self, instance_id: str) -> ComplianceReport:
        checks = [
            self.verify_encryption_at_rest(instance_id),
            self.verify_network_segmentation(instance_id),
            self.verify_access_controls(instance_id),
            self.verify_audit_logging(instance_id),
            self.verify_data_residency(instance_id)
        ]
        
        report = ComplianceReport(
            instance_id=instance_id,
            checks=checks,
            compliant=all(check.passed for check in checks),
            remediation_actions=self.generate_remediation_actions(checks)
        )
        
        if not report.compliant and self.auto_remediation_enabled:
            self.execute_remediation_actions(report.remediation_actions)
            
        return report

interface CostOptimizer {
  analyzeResourceWaste(): Promise<WasteAnalysis>;
  recommendRightsizing(instances: VDIInstance[]): Promise<RightsizingPlan>;
  implementCostControls(): Promise<void>;
}
 
class SmartCostOptimizer implements CostOptimizer {
  async analyzeResourceWaste(): Promise<WasteAnalysis> {
    const instances = await this.getAllInstances();
    const utilization = await this.getUtilizationData(instances, 30); // 30 days
    
    return {
      overProvisioned: instances.filter(i => 
        utilization[i.id].avgCpu < 20 && utilization[i.id].avgMemory < 30
      ),
      underUtilized: instances.filter(i => 
        utilization[i.id].idleHours > 16 // idle more than 16h/day
      ),
      potentialSavings: this.calculatePotentialSavings(instances, utilization)
    };
  }
}

interface NextGenVDI {
  enableGPUSharing(): Promise<void>;
  implementEdgeComputing(): Promise<void>;
  enableServerlessModel(): Promise<void>;
  personalizeUserExperience(): Promise<void>;
}