Chapter 11.4: Capacity Planning

Capacity Planning is essential for ensuring systems can handle current and future load demands while optimizing costs and maintaining performance standards.

1. Capacity Planning Fundamentals

Capacity Planning Process

capacity_planning_process:
  data_collection:
    - current_usage_metrics
    - historical_performance_data
    - business_growth_projections
    - seasonal_patterns
    - traffic_forecasts
    
  analysis:
    - trend_analysis
    - peak_load_identification
    - bottleneck_detection
    - resource_utilization_patterns
    - cost_analysis
    
  modeling:
    - capacity_models
    - growth_projections
    - scenario_planning
    - what_if_analysis
    - load_testing_results
    
  planning:
    - resource_requirements
    - scaling_strategies
    - timeline_planning
    - budget_allocation
    - risk_assessment
    
  implementation:
    - resource_provisioning
    - infrastructure_scaling
    - monitoring_setup
    - validation_testing
    - documentation_updates

Capacity Metrics Framework

@Component
public class CapacityMetricsCollector {
    
    private final MetricsRegistry metricsRegistry;
    private final ResourceMonitoringService resourceMonitoringService;
    private final PerformanceMonitoringService performanceMonitoringService;
    
    public CapacityMetrics collectCurrentCapacityMetrics(String service) {
        return CapacityMetrics.builder()
            .service(service)
            .timestamp(Instant.now())
            .resourceUtilization(collectResourceUtilization(service))
            .performanceMetrics(collectPerformanceMetrics(service))
            .throughputMetrics(collectThroughputMetrics(service))
            .concurrencyMetrics(collectConcurrencyMetrics(service))
            .build();
    }
    
    private ResourceUtilization collectResourceUtilization(String service) {
        return ResourceUtilization.builder()
            .cpuUsage(resourceMonitoringService.getCpuUsage(service))
            .memoryUsage(resourceMonitoringService.getMemoryUsage(service))
            .diskUsage(resourceMonitoringService.getDiskUsage(service))
            .networkUtilization(resourceMonitoringService.getNetworkUtilization(service))
            .connectionPools(resourceMonitoringService.getConnectionPoolStats(service))
            .threadPools(resourceMonitoringService.getThreadPoolStats(service))
            .build();
    }
    
    private PerformanceMetrics collectPerformanceMetrics(String service) {
        return PerformanceMetrics.builder()
            .responseTime(performanceMonitoringService.getResponseTimeStats(service))
            .errorRate(performanceMonitoringService.getErrorRate(service))
            .availability(performanceMonitoringService.getAvailability(service))
            .queueDepth(performanceMonitoringService.getQueueDepth(service))
            .latencyPercentiles(performanceMonitoringService.getLatencyPercentiles(service))
            .build();
    }
    
    private ThroughputMetrics collectThroughputMetrics(String service) {
        return ThroughputMetrics.builder()
            .requestsPerSecond(getThroughputMetric(service, "requests_per_second"))
            .transactionsPerSecond(getThroughputMetric(service, "transactions_per_second"))
            .messagesPerSecond(getThroughputMetric(service, "messages_per_second"))
            .dataProcessedPerSecond(getThroughputMetric(service, "data_processed_per_second"))
            .build();
    }
    
    private ConcurrencyMetrics collectConcurrencyMetrics(String service) {
        return ConcurrencyMetrics.builder()
            .activeConnections(metricsRegistry.getGauge(service + "_active_connections").getValue())
            .concurrentUsers(metricsRegistry.getGauge(service + "_concurrent_users").getValue())
            .activeThreads(metricsRegistry.getGauge(service + "_active_threads").getValue())
            .queuedRequests(metricsRegistry.getGauge(service + "_queued_requests").getValue())
            .build();
    }
}

2. Historical Analysis and Trend Prediction

Historical Data Analysis Service

@Service
@Slf4j
public class HistoricalAnalysisService {
    
    private final MetricsRepository metricsRepository;
    private final TrendAnalysisEngine trendAnalysisEngine;
    private final SeasonalPatternDetector seasonalDetector;
    private final AnomalyDetectionService anomalyDetectionService;
    
    public HistoricalAnalysisReport analyzeHistoricalData(String service, Duration period) {
        LocalDateTime endTime = LocalDateTime.now();
        LocalDateTime startTime = endTime.minus(period);
        
        // Collect historical data
        List<CapacityMetrics> historicalMetrics = metricsRepository
            .findByServiceAndTimestampBetween(service, startTime, endTime);
        
        if (historicalMetrics.isEmpty()) {
            throw new InsufficientDataException("No historical data available for service: " + service);
        }
        
        // Analyze trends
        TrendAnalysisResult trendAnalysis = trendAnalysisEngine.analyzeTrends(historicalMetrics);
        
        // Detect seasonal patterns
        SeasonalPatterns seasonalPatterns = seasonalDetector.detectPatterns(historicalMetrics);
        
        // Identify anomalies
        List<CapacityAnomaly> anomalies = anomalyDetectionService
            .detectCapacityAnomalies(historicalMetrics);
        
        // Calculate growth rates
        GrowthRates growthRates = calculateGrowthRates(historicalMetrics);
        
        // Identify peak periods
        List<PeakPeriod> peakPeriods = identifyPeakPeriods(historicalMetrics);
        
        // Analyze resource utilization patterns
        ResourceUtilizationPatterns utilizationPatterns = 
            analyzeUtilizationPatterns(historicalMetrics);
        
        return HistoricalAnalysisReport.builder()
            .service(service)
            .analysisDate(LocalDateTime.now())
            .dataPeriod(period)
            .dataPoints(historicalMetrics.size())
            .trendAnalysis(trendAnalysis)
            .seasonalPatterns(seasonalPatterns)
            .anomalies(anomalies)
            .growthRates(growthRates)
            .peakPeriods(peakPeriods)
            .utilizationPatterns(utilizationPatterns)
            .build();
    }
    
    private GrowthRates calculateGrowthRates(List<CapacityMetrics> metrics) {
        if (metrics.size() < 2) {
            return GrowthRates.noData();
        }
        
        // Sort metrics by timestamp
        List<CapacityMetrics> sortedMetrics = metrics.stream()
            .sorted(Comparator.comparing(CapacityMetrics::getTimestamp))
            .collect(Collectors.toList());
        
        CapacityMetrics earliest = sortedMetrics.get(0);
        CapacityMetrics latest = sortedMetrics.get(sortedMetrics.size() - 1);
        
        Duration timeDiff = Duration.between(earliest.getTimestamp(), latest.getTimestamp());
        double timeFactor = timeDiff.toDays() / 365.0; // Annualized growth rate
        
        return GrowthRates.builder()
            .throughputGrowthRate(calculateGrowthRate(
                earliest.getThroughputMetrics().getRequestsPerSecond(),
                latest.getThroughputMetrics().getRequestsPerSecond(),
                timeFactor))
            .cpuUsageGrowthRate(calculateGrowthRate(
                earliest.getResourceUtilization().getCpuUsage().getAverage(),
                latest.getResourceUtilization().getCpuUsage().getAverage(),
                timeFactor))
            .memoryUsageGrowthRate(calculateGrowthRate(
                earliest.getResourceUtilization().getMemoryUsage().getAverage(),
                latest.getResourceUtilization().getMemoryUsage().getAverage(),
                timeFactor))
            .responseTimeGrowthRate(calculateGrowthRate(
                earliest.getPerformanceMetrics().getResponseTime().getAverage(),
                latest.getPerformanceMetrics().getResponseTime().getAverage(),
                timeFactor))
            .build();
    }
    
    private double calculateGrowthRate(double startValue, double endValue, double timeFactor) {
        if (startValue == 0) return 0;
        return (Math.pow(endValue / startValue, 1.0 / timeFactor) - 1) * 100;
    }
    
    private List<PeakPeriod> identifyPeakPeriods(List<CapacityMetrics> metrics) {
        List<PeakPeriod> peakPeriods = new ArrayList<>();
        
        // Calculate rolling average and identify periods significantly above average
        int windowSize = Math.min(24, metrics.size() / 10); // 24-hour windows or 10% of data
        double threshold = 1.5; // 50% above average
        
        for (int i = windowSize; i < metrics.size() - windowSize; i++) {
            double windowAverage = calculateWindowAverage(metrics, i - windowSize, i + windowSize);
            double currentValue = getCurrentLoadValue(metrics.get(i));
            
            if (currentValue > windowAverage * threshold) {
                PeakPeriod peak = identifyPeakPeriod(metrics, i);
                if (peak != null && !overlapsWithExistingPeak(peakPeriods, peak)) {
                    peakPeriods.add(peak);
                }
            }
        }
        
        return peakPeriods.stream()
            .sorted(Comparator.comparing(PeakPeriod::getStartTime))
            .collect(Collectors.toList());
    }
}

Machine Learning-based Forecasting

@Service
public class CapacityForecastingService {
    
    private final TimeSeriesForecaster timeSeriesForecaster;
    private final SeasonalForecastEngine seasonalForecaster;
    private final RegressionAnalysisService regressionService;
    private final BusinessGrowthProjectionService businessProjectionService;
    
    public CapacityForecast generateForecast(String service, Duration forecastPeriod) {
        // Collect historical data
        List<CapacityMetrics> historicalData = getHistoricalData(service, Duration.ofDays(365));
        
        if (historicalData.size() < 30) {
            throw new InsufficientDataException("Need at least 30 data points for forecasting");
        }
        
        // Generate time series forecasts
        TimeSeriesForecast throughputForecast = forecastThroughput(historicalData, forecastPeriod);
        TimeSeriesForecast resourceForecast = forecastResourceUsage(historicalData, forecastPeriod);
        
        // Apply seasonal adjustments
        SeasonalAdjustments seasonalAdjustments = seasonalForecaster
            .calculateSeasonalAdjustments(historicalData, forecastPeriod);
        
        // Incorporate business growth projections
        BusinessGrowthProjection businessGrowth = businessProjectionService
            .getGrowthProjection(service, forecastPeriod);
        
        // Generate scenario-based forecasts
        List<CapacityScenario> scenarios = generateCapacityScenarios(
            historicalData, throughputForecast, resourceForecast, 
            seasonalAdjustments, businessGrowth);
        
        // Calculate confidence intervals
        ConfidenceIntervals confidenceIntervals = calculateConfidenceIntervals(
            scenarios, forecastPeriod);
        
        return CapacityForecast.builder()
            .service(service)
            .forecastDate(LocalDateTime.now())
            .forecastPeriod(forecastPeriod)
            .baselineForecast(scenarios.get(0)) // Conservative scenario
            .scenarios(scenarios)
            .confidenceIntervals(confidenceIntervals)
            .methodology(ForecastMethodology.ML_HYBRID)
            .dataQuality(assessDataQuality(historicalData))
            .build();
    }
    
    private TimeSeriesForecast forecastThroughput(List<CapacityMetrics> historicalData, 
                                                 Duration forecastPeriod) {
        // Extract throughput time series
        List<TimeSeriesDataPoint> throughputSeries = extractThroughputSeries(historicalData);
        
        // Apply ARIMA or Prophet forecasting
        return timeSeriesForecaster.forecast(throughputSeries, forecastPeriod);
    }
    
    private TimeSeriesForecast forecastResourceUsage(List<CapacityMetrics> historicalData, 
                                                    Duration forecastPeriod) {
        // Extract resource usage time series
        List<TimeSeriesDataPoint> resourceSeries = extractResourceUsageSeries(historicalData);
        
        // Use ensemble forecasting (ARIMA + Prophet + Linear Regression)
        List<TimeSeriesForecast> individualForecasts = List.of(
            timeSeriesForecaster.forecastWithARIMA(resourceSeries, forecastPeriod),
            timeSeriesForecaster.forecastWithProphet(resourceSeries, forecastPeriod),
            regressionService.forecastWithLinearRegression(resourceSeries, forecastPeriod)
        );
        
        // Combine forecasts using weighted average
        return combineForecasts(individualForecasts);
    }
    
    private List<CapacityScenario> generateCapacityScenarios(
            List<CapacityMetrics> historicalData,
            TimeSeriesForecast throughputForecast,
            TimeSeriesForecast resourceForecast,
            SeasonalAdjustments seasonalAdjustments,
            BusinessGrowthProjection businessGrowth) {
        
        List<CapacityScenario> scenarios = new ArrayList<>();
        
        // Conservative scenario (10th percentile)
        scenarios.add(createScenario("Conservative", 0.1, 
            throughputForecast, resourceForecast, seasonalAdjustments, businessGrowth));
        
        // Expected scenario (50th percentile)
        scenarios.add(createScenario("Expected", 0.5, 
            throughputForecast, resourceForecast, seasonalAdjustments, businessGrowth));
        
        // Optimistic scenario (90th percentile)
        scenarios.add(createScenario("Optimistic", 0.9, 
            throughputForecast, resourceForecast, seasonalAdjustments, businessGrowth));
        
        // High growth scenario (Business driven)
        scenarios.add(createHighGrowthScenario(
            throughputForecast, resourceForecast, seasonalAdjustments, businessGrowth));
        
        return scenarios;
    }
    
    private CapacityScenario createScenario(String name, double percentile,
                                           TimeSeriesForecast throughputForecast,
                                           TimeSeriesForecast resourceForecast,
                                           SeasonalAdjustments seasonalAdjustments,
                                           BusinessGrowthProjection businessGrowth) {
        return CapacityScenario.builder()
            .name(name)
            .percentile(percentile)
            .throughputProjection(adjustThroughputProjection(throughputForecast, percentile, 
                seasonalAdjustments, businessGrowth))
            .resourceProjection(adjustResourceProjection(resourceForecast, percentile, 
                seasonalAdjustments))
            .peakLoadMultiplier(calculatePeakLoadMultiplier(percentile))
            .confidence(calculateScenarioConfidence(percentile))
            .build();
    }
}

3. Load Testing and Performance Validation

Load Testing Framework

@Service
@Slf4j
public class LoadTestingService {
    
    private final LoadTestExecutor loadTestExecutor;
    private final PerformanceAnalyzer performanceAnalyzer;
    private final CapacityModelValidator capacityValidator;
    private final TestResultsRepository testResultsRepository;
    
    public LoadTestResults executeCapacityValidationTest(CapacityTestPlan testPlan) {
        log.info("Starting capacity validation test: {}", testPlan.getName());
        
        // Pre-test system validation
        SystemState preTestState = captureSystemState(testPlan.getTargetService());
        
        try {
            // Execute load test phases
            List<LoadTestPhaseResult> phaseResults = new ArrayList<>();
            
            for (LoadTestPhase phase : testPlan.getPhases()) {
                LoadTestPhaseResult phaseResult = executeLoadTestPhase(phase);
                phaseResults.add(phaseResult);
                
                // Check if test should continue
                if (!shouldContinueTest(phaseResult, testPlan.getFailureCriteria())) {
                    break;
                }
            }
            
            // Post-test system validation
            SystemState postTestState = captureSystemState(testPlan.getTargetService());
            
            // Analyze results
            LoadTestAnalysis analysis = performanceAnalyzer.analyzeResults(
                phaseResults, preTestState, postTestState);
            
            // Validate capacity model
            CapacityModelValidation modelValidation = capacityValidator
                .validateModel(testPlan.getCapacityModel(), analysis);
            
            LoadTestResults results = LoadTestResults.builder()
                .testPlan(testPlan)
                .executionDate(LocalDateTime.now())
                .phaseResults(phaseResults)
                .preTestState(preTestState)
                .postTestState(postTestState)
                .analysis(analysis)
                .modelValidation(modelValidation)
                .success(determineTestSuccess(phaseResults, analysis))
                .build();
            
            // Store results
            testResultsRepository.save(results);
            
            return results;
            
        } catch (Exception e) {
            log.error("Load test execution failed: {}", e.getMessage(), e);
            throw new LoadTestExecutionException("Failed to execute load test", e);
        }
    }
    
    private LoadTestPhaseResult executeLoadTestPhase(LoadTestPhase phase) {
        log.info("Executing load test phase: {} - Target Load: {} users", 
            phase.getName(), phase.getTargetLoad());
        
        LoadTestPhaseResult.Builder resultBuilder = LoadTestPhaseResult.builder()
            .phaseName(phase.getName())
            .startTime(Instant.now())
            .targetLoad(phase.getTargetLoad())
            .duration(phase.getDuration());
        
        try {
            // Configure load generators
            loadTestExecutor.configureLoadGenerators(phase.getLoadConfiguration());
            
            // Start load generation
            LoadGenerationContext context = loadTestExecutor.startLoadGeneration(phase);
            
            // Monitor system during test
            List<SystemSnapshot> snapshots = monitorSystemDuringTest(phase);
            resultBuilder.systemSnapshots(snapshots);
            
            // Wait for phase completion
            LoadGenerationResults genResults = waitForPhaseCompletion(context, phase);
            resultBuilder.loadGenerationResults(genResults);
            
            // Analyze phase performance
            PhasePerformanceMetrics metrics = analyzePhasePerformance(snapshots, genResults);
            resultBuilder.performanceMetrics(metrics);
            
            return resultBuilder
                .endTime(Instant.now())
                .success(true)
                .build();
                
        } catch (Exception e) {
            log.error("Load test phase {} failed: {}", phase.getName(), e.getMessage());
            return resultBuilder
                .endTime(Instant.now())
                .success(false)
                .errorMessage(e.getMessage())
                .build();
        }
    }
    
    private List<SystemSnapshot> monitorSystemDuringTest(LoadTestPhase phase) {
        List<SystemSnapshot> snapshots = new ArrayList<>();
        long snapshotInterval = phase.getMonitoringInterval().toMillis();
        long phaseEndTime = System.currentTimeMillis() + phase.getDuration().toMillis();
        
        while (System.currentTimeMillis() < phaseEndTime) {
            try {
                SystemSnapshot snapshot = captureSystemSnapshot(phase.getTargetService());
                snapshots.add(snapshot);
                
                // Check for performance degradation
                if (detectPerformanceDegradation(snapshot, phase.getThresholds())) {
                    log.warn("Performance degradation detected during phase: {}", phase.getName());
                }
                
                Thread.sleep(snapshotInterval);
                
            } catch (InterruptedException e) {
                Thread.currentThread().interrupt();
                break;
            }
        }
        
        return snapshots;
    }
    
    public CapacityTestPlan generateOptimalTestPlan(String service, 
                                                   CapacityRequirements requirements) {
        // Analyze current capacity
        CapacityMetrics currentCapacity = getCurrentCapacity(service);
        
        // Calculate test phases based on requirements
        List<LoadTestPhase> phases = calculateTestPhases(currentCapacity, requirements);
        
        // Define performance thresholds
        PerformanceThresholds thresholds = definePerformanceThresholds(requirements);
        
        // Set failure criteria
        TestFailureCriteria failureCriteria = defineFailureCriteria(requirements);
        
        return CapacityTestPlan.builder()
            .name("Capacity Validation Test - " + service)
            .targetService(service)
            .phases(phases)
            .thresholds(thresholds)
            .failureCriteria(failureCriteria)
            .estimatedDuration(calculateTotalDuration(phases))
            .build();
    }
    
    private List<LoadTestPhase> calculateTestPhases(CapacityMetrics currentCapacity, 
                                                   CapacityRequirements requirements) {
        List<LoadTestPhase> phases = new ArrayList<>();
        
        // Baseline phase
        phases.add(LoadTestPhase.builder()
            .name("Baseline")
            .targetLoad(getCurrentLoad(currentCapacity))
            .duration(Duration.ofMinutes(10))
            .rampUpTime(Duration.ofMinutes(2))
            .build());
        
        // Incremental load phases
        double currentLoad = getCurrentLoad(currentCapacity);
        double targetLoad = requirements.getTargetLoad();
        double increment = (targetLoad - currentLoad) / 5; // 5 incremental phases
        
        for (int i = 1; i <= 5; i++) {
            double phaseLoad = currentLoad + (increment * i);
            phases.add(LoadTestPhase.builder()
                .name("Phase " + i)
                .targetLoad(phaseLoad)
                .duration(Duration.ofMinutes(15))
                .rampUpTime(Duration.ofMinutes(3))
                .build());
        }
        
        // Peak load phase
        phases.add(LoadTestPhase.builder()
            .name("Peak Load")
            .targetLoad(requirements.getPeakLoad())
            .duration(Duration.ofMinutes(20))
            .rampUpTime(Duration.ofMinutes(5))
            .build());
        
        // Stress test phase
        phases.add(LoadTestPhase.builder()
            .name("Stress Test")
            .targetLoad(requirements.getStressTestLoad())
            .duration(Duration.ofMinutes(10))
            .rampUpTime(Duration.ofMinutes(2))
            .build());
        
        return phases;
    }
}

Performance Benchmarking

@Service
public class PerformanceBenchmarkingService {
    
    private final BenchmarkRepository benchmarkRepository;
    private final PerformanceMetricsCollector metricsCollector;
    private final BenchmarkComparator benchmarkComparator;
    
    public BenchmarkResults runPerformanceBenchmark(BenchmarkConfiguration config) {
        log.info("Starting performance benchmark: {}", config.getName());
        
        // Setup benchmark environment
        BenchmarkEnvironment environment = setupBenchmarkEnvironment(config);
        
        try {
            // Run benchmark scenarios
            List<BenchmarkScenarioResult> scenarioResults = new ArrayList<>();
            
            for (BenchmarkScenario scenario : config.getScenarios()) {
                BenchmarkScenarioResult result = runBenchmarkScenario(scenario, environment);
                scenarioResults.add(result);
            }
            
            // Aggregate results
            BenchmarkSummary summary = aggregateBenchmarkResults(scenarioResults);
            
            // Compare with baselines
            BenchmarkComparison comparison = comparewithBaselines(summary, config);
            
            BenchmarkResults results = BenchmarkResults.builder()
                .configuration(config)
                .environment(environment)
                .scenarioResults(scenarioResults)
                .summary(summary)
                .comparison(comparison)
                .executionDate(LocalDateTime.now())
                .build();
            
            // Store results
            benchmarkRepository.save(results);
            
            return results;
            
        } finally {
            cleanupBenchmarkEnvironment(environment);
        }
    }
    
    private BenchmarkScenarioResult runBenchmarkScenario(BenchmarkScenario scenario, 
                                                        BenchmarkEnvironment environment) {
        log.info("Running benchmark scenario: {}", scenario.getName());
        
        // Pre-scenario measurements
        PerformanceBaseline preScenarioBaseline = capturePerformanceBaseline(environment);
        
        // Execute scenario
        ScenarioExecution execution = executeScenario(scenario, environment);
        
        // Post-scenario measurements  
        PerformanceBaseline postScenarioBaseline = capturePerformanceBaseline(environment);
        
        // Collect detailed metrics
        DetailedPerformanceMetrics detailedMetrics = 
            metricsCollector.collectDetailedMetrics(execution);
        
        return BenchmarkScenarioResult.builder()
            .scenario(scenario)
            .execution(execution)
            .preScenarioBaseline(preScenarioBaseline)
            .postScenarioBaseline(postScenarioBaseline)
            .detailedMetrics(detailedMetrics)
            .build();
    }
    
    private ScenarioExecution executeScenario(BenchmarkScenario scenario, 
                                             BenchmarkEnvironment environment) {
        ScenarioExecution.Builder executionBuilder = ScenarioExecution.builder()
            .scenario(scenario)
            .startTime(Instant.now());
        
        try {
            // Setup scenario-specific configuration
            configureScenarioEnvironment(scenario, environment);
            
            // Execute workload
            WorkloadExecutionResults workloadResults = executeWorkload(scenario.getWorkload());
            executionBuilder.workloadResults(workloadResults);
            
            // Monitor system behavior
            SystemBehaviorMetrics behaviorMetrics = monitorSystemBehavior(scenario);
            executionBuilder.behaviorMetrics(behaviorMetrics);
            
            return executionBuilder
                .endTime(Instant.now())
                .success(true)
                .build();
                
        } catch (Exception e) {
            log.error("Scenario execution failed: {}", e.getMessage());
            return executionBuilder
                .endTime(Instant.now())
                .success(false)
                .errorMessage(e.getMessage())
                .build();
        }
    }
    
    public BenchmarkTrends analyzeBenchmarkTrends(String service, Duration period) {
        List<BenchmarkResults> historicalBenchmarks = benchmarkRepository
            .findByServiceAndExecutionDateAfter(service, LocalDateTime.now().minus(period));
        
        if (historicalBenchmarks.size() < 2) {
            return BenchmarkTrends.insufficientData();
        }
        
        // Analyze throughput trends
        ThroughputTrend throughputTrend = analyzeThroughputTrend(historicalBenchmarks);
        
        // Analyze latency trends
        LatencyTrend latencyTrend = analyzeLatencyTrend(historicalBenchmarks);
        
        // Analyze resource utilization trends
        ResourceUtilizationTrend resourceTrend = analyzeResourceTrend(historicalBenchmarks);
        
        // Analyze scalability trends
        ScalabilityTrend scalabilityTrend = analyzeScalabilityTrend(historicalBenchmarks);
        
        return BenchmarkTrends.builder()
            .service(service)
            .period(period)
            .dataPoints(historicalBenchmarks.size())
            .throughputTrend(throughputTrend)
            .latencyTrend(latencyTrend)
            .resourceTrend(resourceTrend)
            .scalabilityTrend(scalabilityTrend)
            .trendDirection(determineTrendDirection(throughputTrend, latencyTrend))
            .build();
    }
}

4. Proactive Scaling and Resource Management

Auto-Scaling Strategy

@Service
@Slf4j
public class ProactiveScalingService {
    
    private final CapacityForecastingService forecastingService;
    private final ResourceProvisioningService provisioningService;
    private final MetricsCollector metricsCollector;
    private final ScalingPolicyRepository scalingPolicyRepository;
    private final CostOptimizationService costOptimizationService;
    
    @Scheduled(fixedDelay = 300000) // Check every 5 minutes
    public void executeProactiveScaling() {
        List<ScalingPolicy> activePolicies = scalingPolicyRepository.findByStatus(PolicyStatus.ACTIVE);
        
        for (ScalingPolicy policy : activePolicies) {
            try {
                evaluateScalingPolicy(policy);
            } catch (Exception e) {
                log.error("Error evaluating scaling policy {}: {}", policy.getName(), e.getMessage());
            }
        }
    }
    
    private void evaluateScalingPolicy(ScalingPolicy policy) {
        String service = policy.getTargetService();
        
        // Get current metrics
        CapacityMetrics currentMetrics = metricsCollector.collectCurrentCapacityMetrics(service);
        
        // Get forecast for the scaling horizon
        CapacityForecast forecast = forecastingService.generateForecast(
            service, policy.getScalingHorizon());
        
        // Evaluate scaling triggers
        ScalingDecision decision = evaluateScalingTriggers(policy, currentMetrics, forecast);
        
        if (decision.shouldScale()) {
            executeScalingAction(policy, decision);
        }
    }
    
    private ScalingDecision evaluateScalingTriggers(ScalingPolicy policy, 
                                                   CapacityMetrics currentMetrics, 
                                                   CapacityForecast forecast) {
        ScalingDecision.Builder decisionBuilder = ScalingDecision.builder()
            .policy(policy)
            .evaluationTime(Instant.now());
        
        // Check current utilization triggers
        if (isCurrentUtilizationTriggerMet(policy, currentMetrics)) {
            decisionBuilder.triggerType(ScalingTriggerType.CURRENT_UTILIZATION)
                .scaleDirection(determineScaleDirection(policy, currentMetrics))
                .confidence(0.9);
        }
        
        // Check predictive triggers
        else if (isPredictiveTriggerMet(policy, forecast)) {
            decisionBuilder.triggerType(ScalingTriggerType.PREDICTIVE)
                .scaleDirection(ScalingDirection.UP)
                .confidence(forecast.getConfidenceIntervals().getConfidence());
        }
        
        // Check scheduled triggers
        else if (isScheduledTriggerMet(policy)) {
            decisionBuilder.triggerType(ScalingTriggerType.SCHEDULED)
                .scaleDirection(ScalingDirection.UP)
                .confidence(1.0);
        }
        
        // Calculate optimal scale amount
        if (decisionBuilder.build().shouldScale()) {
            int scaleAmount = calculateOptimalScaleAmount(policy, currentMetrics, forecast);
            decisionBuilder.scaleAmount(scaleAmount);
        }
        
        return decisionBuilder.build();
    }
    
    private void executeScalingAction(ScalingPolicy policy, ScalingDecision decision) {
        String service = policy.getTargetService();
        
        log.info("Executing scaling action for service {}: {} by {} units", 
            service, decision.getScaleDirection(), decision.getScaleAmount());
        
        try {
            // Check scaling constraints
            if (!validateScalingConstraints(policy, decision)) {
                log.warn("Scaling action blocked by constraints for service: {}", service);
                return;
            }
            
            // Calculate cost impact
            CostImpact costImpact = costOptimizationService.calculateScalingCost(policy, decision);
            
            // Execute scaling if cost is acceptable
            if (costImpact.isAcceptable()) {
                ScalingResult result = provisioningService.executeScaling(policy, decision);
                
                // Record scaling action
                recordScalingAction(policy, decision, result, costImpact);
                
                // Monitor scaling results
                scheduleScalingValidation(policy, decision, result);
                
            } else {
                log.warn("Scaling action rejected due to cost impact for service: {}", service);
            }
            
        } catch (Exception e) {
            log.error("Failed to execute scaling action for service {}: {}", service, e.getMessage());
            handleScalingFailure(policy, decision, e);
        }
    }
    
    private int calculateOptimalScaleAmount(ScalingPolicy policy, 
                                          CapacityMetrics currentMetrics, 
                                          CapacityForecast forecast) {
        if (policy.getScalingStrategy() == ScalingStrategy.CONSERVATIVE) {
            return calculateConservativeScaling(policy, currentMetrics, forecast);
        } else if (policy.getScalingStrategy() == ScalingStrategy.AGGRESSIVE) {
            return calculateAggressiveScaling(policy, currentMetrics, forecast);
        } else {
            return calculateBalancedScaling(policy, currentMetrics, forecast);
        }
    }
    
    private int calculateBalancedScaling(ScalingPolicy policy, 
                                       CapacityMetrics currentMetrics, 
                                       CapacityForecast forecast) {
        // Calculate based on forecast peak load
        double forecastPeak = forecast.getScenarios().stream()
            .mapToDouble(scenario -> scenario.getThroughputProjection().getPeakValue())
            .max()
            .orElse(0.0);
        
        // Current capacity
        double currentCapacity = getCurrentThroughputCapacity(currentMetrics);
        
        // Target utilization
        double targetUtilization = policy.getTargetUtilization();
        
        // Required capacity with buffer
        double requiredCapacity = forecastPeak / targetUtilization * 1.1; // 10% buffer
        
        // Calculate scale amount
        if (requiredCapacity > currentCapacity) {
            double scaleFactor = requiredCapacity / currentCapacity;
            return (int) Math.ceil(scaleFactor) - 1; // Subtract current instance
        }
        
        return 0;
    }
    
    @Async
    public void scheduleScalingValidation(ScalingPolicy policy, 
                                         ScalingDecision decision, 
                                         ScalingResult result) {
        // Wait for scaling to complete
        try {
            Thread.sleep(policy.getScalingCompletionTime().toMillis());
            
            // Validate scaling results
            ScalingValidationResult validation = validateScalingResults(policy, decision, result);
            
            // Take corrective action if needed
            if (!validation.isSuccessful()) {
                handleScalingValidationFailure(policy, decision, result, validation);
            }
            
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
            log.warn("Scaling validation interrupted for service: {}", policy.getTargetService());
        }
    }
}

Cost-Aware Capacity Planning

@Service
public class CostAwareCapacityPlanningService {
    
    private final CostModelingService costModelingService;
    private final ResourcePricingService pricingService;
    private final BudgetConstraintService budgetService;
    private final CostOptimizationEngine optimizationEngine;
    
    public CostOptimizedCapacityPlan generateCostOptimizedPlan(
            CapacityRequirements requirements, 
            BudgetConstraints budgetConstraints) {
        
        // Generate multiple capacity scenarios
        List<CapacityScenario> scenarios = generateCapacityScenarios(requirements);
        
        // Calculate costs for each scenario
        List<CostOptimizedScenario> costScenarios = scenarios.stream()
            .map(scenario -> calculateScenarioCost(scenario, requirements))
            .collect(Collectors.toList());
        
        // Filter scenarios by budget constraints
        List<CostOptimizedScenario> feasibleScenarios = costScenarios.stream()
            .filter(scenario -> budgetService.isWithinBudget(scenario.getCost(), budgetConstraints))
            .collect(Collectors.toList());
        
        if (feasibleScenarios.isEmpty()) {
            throw new BudgetConstraintException("No feasible scenarios within budget constraints");
        }
        
        // Optimize scenarios using multi-objective optimization
        CostOptimizedScenario optimalScenario = optimizationEngine
            .findOptimalScenario(feasibleScenarios, requirements, budgetConstraints);
        
        // Generate implementation timeline
        ImplementationTimeline timeline = generateImplementationTimeline(optimalScenario);
        
        // Calculate ROI and TCO
        ROIAnalysis roiAnalysis = calculateROI(optimalScenario, requirements);
        TCOAnalysis tcoAnalysis = calculateTCO(optimalScenario, Duration.ofYears(3));
        
        return CostOptimizedCapacityPlan.builder()
            .requirements(requirements)
            .budgetConstraints(budgetConstraints)
            .optimalScenario(optimalScenario)
            .alternativeScenarios(feasibleScenarios)
            .implementationTimeline(timeline)
            .roiAnalysis(roiAnalysis)
            .tcoAnalysis(tcoAnalysis)
            .generatedDate(LocalDateTime.now())
            .build();
    }
    
    private CostOptimizedScenario calculateScenarioCost(CapacityScenario scenario, 
                                                       CapacityRequirements requirements) {
        // Calculate infrastructure costs
        InfrastructureCost infraCost = costModelingService
            .calculateInfrastructureCost(scenario.getResourceProjection());
        
        // Calculate operational costs
        OperationalCost operationalCost = costModelingService
            .calculateOperationalCost(scenario, requirements);
        
        // Calculate data transfer costs
        DataTransferCost transferCost = costModelingService
            .calculateDataTransferCost(scenario.getThroughputProjection());
        
        // Calculate storage costs
        StorageCost storageCost = costModelingService
            .calculateStorageCost(scenario.getDataStorageProjection());
        
        // Calculate total cost
        TotalCost totalCost = TotalCost.builder()
            .infrastructure(infraCost)
            .operational(operationalCost)
            .dataTransfer(transferCost)
            .storage(storageCost)
            .build();
        
        return CostOptimizedScenario.builder()
            .scenario(scenario)
            .cost(totalCost)
            .costPerformanceRatio(calculateCostPerformanceRatio(totalCost, scenario))
            .costEfficiencyScore(calculateCostEfficiencyScore(totalCost, scenario))
            .build();
    }
    
    public CostOptimizationRecommendations generateCostOptimizationRecommendations(
            String service, Duration period) {
        
        // Analyze current spending
        CostAnalysis currentCosts = analyzeCcurrentCosts(service, period);
        
        // Identify optimization opportunities
        List<CostOptimizationOpportunity> opportunities = 
            identifyOptimizationOpportunities(service, currentCosts);
        
        // Calculate potential savings
        PotentialSavings potentialSavings = calculatePotentialSavings(opportunities);
        
        // Prioritize recommendations
        List<PrioritizedRecommendation> prioritizedRecommendations = 
            prioritizeRecommendations(opportunities, potentialSavings);
        
        return CostOptimizationRecommendations.builder()
            .service(service)
            .analysisDate(LocalDateTime.now())
            .currentCosts(currentCosts)
            .opportunities(opportunities)
            .potentialSavings(potentialSavings)
            .recommendations(prioritizedRecommendations)
            .implementationRoadmap(generateImplementationRoadmap(prioritizedRecommendations))
            .build();
    }
    
    private List<CostOptimizationOpportunity> identifyOptimizationOpportunities(
            String service, CostAnalysis currentCosts) {
        
        List<CostOptimizationOpportunity> opportunities = new ArrayList<>();
        
        // Right-sizing opportunities
        opportunities.addAll(identifyRightSizingOpportunities(service, currentCosts));
        
        // Reserved instance opportunities
        opportunities.addAll(identifyReservedInstanceOpportunities(service, currentCosts));
        
        // Spot instance opportunities
        opportunities.addAll(identifySpotInstanceOpportunities(service, currentCosts));
        
        // Storage optimization opportunities
        opportunities.addAll(identifyStorageOptimizationOpportunities(service, currentCosts));
        
        // Auto-scaling optimization opportunities
        opportunities.addAll(identifyAutoScalingOptimizations(service, currentCosts));
        
        // Architecture optimization opportunities
        opportunities.addAll(identifyArchitectureOptimizations(service, currentCosts));
        
        return opportunities;
    }
}

Multi-Environment Capacity Coordination

# Multi-environment capacity coordination configuration
capacity_coordination:
  environments:
    production:
      priority: 1
      resource_allocation: 60%
      scaling_policy: aggressive
      cost_optimization: balanced
      
    staging:
      priority: 2
      resource_allocation: 25%
      scaling_policy: conservative
      cost_optimization: aggressive
      
    development:
      priority: 3
      resource_allocation: 15%
      scaling_policy: manual
      cost_optimization: aggressive
  
  coordination_rules:
    - if: production.cpu_usage > 80%
      then: reduce_staging_resources_by: 20%
      
    - if: production.memory_usage > 85%
      then: reduce_development_resources_by: 30%
      
    - if: budget_utilization > 90%
      then: freeze_non_production_scaling
  
  sharing_policies:
    burst_capacity:
      enabled: true
      max_burst_duration: 2h
      source_environments: [staging, development]
      target_environment: production
      
    resource_pooling:
      enabled: true
      shared_resources: [compute, storage]
      allocation_strategy: dynamic

This comprehensive capacity planning framework provides the foundation for ensuring your systems can handle current and future demands while optimizing costs and maintaining performance standards. The integration of machine learning forecasting, proactive scaling, and cost optimization ensures efficient resource utilization across all environments.