Capacity Planning

Capacity Planning, sistem kaynaklarının gelecekteki ihtiyaçları karşılayacak şekilde planlanması ve ölçeklendirilmesidir. Etkili kapasite planlaması, performans sorunlarını önler, maliyetleri optimize eder ve kullanıcı deneyimini garanti altına alır.

Hızlı Karar

Sinyal	Aksiyon	Dikkat
Kullanım trendi artıyor	Forecast + scale plan	Peak ve sezonluk etki ayrılmalı
Saturation yaklaşıyor	Capacity artır veya optimize et	Kök neden ölçülmeli
Kaynaklar boşta	Rightsizing	Ani burst ihtiyacı korunmalı
Yeni kampanya/lansman	Load test + pre-scale	Downstream kapasite dahil edilmeli

Üretim Kontrol Listesi

Problem: Hangi kaynak ne zaman tükenecek?
Çözüm: Forecast, headroom, autoscaling, load test ve rollback planı net mi?
Trade-off: Fazla kapasite maliyet; az kapasite kesinti üretir.
Hata durumu: Hot partition, autoscaling lag, quota limit ve dependency saturation ele alınmalı.
Ölçüm: Utilization, saturation, request rate, queue depth, forecast error ve unit cost izlenmeli.
Güvenlik/maliyet: Kapasite artırımı güvenlik limitlerini gevşetmemeli; rezervasyon/taahhüt kararları dikkat ister.

Capacity Planning Temelleri

1. Kapasite Modelleme

Historical Data Analysis

java

// Historical Capacity Analyzer
@Service
public class HistoricalCapacityAnalyzer {
    
    @Autowired
    private MetricsRepository metricsRepository;
    
    public CapacityTrendAnalysis analyzeHistoricalTrends(String serviceName, Duration period) {
        LocalDateTime endDate = LocalDateTime.now();
        LocalDateTime startDate = endDate.minus(period);
        
        List<ServiceMetrics> historicalMetrics = metricsRepository
            .findByServiceNameAndTimestampBetween(serviceName, startDate, endDate);
        
        CapacityTrendAnalysis analysis = new CapacityTrendAnalysis();
        
        // CPU utilization trends
        CPUTrend cpuTrend = analyzeCPUTrend(historicalMetrics);
        analysis.setCpuTrend(cpuTrend);
        
        // Memory utilization trends
        MemoryTrend memoryTrend = analyzeMemoryTrend(historicalMetrics);
        analysis.setMemoryTrend(memoryTrend);
        
        // Network I/O trends
        NetworkTrend networkTrend = analyzeNetworkTrend(historicalMetrics);
        analysis.setNetworkTrend(networkTrend);
        
        // Storage trends
        StorageTrend storageTrend = analyzeStorageTrend(historicalMetrics);
        analysis.setStorageTrend(storageTrend);
        
        // Traffic patterns
        TrafficPattern trafficPattern = analyzeTrafficPattern(historicalMetrics);
        analysis.setTrafficPattern(trafficPattern);
        
        return analysis;
    }
    
    private CPUTrend analyzeCPUTrend(List<ServiceMetrics> metrics) {
        List<Double> cpuValues = metrics.stream()
            .map(ServiceMetrics::getCpuUtilization)
            .collect(Collectors.toList());
        
        LinearRegressionResult regression = performLinearRegression(cpuValues);
        
        return CPUTrend.builder()
            .averageUtilization(cpuValues.stream().mapToDouble(Double::doubleValue).average().orElse(0.0))
            .peakUtilization(cpuValues.stream().mapToDouble(Double::doubleValue).max().orElse(0.0))
            .growthRate(regression.getSlope())
            .correlation(regression.getCorrelation())
            .projectedUtilization(projectFutureUtilization(cpuValues, Duration.ofDays(30)))
            .build();
    }
    
    private double projectFutureUtilization(List<Double> historicalValues, Duration projectionPeriod) {
        LinearRegressionResult regression = performLinearRegression(historicalValues);
        double daysToProject = projectionPeriod.toDays();
        double currentValue = historicalValues.get(historicalValues.size() - 1);
        
        return currentValue + (regression.getSlope() * daysToProject);
    }
}

Seasonal Pattern Detection

java

// Seasonal Pattern Analyzer
@Component
public class SeasonalPatternAnalyzer {
    
    public SeasonalAnalysis analyzeSeasonalPatterns(String serviceName, Duration analysisWindow) {
        List<ServiceMetrics> yearlyMetrics = getYearlyMetrics(serviceName);
        
        SeasonalAnalysis analysis = new SeasonalAnalysis();
        
        // Daily patterns
        DailyPattern dailyPattern = extractDailyPattern(yearlyMetrics);
        analysis.setDailyPattern(dailyPattern);
        
        // Weekly patterns
        WeeklyPattern weeklyPattern = extractWeeklyPattern(yearlyMetrics);
        analysis.setWeeklyPattern(weeklyPattern);
        
        // Monthly patterns
        MonthlyPattern monthlyPattern = extractMonthlyPattern(yearlyMetrics);
        analysis.setMonthlyPattern(monthlyPattern);
        
        // Holiday impacts
        HolidayImpactAnalysis holidayImpact = analyzeHolidayImpacts(yearlyMetrics);
        analysis.setHolidayImpact(holidayImpact);
        
        // Special events
        SpecialEventAnalysis specialEvents = analyzeSpecialEvents(yearlyMetrics);
        analysis.setSpecialEvents(specialEvents);
        
        return analysis;
    }
    
    private DailyPattern extractDailyPattern(List<ServiceMetrics> metrics) {
        Map<Integer, List<Double>> hourlyTraffic = metrics.stream()
            .collect(Collectors.groupingBy(
                metric -> metric.getTimestamp().getHour(),
                Collectors.mapping(ServiceMetrics::getRequestsPerSecond, Collectors.toList())
            ));
        
        Map<Integer, Double> avgTrafficByHour = hourlyTraffic.entrySet().stream()
            .collect(Collectors.toMap(
                Map.Entry::getKey,
                entry -> entry.getValue().stream().mapToDouble(Double::doubleValue).average().orElse(0.0)
            ));
        
        // Peak hours detection
        List<Integer> peakHours = avgTrafficByHour.entrySet().stream()
            .filter(entry -> entry.getValue() > getTrafficThreshold(avgTrafficByHour))
            .map(Map.Entry::getKey)
            .sorted()
            .collect(Collectors.toList());
        
        return DailyPattern.builder()
            .avgTrafficByHour(avgTrafficByHour)
            .peakHours(peakHours)
            .peakTrafficMultiplier(calculatePeakMultiplier(avgTrafficByHour))
            .lowTrafficHours(getLowTrafficHours(avgTrafficByHour))
            .build();
    }
}

2. Load Forecasting

Machine Learning Based Forecasting

java

// Load Forecasting Service
@Service
public class LoadForecastingService {
    
    @Autowired
    private TimeSeriesAnalyzer timeSeriesAnalyzer;
    
    @Autowired
    private MachineLearningPredictor mlPredictor;
    
    public LoadForecast generateLoadForecast(LoadForecastRequest request) {
        String serviceName = request.getServiceName();
        Duration forecastHorizon = request.getForecastHorizon();
        
        // Historical data preparation
        TimeSeriesData historicalData = prepareTimeSeriesData(serviceName, Duration.ofDays(90));
        
        // Multiple forecasting models
        List<ForecastModel> models = List.of(
            new ARIMAModel(),
            new LinearRegressionModel(),
            new RandomForestModel(),
            new LSTMNeuralNetworkModel()
        );
        
        List<ForecastResult> modelResults = new ArrayList<>();
        
        for (ForecastModel model : models) {
            ForecastResult result = model.forecast(historicalData, forecastHorizon);
            modelResults.add(result);
        }
        
        // Ensemble forecasting
        ForecastResult ensembleResult = combineForecasts(modelResults);
        
        // Confidence intervals
        ConfidenceInterval confidenceInterval = calculateConfidenceInterval(ensembleResult);
        
        return LoadForecast.builder()
            .serviceName(serviceName)
            .forecastHorizon(forecastHorizon)
            .predictedLoad(ensembleResult.getPredictedValues())
            .confidenceInterval(confidenceInterval)
            .modelAccuracy(ensembleResult.getAccuracy())
            .seasonalFactors(extractSeasonalFactors(historicalData))
            .trendFactors(extractTrendFactors(historicalData))
            .generatedAt(Instant.now())
            .build();
    }
    
    private ForecastResult combineForecasts(List<ForecastResult> modelResults) {
        // Weighted ensemble based on model accuracy
        Map<ForecastModel, Double> weights = calculateModelWeights(modelResults);
        
        List<Double> ensemblePredictions = new ArrayList<>();
        
        int forecastLength = modelResults.get(0).getPredictedValues().size();
        
        for (int i = 0; i < forecastLength; i++) {
            double weightedSum = 0.0;
            double totalWeight = 0.0;
            
            for (ForecastResult result : modelResults) {
                double weight = weights.get(result.getModel());
                weightedSum += result.getPredictedValues().get(i) * weight;
                totalWeight += weight;
            }
            
            ensemblePredictions.add(weightedSum / totalWeight);
        }
        
        return ForecastResult.builder()
            .predictedValues(ensemblePredictions)
            .accuracy(calculateEnsembleAccuracy(modelResults))
            .model(new EnsembleModel(modelResults))
            .build();
    }
}

Business Growth Integration

java

// Business Growth Factor Integration
@Component
public class BusinessGrowthIntegrator {
    
    public AdjustedLoadForecast integrateBusinessFactors(LoadForecast baseForecast, BusinessGrowthFactors factors) {
        List<Double> adjustedPredictions = new ArrayList<>();
        
        List<Double> basePredictions = baseForecast.getPredictedLoad();
        
        for (int i = 0; i < basePredictions.size(); i++) {
            double basePrediction = basePredictions.get(i);
            double timeOffsetDays = i; // i-th day in the future
            
            // Apply growth factors
            double growthMultiplier = calculateGrowthMultiplier(factors, timeOffsetDays);
            double adjustedPrediction = basePrediction * growthMultiplier;
            
            adjustedPredictions.add(adjustedPrediction);
        }
        
        return AdjustedLoadForecast.builder()
            .baseForecast(baseForecast)
            .adjustedPredictions(adjustedPredictions)
            .growthFactors(factors)
            .adjustmentRatio(calculateAverageAdjustmentRatio(basePredictions, adjustedPredictions))
            .build();
    }
    
    private double calculateGrowthMultiplier(BusinessGrowthFactors factors, double timeOffsetDays) {
        double baseGrowthRate = factors.getAnnualGrowthRate();
        
        // Product launch impact
        double productLaunchImpact = calculateProductLaunchImpact(factors, timeOffsetDays);
        
        // Marketing campaign impact
        double marketingImpact = calculateMarketingImpact(factors, timeOffsetDays);
        
        // Seasonal business factors
        double seasonalFactor = calculateSeasonalBusinessFactor(factors, timeOffsetDays);
        
        // Geographic expansion
        double expansionFactor = calculateExpansionFactor(factors, timeOffsetDays);
        
        double dailyGrowthRate = Math.pow(1 + baseGrowthRate, 1.0 / 365.0) - 1;
        double baseMultiplier = Math.pow(1 + dailyGrowthRate, timeOffsetDays);
        
        return baseMultiplier * productLaunchImpact * marketingImpact * seasonalFactor * expansionFactor;
    }
}

Capacity Planning Strategies

1. Proactive Scaling

java

// Proactive Scaling Service
@Service
public class ProactiveScalingService {
    
    @Autowired
    private LoadForecastingService forecastingService;
    
    @Autowired
    private KubernetesScalingService k8sScalingService;
    
    @Scheduled(fixedRate = 3600000) // Her saat
    public void evaluateProactiveScaling() {
        List<String> monitoredServices = getMonitoredServices();
        
        for (String serviceName : monitoredServices) {
            ProactiveScalingDecision decision = evaluateScalingNeed(serviceName);
            
            if (decision.isScalingNeeded()) {
                executeProactiveScaling(serviceName, decision);
            }
        }
    }
    
    private ProactiveScalingDecision evaluateScalingNeed(String serviceName) {
        // 4 saatlik forecast al
        LoadForecast forecast = forecastingService.generateLoadForecast(
            LoadForecastRequest.builder()
                .serviceName(serviceName)
                .forecastHorizon(Duration.ofHours(4))
                .build()
        );
        
        // Mevcut kapasite
        ServiceCapacity currentCapacity = getCurrentServiceCapacity(serviceName);
        
        // Capacity utilization projections
        List<Double> projectedUtilization = calculateProjectedUtilization(forecast, currentCapacity);
        
        // Scaling triggers
        ScalingTriggers triggers = getScalingTriggers(serviceName);
        
        ProactiveScalingDecision decision = new ProactiveScalingDecision();
        
        // Scale up scenarios
        if (willExceedCapacity(projectedUtilization, triggers.getScaleUpThreshold())) {
            int requiredReplicas = calculateRequiredReplicas(forecast, triggers);
            decision.setScalingNeeded(true);
            decision.setScalingDirection(ScalingDirection.UP);
            decision.setTargetReplicas(requiredReplicas);
            decision.setReason("Predicted capacity utilization will exceed threshold");
        }
        
        // Scale down scenarios
        else if (willBeUnderutilized(projectedUtilization, triggers.getScaleDownThreshold())) {
            int optimalReplicas = calculateOptimalReplicas(forecast, triggers);
            decision.setScalingNeeded(true);
            decision.setScalingDirection(ScalingDirection.DOWN);
            decision.setTargetReplicas(optimalReplicas);
            decision.setReason("Predicted capacity utilization allows for optimization");
        }
        
        return decision;
    }
    
    private void executeProactiveScaling(String serviceName, ProactiveScalingDecision decision) {
        ScalingAction action = ScalingAction.builder()
            .serviceName(serviceName)
            .currentReplicas(getCurrentReplicaCount(serviceName))
            .targetReplicas(decision.getTargetReplicas())
            .reason(decision.getReason())
            .executedAt(Instant.now())
            .type(ScalingType.PROACTIVE)
            .build();
        
        // Execute scaling
        k8sScalingService.scaleService(serviceName, decision.getTargetReplicas());
        
        // Log scaling action
        scalingActionRepository.save(action);
        
        // Send notification
        notificationService.sendScalingNotification(action);
        
        log.info("Proactive scaling executed for service {}: {} -> {} replicas ({})",
            serviceName, action.getCurrentReplicas(), action.getTargetReplicas(), decision.getReason());
    }
}

2. Cost-Aware Capacity Planning

java

// Cost-Aware Capacity Planner
@Component
public class CostAwareCapacityPlanner {
    
    public CapacityPlan createCostOptimizedPlan(CapacityPlanningRequest request) {
        String serviceName = request.getServiceName();
        Duration planningHorizon = request.getPlanningHorizon();
        CostConstraints costConstraints = request.getCostConstraints();
        
        // Load forecast
        LoadForecast loadForecast = forecastingService.generateLoadForecast(
            LoadForecastRequest.builder()
                .serviceName(serviceName)
                .forecastHorizon(planningHorizon)
                .build()
        );
        
        // Infrastructure options analysis
        List<InfrastructureOption> options = analyzeInfrastructureOptions(serviceName, loadForecast);
        
        // Cost modeling for each option
        List<CostModel> costModels = options.stream()
            .map(option -> createCostModel(option, loadForecast, planningHorizon))
            .collect(Collectors.toList());
        
        // Multi-objective optimization
        OptimizationResult optimization = optimizeCapacityPlan(costModels, costConstraints);
        
        return CapacityPlan.builder()
            .serviceName(serviceName)
            .planningHorizon(planningHorizon)
            .recommendedInfrastructure(optimization.getOptimalOption())
            .costProjection(optimization.getCostProjection())
            .performanceProjection(optimization.getPerformanceProjection())
            .riskAssessment(optimization.getRiskAssessment())
            .alternativeOptions(optimization.getAlternativeOptions())
            .createdAt(Instant.now())
            .build();
    }
    
    private List<InfrastructureOption> analyzeInfrastructureOptions(String serviceName, LoadForecast forecast) {
        List<InfrastructureOption> options = new ArrayList<>();
        
        // Current setup optimization
        options.add(optimizeCurrentSetup(serviceName, forecast));
        
        // Vertical scaling option
        options.add(createVerticalScalingOption(serviceName, forecast));
        
        // Horizontal scaling option
        options.add(createHorizontalScalingOption(serviceName, forecast));
        
        // Auto-scaling option
        options.add(createAutoScalingOption(serviceName, forecast));
        
        // Spot instance option
        options.add(createSpotInstanceOption(serviceName, forecast));
        
        // Reserved instance option
        options.add(createReservedInstanceOption(serviceName, forecast));
        
        // Serverless option
        if (isServerlessViable(serviceName, forecast)) {
            options.add(createServerlessOption(serviceName, forecast));
        }
        
        return options;
    }
    
    private CostModel createCostModel(InfrastructureOption option, LoadForecast forecast, Duration horizon) {
        CostModel model = new CostModel();
        
        // Compute costs
        ComputeCost computeCost = calculateComputeCost(option, forecast, horizon);
        model.setComputeCost(computeCost);
        
        // Storage costs
        StorageCost storageCost = calculateStorageCost(option, forecast, horizon);
        model.setStorageCost(storageCost);
        
        // Network costs
        NetworkCost networkCost = calculateNetworkCost(option, forecast, horizon);
        model.setNetworkCost(networkCost);
        
        // Operational costs
        OperationalCost operationalCost = calculateOperationalCost(option, horizon);
        model.setOperationalCost(operationalCost);
        
        // Total cost
        double totalCost = computeCost.getTotal() + storageCost.getTotal() + 
                          networkCost.getTotal() + operationalCost.getTotal();
        model.setTotalCost(totalCost);
        
        return model;
    }
}

3. Multi-Environment Capacity Planning

java

// Multi-Environment Capacity Coordinator
@Service
public class MultiEnvironmentCapacityCoordinator {
    
    public GlobalCapacityPlan createGlobalCapacityPlan(GlobalCapacityRequest request) {
        List<Environment> environments = request.getEnvironments();
        Duration planningHorizon = request.getPlanningHorizon();
        
        GlobalCapacityPlan globalPlan = new GlobalCapacityPlan();
        
        // Environment-specific plans
        Map<Environment, CapacityPlan> environmentPlans = new HashMap<>();
        
        for (Environment env : environments) {
            CapacityPlan envPlan = createEnvironmentCapacityPlan(env, planningHorizon);
            environmentPlans.put(env, envPlan);
        }
        
        globalPlan.setEnvironmentPlans(environmentPlans);
        
        // Cross-environment optimization
        CrossEnvironmentOptimization optimization = optimizeAcrossEnvironments(environmentPlans);
        globalPlan.setOptimization(optimization);
        
        // Resource sharing opportunities
        ResourceSharingAnalysis sharingAnalysis = analyzeResourceSharingOpportunities(environmentPlans);
        globalPlan.setResourceSharing(sharingAnalysis);
        
        // Disaster recovery planning
        DisasterRecoveryPlan drPlan = createDisasterRecoveryPlan(environmentPlans);
        globalPlan.setDisasterRecoveryPlan(drPlan);
        
        return globalPlan;
    }
    
    private CrossEnvironmentOptimization optimizeAcrossEnvironments(Map<Environment, CapacityPlan> plans) {
        CrossEnvironmentOptimization optimization = new CrossEnvironmentOptimization();
        
        // Workload migration opportunities
        List<WorkloadMigrationOpportunity> migrationOpportunities = 
            identifyWorkloadMigrationOpportunities(plans);
        optimization.setMigrationOpportunities(migrationOpportunities);
        
        // Shared resource pools
        List<SharedResourcePool> sharedPools = identifySharedResourcePools(plans);
        optimization.setSharedResourcePools(sharedPools);
        
        // Cost arbitrage opportunities
        List<CostArbitrageOpportunity> arbitrageOpportunities = 
            identifyCostArbitrageOpportunities(plans);
        optimization.setArbitrageOpportunities(arbitrageOpportunities);
        
        return optimization;
    }
    
    private List<WorkloadMigrationOpportunity> identifyWorkloadMigrationOpportunities(
            Map<Environment, CapacityPlan> plans) {
        
        List<WorkloadMigrationOpportunity> opportunities = new ArrayList<>();
        
        for (Map.Entry<Environment, CapacityPlan> sourceEntry : plans.entrySet()) {
            Environment sourceEnv = sourceEntry.getKey();
            CapacityPlan sourcePlan = sourceEntry.getValue();
            
            for (Map.Entry<Environment, CapacityPlan> targetEntry : plans.entrySet()) {
                Environment targetEnv = targetEntry.getKey();
                CapacityPlan targetPlan = targetEntry.getValue();
                
                if (sourceEnv.equals(targetEnv)) continue;
                
                // Analyze migration viability
                MigrationAnalysis analysis = analyzeMigrationViability(sourcePlan, targetPlan);
                
                if (analysis.isViable() && analysis.getCostSavings() > 0) {
                    WorkloadMigrationOpportunity opportunity = WorkloadMigrationOpportunity.builder()
                        .sourceEnvironment(sourceEnv)
                        .targetEnvironment(targetEnv)
                        .workloadServices(analysis.getMigratableServices())
                        .costSavings(analysis.getCostSavings())
                        .migrationCost(analysis.getMigrationCost())
                        .netBenefit(analysis.getCostSavings() - analysis.getMigrationCost())
                        .riskLevel(analysis.getRiskLevel())
                        .build();
                        
                    opportunities.add(opportunity);
                }
            }
        }
        
        return opportunities.stream()
            .sorted(Comparator.comparing(WorkloadMigrationOpportunity::getNetBenefit).reversed())
            .collect(Collectors.toList());
    }
}

Capacity Monitoring ve Alerting

1. Real-time Capacity Monitoring

java

// Real-time Capacity Monitor
@Component
public class RealTimeCapacityMonitor {
    
    private final MeterRegistry meterRegistry;
    
    @EventListener
    public void onCapacityMetricsUpdate(CapacityMetricsUpdateEvent event) {
        CapacityMetrics metrics = event.getMetrics();
        String serviceName = metrics.getServiceName();
        
        // Current utilization tracking
        Gauge.builder("capacity_utilization_percentage")
            .description("Current capacity utilization percentage")
            .tag("service", serviceName)
            .tag("resource", "cpu")
            .register(meterRegistry, metrics, m -> m.getCpuUtilization());
            
        Gauge.builder("capacity_utilization_percentage")
            .description("Current capacity utilization percentage")
            .tag("service", serviceName)
            .tag("resource", "memory")
            .register(meterRegistry, metrics, m -> m.getMemoryUtilization());
        
        // Available capacity tracking
        Gauge.builder("capacity_available_units")
            .description("Available capacity units")
            .tag("service", serviceName)
            .tag("resource", "cpu")
            .register(meterRegistry, metrics, m -> m.getAvailableCPU());
            
        // Projected time to capacity exhaustion
        double timeToExhaustion = calculateTimeToCapacityExhaustion(metrics);
        Gauge.builder("capacity_time_to_exhaustion_hours")
            .description("Projected time to capacity exhaustion in hours")
            .tag("service", serviceName)
            .register(meterRegistry, timeToExhaustion);
        
        // Capacity efficiency metrics
        double efficiency = calculateCapacityEfficiency(metrics);
        Gauge.builder("capacity_efficiency_ratio")
            .description("Capacity efficiency ratio")
            .tag("service", serviceName)
            .register(meterRegistry, efficiency);
    }
    
    @Scheduled(fixedRate = 300000) // Her 5 dakika
    public void evaluateCapacityAlerts() {
        List<String> services = getMonitoredServices();
        
        for (String serviceName : services) {
            CapacityMetrics currentMetrics = getCurrentCapacityMetrics(serviceName);
            CapacityThresholds thresholds = getCapacityThresholds(serviceName);
            
            evaluateAndSendAlerts(serviceName, currentMetrics, thresholds);
        }
    }
    
    private void evaluateAndSendAlerts(String serviceName, CapacityMetrics metrics, CapacityThresholds thresholds) {
        // High utilization alert
        if (metrics.getCpuUtilization() > thresholds.getCpuWarningThreshold()) {
            CapacityAlert alert = CapacityAlert.builder()
                .serviceName(serviceName)
                .alertType(CapacityAlertType.HIGH_CPU_UTILIZATION)
                .severity(metrics.getCpuUtilization() > thresholds.getCpuCriticalThreshold() ? 
                    AlertSeverity.CRITICAL : AlertSeverity.WARNING)
                .message(String.format("CPU utilization %.1f%% exceeds threshold %.1f%%", 
                    metrics.getCpuUtilization(), thresholds.getCpuWarningThreshold()))
                .currentValue(metrics.getCpuUtilization())
                .threshold(thresholds.getCpuWarningThreshold())
                .build();
                
            capacityAlertService.sendAlert(alert);
        }
        
        // Projected capacity exhaustion alert
        double timeToExhaustion = calculateTimeToCapacityExhaustion(metrics);
        if (timeToExhaustion < thresholds.getTimeToExhaustionWarningHours()) {
            CapacityAlert alert = CapacityAlert.builder()
                .serviceName(serviceName)
                .alertType(CapacityAlertType.PROJECTED_CAPACITY_EXHAUSTION)
                .severity(timeToExhaustion < thresholds.getTimeToExhaustionCriticalHours() ? 
                    AlertSeverity.CRITICAL : AlertSeverity.WARNING)
                .message(String.format("Projected capacity exhaustion in %.1f hours", timeToExhaustion))
                .currentValue(timeToExhaustion)
                .threshold(thresholds.getTimeToExhaustionWarningHours())
                .build();
                
            capacityAlertService.sendAlert(alert);
        }
    }
}

2. Capacity Planning Dashboard

java

// Capacity Planning Dashboard Service
@RestController
@RequestMapping("/api/capacity")
public class CapacityPlanningDashboardController {
    
    @GetMapping("/overview")
    public CapacityOverview getCapacityOverview() {
        CapacityOverview overview = new CapacityOverview();
        
        // Current capacity status
        Map<String, ServiceCapacityStatus> serviceStatuses = getAllServiceCapacityStatuses();
        overview.setServiceStatuses(serviceStatuses);
        
        // Global capacity metrics
        GlobalCapacityMetrics globalMetrics = calculateGlobalCapacityMetrics();
        overview.setGlobalMetrics(globalMetrics);
        
        // Capacity alerts
        List<CapacityAlert> activeAlerts = getActiveCapacityAlerts();
        overview.setActiveAlerts(activeAlerts);
        
        // Upcoming capacity events
        List<CapacityEvent> upcomingEvents = getUpcomingCapacityEvents();
        overview.setUpcomingEvents(upcomingEvents);
        
        return overview;
    }
    
    @GetMapping("/forecast/{serviceName}")
    public CapacityForecastResponse getServiceCapacityForecast(
            @PathVariable String serviceName,
            @RequestParam(defaultValue = "30") int forecastDays) {
        
        LoadForecast forecast = forecastingService.generateLoadForecast(
            LoadForecastRequest.builder()
                .serviceName(serviceName)
                .forecastHorizon(Duration.ofDays(forecastDays))
                .build()
        );
        
        CapacityRecommendations recommendations = 
            capacityRecommendationService.generateRecommendations(serviceName, forecast);
        
        return CapacityForecastResponse.builder()
            .serviceName(serviceName)
            .forecast(forecast)
            .recommendations(recommendations)
            .confidenceLevel(forecast.getConfidenceInterval().getLevel())
            .build();
    }
    
    @GetMapping("/cost-analysis")
    public CostAnalysisResponse getCostAnalysis(
            @RequestParam(required = false) List<String> services,
            @RequestParam(defaultValue = "90") int analysisDays) {
        
        if (services == null || services.isEmpty()) {
            services = getAllMonitoredServices();
        }
        
        CostAnalysisResponse response = new CostAnalysisResponse();
        
        for (String serviceName : services) {
            ServiceCostAnalysis costAnalysis = performServiceCostAnalysis(serviceName, analysisDays);
            response.addServiceAnalysis(serviceName, costAnalysis);
        }
        
        // Global cost optimization opportunities
        List<CostOptimizationOpportunity> opportunities = 
            identifyGlobalCostOptimizationOpportunities(services);
        response.setOptimizationOpportunities(opportunities);
        
        return response;
    }
}

Best Practices

1. Capacity Planning Process

java

// Capacity Planning Process Orchestrator
@Component
public class CapacityPlanningOrchestrator {
    
    @Scheduled(cron = "0 0 2 * * SUN") // Her Pazar 02:00'da
    public void weeklyCapacityPlanningReview() {
        log.info("Starting weekly capacity planning review");
        
        try {
            // 1. Collect and analyze historical data
            HistoricalAnalysisReport historicalReport = analyzeHistoricalData();
            
            // 2. Generate load forecasts
            Map<String, LoadForecast> serviceForecasts = generateServiceForecasts();
            
            // 3. Evaluate current capacity
            CapacityAssessmentReport currentCapacity = assessCurrentCapacity();
            
            // 4. Identify capacity gaps
            List<CapacityGap> capacityGaps = identifyCapacityGaps(serviceForecasts, currentCapacity);
            
            // 5. Generate recommendations
            List<CapacityRecommendation> recommendations = generateRecommendations(capacityGaps);
            
            // 6. Create capacity plan
            WeeklyCapacityPlan plan = createWeeklyCapacityPlan(recommendations);
            
            // 7. Send report to stakeholders
            sendCapacityPlanningReport(plan);
            
            // 8. Update capacity planning dashboard
            updateCapacityPlanningDashboard(plan);
            
        } catch (Exception e) {
            log.error("Weekly capacity planning review failed", e);
            alertService.sendCapacityPlanningAlert("Weekly capacity planning review failed: " + e.getMessage());
        }
    }
    
    private List<CapacityRecommendation> generateRecommendations(List<CapacityGap> gaps) {
        List<CapacityRecommendation> recommendations = new ArrayList<>();
        
        for (CapacityGap gap : gaps) {
            switch (gap.getType()) {
                case IMMEDIATE_SCALING_NEEDED:
                    recommendations.add(createImmediateScalingRecommendation(gap));
                    break;
                    
                case FUTURE_CAPACITY_SHORTAGE:
                    recommendations.add(createFutureCapacityRecommendation(gap));
                    break;
                    
                case RESOURCE_INEFFICIENCY:
                    recommendations.add(createEfficiencyRecommendation(gap));
                    break;
                    
                case COST_OPTIMIZATION_OPPORTUNITY:
                    recommendations.add(createCostOptimizationRecommendation(gap));
                    break;
            }
        }
        
        return recommendations.stream()
            .sorted(Comparator.comparing(CapacityRecommendation::getPriority).reversed())
            .collect(Collectors.toList());
    }
}

2. Capacity Planning Metrics

yaml

# Prometheus capacity planning queries
capacity_planning_queries:
  # Current utilization
  cpu_utilization: |
    avg by (service) (
      rate(cpu_usage_seconds_total[5m]) / on(instance) cpu_count
    ) * 100
    
  memory_utilization: |
    avg by (service) (
      memory_usage_bytes / memory_limit_bytes
    ) * 100
    
  # Growth rate
  weekly_growth_rate: |
    (
      avg_over_time(http_requests_total[7d]) - 
      avg_over_time(http_requests_total[7d] offset 7d)
    ) / avg_over_time(http_requests_total[7d] offset 7d) * 100
    
  # Capacity headroom
  cpu_headroom: |
    100 - avg by (service) (
      rate(cpu_usage_seconds_total[5m]) / on(instance) cpu_count
    ) * 100
    
  # Time to capacity exhaustion
  time_to_exhaustion: |
    (
      100 - avg by (service)(rate(cpu_usage_seconds_total[5m]) / on(instance) cpu_count) * 100
    ) / (
      deriv(avg by (service)(rate(cpu_usage_seconds_total[5m]) / on(instance) cpu_count) * 100[1d])
    ) / 24

3. Automation

java

// Automated Capacity Management
@Component
public class AutomatedCapacityManager {
    
    @EventListener
    public void onCapacityRecommendationGenerated(CapacityRecommendationEvent event) {
        CapacityRecommendation recommendation = event.getRecommendation();
        
        if (recommendation.isAutoApprovalEligible()) {
            executeAutomaticCapacityAction(recommendation);
        } else {
            requestManualApproval(recommendation);
        }
    }
    
    private void executeAutomaticCapacityAction(CapacityRecommendation recommendation) {
        switch (recommendation.getActionType()) {
            case SCALE_UP:
                executeAutomaticScaleUp(recommendation);
                break;
                
            case RESOURCE_OPTIMIZATION:
                executeAutomaticOptimization(recommendation);
                break;
                
            case COST_OPTIMIZATION:
                executeAutomaticCostOptimization(recommendation);
                break;
                
            default:
                log.warn("Unsupported automatic action type: {}", recommendation.getActionType());
        }
    }
    
    private boolean isAutoApprovalEligible(CapacityRecommendation recommendation) {
        // Safety checks
        if (recommendation.getRiskLevel() == RiskLevel.HIGH) {
            return false;
        }
        
        // Cost impact check
        if (recommendation.getCostImpact() > maxAutoApprovalCostImpact) {
            return false;
        }
        
        // Business hours check
        if (!isBusinessHours() && recommendation.getActionType().requiresBusinessHours()) {
            return false;
        }
        
        return true;
    }
}

Etkili capacity planning, sistem performansını garanti altına alırken maliyetleri optimize eden kritik bir SRE pratiğidir. Proaktif yaklaşım, veri odaklı kararlar ve otomasyonla sürdürülebilir büyüme sağlanabilir.

Capacity Planning ​

Hızlı Karar ​

Üretim Kontrol Listesi ​

Capacity Planning Temelleri ​

1. Kapasite Modelleme ​

Historical Data Analysis ​

Seasonal Pattern Detection ​

2. Load Forecasting ​

Machine Learning Based Forecasting ​

Business Growth Integration ​

Capacity Planning Strategies ​

1. Proactive Scaling ​

2. Cost-Aware Capacity Planning ​

3. Multi-Environment Capacity Planning ​

Capacity Monitoring ve Alerting ​

1. Real-time Capacity Monitoring ​

2. Capacity Planning Dashboard ​

Best Practices ​

1. Capacity Planning Process ​

2. Capacity Planning Metrics ​

3. Automation ​

Capacity Planning

Hızlı Karar

Üretim Kontrol Listesi

Capacity Planning Temelleri

1. Kapasite Modelleme

Historical Data Analysis

Seasonal Pattern Detection

2. Load Forecasting

Machine Learning Based Forecasting

Business Growth Integration

Capacity Planning Strategies

1. Proactive Scaling

2. Cost-Aware Capacity Planning

3. Multi-Environment Capacity Planning

Capacity Monitoring ve Alerting

1. Real-time Capacity Monitoring

2. Capacity Planning Dashboard

Best Practices

1. Capacity Planning Process

2. Capacity Planning Metrics

3. Automation