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.
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])
) / 243. 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.