你怎么做容量评估？

1. 概述

1.1 容量评估的重要性

容量评估是系统设计和运维中的关键环节，通过科学的方法评估系统资源需求，确保系统能够满足业务增长和性能要求，避免资源浪费和性能瓶颈。

本文内容：

  • 评估方法：容量评估的方法和流程
  • 资源评估：CPU、内存、存储、网络等资源评估
  • 性能评估：系统性能指标和瓶颈分析
  • 容量规划：基于评估结果进行容量规划
  • 容量监控：持续监控和调整容量
  • 实战案例：容量评估实践案例

1.2 本文内容结构

本文将从以下几个方面深入探讨容量评估：

  1. 评估方法：容量评估的方法和流程
  2. 资源评估：各类资源的评估方法
  3. 性能评估：系统性能评估和瓶颈分析
  4. 容量规划：基于评估的容量规划
  5. 容量监控：持续监控和优化
  6. 实战案例：容量评估实践案例

2. 评估方法

2.1 评估流程

2.1.1 容量评估流程

容量评估流程：

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// 容量评估流程
public class CapacityAssessmentProcess {
    
    // 评估步骤
    public enum AssessmentStep {
        REQUIREMENTS_ANALYSIS,  // 需求分析
        CURRENT_STATE_ANALYSIS, // 现状分析
        WORKLOAD_ANALYSIS,      // 负载分析
        RESOURCE_EVALUATION,    // 资源评估
        PERFORMANCE_EVALUATION, // 性能评估
        CAPACITY_PLANNING,      // 容量规划
        VALIDATION              // 验证
    }
    
    public CapacityAssessmentResult executeAssessment(BusinessRequirements requirements) {
        CapacityAssessmentResult result = new CapacityAssessmentResult();
        
        // 1. 需求分析
        result.setRequirements(analyzeRequirements(requirements));
        
        // 2. 现状分析
        result.setCurrentState(analyzeCurrentState());
        
        // 3. 负载分析
        result.setWorkload(analyzeWorkload(requirements));
        
        // 4. 资源评估
        result.setResources(evaluateResources(result.getWorkload()));
        
        // 5. 性能评估
        result.setPerformance(evaluatePerformance(result.getResources()));
        
        // 6. 容量规划
        result.setPlan(planCapacity(result));
        
        // 7. 验证
        validateAssessment(result);
        
        return result;
    }
    
    private RequirementsAnalysis analyzeRequirements(BusinessRequirements requirements) {
        RequirementsAnalysis analysis = new RequirementsAnalysis();
        
        // 业务需求
        analysis.setExpectedUsers(requirements.getExpectedUsers());
        analysis.setExpectedQPS(requirements.getExpectedQPS());
        analysis.setExpectedDataVolume(requirements.getExpectedDataVolume());
        analysis.setGrowthRate(requirements.getGrowthRate());
        analysis.setTimeHorizon(requirements.getTimeHorizon());
        
        return analysis;
    }
    
    private CurrentStateAnalysis analyzeCurrentState() {
        CurrentStateAnalysis analysis = new CurrentStateAnalysis();
        
        // 当前资源使用情况
        analysis.setCurrentCPUUsage(monitoringService.getCurrentCPUUsage());
        analysis.setCurrentMemoryUsage(monitoringService.getCurrentMemoryUsage());
        analysis.setCurrentStorageUsage(monitoringService.getCurrentStorageUsage());
        analysis.setCurrentNetworkUsage(monitoringService.getCurrentNetworkUsage());
        
        // 当前性能指标
        analysis.setCurrentQPS(monitoringService.getCurrentQPS());
        analysis.setCurrentResponseTime(monitoringService.getCurrentResponseTime());
        analysis.setCurrentErrorRate(monitoringService.getCurrentErrorRate());
        
        return analysis;
    }
    
    private WorkloadAnalysis analyzeWorkload(BusinessRequirements requirements) {
        WorkloadAnalysis analysis = new WorkloadAnalysis();
        
        // 计算预期负载
        int expectedUsers = requirements.getExpectedUsers();
        double qpsPerUser = calculateQPSPerUser();
        double expectedQPS = expectedUsers * qpsPerUser;
        
        analysis.setExpectedQPS(expectedQPS);
        analysis.setPeakQPS(expectedQPS * 2); // 峰值是平均值的2倍
        analysis.setDataVolume(calculateDataVolume(requirements));
        
        return analysis;
    }
    
    private double calculateQPSPerUser() {
        // 根据历史数据计算每个用户的QPS
        long totalRequests = monitoringService.getTotalRequests();
        int totalUsers = monitoringService.getTotalUsers();
        long timeWindow = monitoringService.getTimeWindow();
        
        return (double) totalRequests / totalUsers / timeWindow;
    }
}

2.2 评估方法

2.2.1 评估方法选择

容量评估方法：

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// 容量评估方法
public class CapacityAssessmentMethods {
    
    // 评估方法类型
    public enum AssessmentMethod {
        HISTORICAL_ANALYSIS,    // 历史数据分析
        BENCHMARK_TESTING,     // 基准测试
        STRESS_TESTING,        // 压力测试
        MODELING,              // 建模分析
        SIMULATION             // 仿真分析
    }
    
    // 历史数据分析
    public class HistoricalAnalysis {
        public CapacityEstimate estimateByHistory(int timeHorizon) {
            // 基于历史数据预测未来容量需求
            HistoricalData data = dataService.getHistoricalData(timeHorizon);
            
            // 分析趋势
            TrendAnalysis trend = analyzeTrend(data);
            
            // 预测未来需求
            CapacityEstimate estimate = new CapacityEstimate();
            estimate.setCPU(projectCPU(trend));
            estimate.setMemory(projectMemory(trend));
            estimate.setStorage(projectStorage(trend));
            estimate.setNetwork(projectNetwork(trend));
            
            return estimate;
        }
        
        private TrendAnalysis analyzeTrend(HistoricalData data) {
            // 分析资源使用趋势
            TrendAnalysis trend = new TrendAnalysis();
            trend.setGrowthRate(calculateGrowthRate(data));
            trend.setSeasonality(analyzeSeasonality(data));
            trend.setPeakPattern(analyzePeakPattern(data));
            return trend;
        }
    }
    
    // 基准测试
    public class BenchmarkTesting {
        public CapacityEstimate estimateByBenchmark(Workload workload) {
            // 通过基准测试评估容量
            BenchmarkResult result = runBenchmark(workload);
            
            CapacityEstimate estimate = new CapacityEstimate();
            estimate.setCPU(calculateCPUFromBenchmark(result));
            estimate.setMemory(calculateMemoryFromBenchmark(result));
            estimate.setStorage(calculateStorageFromBenchmark(result));
            
            return estimate;
        }
        
        private BenchmarkResult runBenchmark(Workload workload) {
            // 运行基准测试
            // 1. 准备测试环境
            // 2. 执行测试
            // 3. 收集结果
            return benchmarkService.run(workload);
        }
    }
    
    // 压力测试
    public class StressTesting {
        public CapacityLimit findCapacityLimit() {
            // 通过压力测试找到容量上限
            int currentLoad = 100;
            int step = 50;
            
            while (true) {
                StressTestResult result = runStressTest(currentLoad);
                
                if (result.hasFailure()) {
                    // 找到容量上限
                    return new CapacityLimit(currentLoad - step);
                }
                
                currentLoad += step;
            }
        }
        
        private StressTestResult runStressTest(int load) {
            // 执行压力测试
            return stressTestService.run(load);
        }
    }
}

3. 资源评估

3.1 CPU评估

3.1.1 CPU容量评估

CPU容量评估：

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// CPU容量评估
public class CPUCapacityAssessment {
    
    public CPURequirement assessCPU(WorkloadAnalysis workload) {
        CPURequirement requirement = new CPURequirement();
        
        // 1. 计算单请求CPU消耗
        double cpuPerRequest = calculateCPUPerRequest();
        
        // 2. 计算总CPU需求
        double totalCPU = workload.getPeakQPS() * cpuPerRequest;
        
        // 3. 考虑安全余量（20%）
        double safetyMargin = 0.2;
        totalCPU = totalCPU * (1 + safetyMargin);
        
        // 4. 计算需要的CPU核心数
        double cpuCores = totalCPU / 100; // 假设每个核心100%使用率
        
        requirement.setCores((int) Math.ceil(cpuCores));
        requirement.setUtilization(calculateOptimalUtilization());
        
        return requirement;
    }
    
    private double calculateCPUPerRequest() {
        // 根据历史数据计算每个请求的CPU消耗
        HistoricalMetrics metrics = monitoringService.getHistoricalMetrics();
        
        double totalCPU = metrics.getTotalCPU();
        long totalRequests = metrics.getTotalRequests();
        long timeWindow = metrics.getTimeWindow();
        
        // CPU消耗 = 总CPU使用率 * CPU核心数 * 时间窗口 / 总请求数
        double cpuPerRequest = (totalCPU / 100) * metrics.getCpuCores() * timeWindow / totalRequests;
        
        return cpuPerRequest;
    }
    
    private double calculateOptimalUtilization() {
        // 最优CPU使用率（通常70-80%）
        // 保留20-30%余量应对突发流量
        return 0.75;
    }
}

3.2 内存评估

3.2.1 内存容量评估

内存容量评估：

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// 内存容量评估
public class MemoryCapacityAssessment {
    
    public MemoryRequirement assessMemory(WorkloadAnalysis workload) {
        MemoryRequirement requirement = new MemoryRequirement();
        
        // 1. 基础内存（系统+应用）
        long baseMemory = calculateBaseMemory();
        
        // 2. 业务内存（缓存+数据）
        long businessMemory = calculateBusinessMemory(workload);
        
        // 3. 堆内存（JVM等）
        long heapMemory = calculateHeapMemory(workload);
        
        // 4. 总内存需求
        long totalMemory = baseMemory + businessMemory + heapMemory;
        
        // 5. 考虑安全余量（20%）
        totalMemory = (long) (totalMemory * 1.2);
        
        requirement.setTotalMemory(totalMemory);
        requirement.setBaseMemory(baseMemory);
        requirement.setBusinessMemory(businessMemory);
        requirement.setHeapMemory(heapMemory);
        
        return requirement;
    }
    
    private long calculateBaseMemory() {
        // 基础内存：操作系统 + 基础服务
        return 2L * 1024 * 1024 * 1024; // 2GB
    }
    
    private long calculateBusinessMemory(WorkloadAnalysis workload) {
        // 业务内存：缓存、会话等
        // 假设每个用户需要1MB内存
        long userMemory = workload.getExpectedUsers() * 1024 * 1024;
        
        // 缓存内存（假设需要存储1小时的数据）
        long cacheMemory = calculateCacheMemory(workload);
        
        return userMemory + cacheMemory;
    }
    
    private long calculateCacheMemory(WorkloadAnalysis workload) {
        // 缓存内存计算
        // 假设缓存命中率80%，需要缓存20%的数据
        long dataVolume = workload.getDataVolume();
        return (long) (dataVolume * 0.2);
    }
    
    private long calculateHeapMemory(WorkloadAnalysis workload) {
        // JVM堆内存计算
        // 通常设置为总内存的50-70%
        long totalMemory = calculateBaseMemory() + calculateBusinessMemory(workload);
        return (long) (totalMemory * 0.6);
    }
}

3.3 存储评估

3.3.1 存储容量评估

存储容量评估：

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// 存储容量评估
public class StorageCapacityAssessment {
    
    public StorageRequirement assessStorage(WorkloadAnalysis workload, int timeHorizon) {
        StorageRequirement requirement = new StorageRequirement();
        
        // 1. 当前数据量
        long currentData = monitoringService.getCurrentDataVolume();
        
        // 2. 数据增长量
        long dataGrowth = calculateDataGrowth(workload, timeHorizon);
        
        // 3. 总存储需求
        long totalStorage = currentData + dataGrowth;
        
        // 4. 考虑冗余（副本、备份等）
        double redundancyFactor = 3.0; // 3副本
        totalStorage = (long) (totalStorage * redundancyFactor);
        
        // 5. 考虑安全余量（30%）
        totalStorage = (long) (totalStorage * 1.3);
        
        requirement.setTotalStorage(totalStorage);
        requirement.setCurrentData(currentData);
        requirement.setDataGrowth(dataGrowth);
        requirement.setRedundancyFactor(redundancyFactor);
        
        return requirement;
    }
    
    private long calculateDataGrowth(WorkloadAnalysis workload, int timeHorizon) {
        // 数据增长量 = 每日数据增量 * 时间跨度
        long dailyDataGrowth = calculateDailyDataGrowth(workload);
        return dailyDataGrowth * timeHorizon;
    }
    
    private long calculateDailyDataGrowth(WorkloadAnalysis workload) {
        // 每日数据增量
        // 假设每个请求产生1KB数据
        long requestsPerDay = (long) (workload.getExpectedQPS() * 86400);
        return requestsPerDay * 1024; // 转换为字节
    }
}

3.4 网络评估

3.4.1 网络容量评估

网络容量评估：

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// 网络容量评估
public class NetworkCapacityAssessment {
    
    public NetworkRequirement assessNetwork(WorkloadAnalysis workload) {
        NetworkRequirement requirement = new NetworkRequirement();
        
        // 1. 入站带宽
        double inboundBandwidth = calculateInboundBandwidth(workload);
        
        // 2. 出站带宽
        double outboundBandwidth = calculateOutboundBandwidth(workload);
        
        // 3. 峰值带宽（考虑突发）
        double peakBandwidth = Math.max(inboundBandwidth, outboundBandwidth) * 2;
        
        // 4. 考虑安全余量（20%）
        peakBandwidth = peakBandwidth * 1.2;
        
        requirement.setInboundBandwidth(inboundBandwidth);
        requirement.setOutboundBandwidth(outboundBandwidth);
        requirement.setPeakBandwidth(peakBandwidth);
        
        return requirement;
    }
    
    private double calculateInboundBandwidth(WorkloadAnalysis workload) {
        // 入站带宽 = QPS * 平均请求大小
        double avgRequestSize = calculateAvgRequestSize();
        return workload.getPeakQPS() * avgRequestSize * 8 / 1024 / 1024; // 转换为Mbps
    }
    
    private double calculateOutboundBandwidth(WorkloadAnalysis workload) {
        // 出站带宽 = QPS * 平均响应大小
        double avgResponseSize = calculateAvgResponseSize();
        return workload.getPeakQPS() * avgResponseSize * 8 / 1024 / 1024; // 转换为Mbps
    }
    
    private double calculateAvgRequestSize() {
        // 计算平均请求大小
        HistoricalMetrics metrics = monitoringService.getHistoricalMetrics();
        return metrics.getTotalRequestSize() / metrics.getTotalRequests();
    }
    
    private double calculateAvgResponseSize() {
        // 计算平均响应大小
        HistoricalMetrics metrics = monitoringService.getHistoricalMetrics();
        return metrics.getTotalResponseSize() / metrics.getTotalRequests();
    }
}

4. 性能评估

4.1 性能指标

4.1.1 性能指标评估

性能指标评估：

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// 性能指标评估
public class PerformanceAssessment {
    
    public PerformanceMetrics assessPerformance(ResourceRequirement resources) {
        PerformanceMetrics metrics = new PerformanceMetrics();
        
        // 1. 响应时间评估
        metrics.setResponseTime(estimateResponseTime(resources));
        
        // 2. 吞吐量评估
        metrics.setThroughput(estimateThroughput(resources));
        
        // 3. 并发能力评估
        metrics.setConcurrency(estimateConcurrency(resources));
        
        // 4. 资源利用率评估
        metrics.setResourceUtilization(estimateResourceUtilization(resources));
        
        return metrics;
    }
    
    private double estimateResponseTime(ResourceRequirement resources) {
        // 估算响应时间
        // 响应时间 = 处理时间 + 网络延迟 + 排队时间
        
        double processingTime = estimateProcessingTime(resources);
        double networkLatency = 10; // 假设10ms网络延迟
        double queueTime = estimateQueueTime(resources);
        
        return processingTime + networkLatency + queueTime;
    }
    
    private double estimateProcessingTime(ResourceRequirement resources) {
        // 处理时间与CPU和内存相关
        // CPU充足时，处理时间主要取决于业务逻辑复杂度
        double baseProcessingTime = 50; // 基础处理时间50ms
        
        // CPU不足时，处理时间会增加
        if (resources.getCPU().getUtilization() > 0.8) {
            baseProcessingTime *= 1.5; // CPU高负载时增加50%
        }
        
        return baseProcessingTime;
    }
    
    private double estimateQueueTime(ResourceRequirement resources) {
        // 排队时间（当请求超过处理能力时）
        // 使用排队论模型估算
        double arrivalRate = resources.getWorkload().getPeakQPS();
        double serviceRate = resources.getCPU().getCores() * 1000 / estimateProcessingTime(resources);
        
        if (arrivalRate >= serviceRate) {
            // 系统过载，排队时间无限增长
            return Double.MAX_VALUE;
        }
        
        // M/M/1排队模型
        double utilization = arrivalRate / serviceRate;
        return utilization / (serviceRate * (1 - utilization));
    }
    
    private double estimateThroughput(ResourceRequirement resources) {
        // 吞吐量估算
        // 吞吐量 = min(CPU处理能力, 内存限制, 网络限制)
        
        double cpuThroughput = resources.getCPU().getCores() * 1000 / estimateProcessingTime(resources);
        double memoryThroughput = estimateMemoryThroughput(resources);
        double networkThroughput = estimateNetworkThroughput(resources);
        
        return Math.min(cpuThroughput, Math.min(memoryThroughput, networkThroughput));
    }
    
    private int estimateConcurrency(ResourceRequirement resources) {
        // 并发能力估算
        // 并发数 = 响应时间 * 吞吐量
        double responseTime = estimateResponseTime(resources);
        double throughput = estimateThroughput(resources);
        
        return (int) (responseTime * throughput / 1000);
    }
}

4.2 瓶颈分析

4.2.1 性能瓶颈识别

性能瓶颈分析：

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// 性能瓶颈分析
public class BottleneckAnalysis {
    
    public List<Bottleneck> identifyBottlenecks(ResourceRequirement resources, 
                                                PerformanceMetrics metrics) {
        List<Bottleneck> bottlenecks = new ArrayList<>();
        
        // 1. CPU瓶颈
        if (resources.getCPU().getUtilization() > 0.8) {
            bottlenecks.add(new Bottleneck("CPU", "CPU使用率过高", 
                resources.getCPU().getUtilization()));
        }
        
        // 2. 内存瓶颈
        if (resources.getMemory().getUtilization() > 0.85) {
            bottlenecks.add(new Bottleneck("Memory", "内存使用率过高", 
                resources.getMemory().getUtilization()));
        }
        
        // 3. 存储瓶颈
        if (resources.getStorage().getUtilization() > 0.8) {
            bottlenecks.add(new Bottleneck("Storage", "存储使用率过高", 
                resources.getStorage().getUtilization()));
        }
        
        // 4. 网络瓶颈
        if (resources.getNetwork().getUtilization() > 0.8) {
            bottlenecks.add(new Bottleneck("Network", "网络带宽使用率过高", 
                resources.getNetwork().getUtilization()));
        }
        
        // 5. 响应时间瓶颈
        if (metrics.getResponseTime() > 1000) {
            bottlenecks.add(new Bottleneck("ResponseTime", "响应时间过长", 
                metrics.getResponseTime()));
        }
        
        return bottlenecks;
    }
    
    public Bottleneck findPrimaryBottleneck(List<Bottleneck> bottlenecks) {
        // 找出主要瓶颈（影响最大的）
        return bottlenecks.stream()
            .max(Comparator.comparing(Bottleneck::getImpact))
            .orElse(null);
    }
    
    public List<Recommendation> generateRecommendations(List<Bottleneck> bottlenecks) {
        List<Recommendation> recommendations = new ArrayList<>();
        
        for (Bottleneck bottleneck : bottlenecks) {
            switch (bottleneck.getType()) {
                case "CPU":
                    recommendations.add(new Recommendation(
                        "增加CPU核心数",
                        "当前CPU使用率过高，建议增加CPU核心数"
                    ));
                    break;
                case "Memory":
                    recommendations.add(new Recommendation(
                        "增加内存容量",
                        "当前内存使用率过高，建议增加内存容量"
                    ));
                    break;
                case "Storage":
                    recommendations.add(new Recommendation(
                        "扩容存储",
                        "当前存储使用率过高，建议扩容存储"
                    ));
                    break;
                case "Network":
                    recommendations.add(new Recommendation(
                        "增加网络带宽",
                        "当前网络带宽使用率过高，建议增加网络带宽"
                    ));
                    break;
            }
        }
        
        return recommendations;
    }
}

5. 容量规划

5.1 规划方法

5.1.1 容量规划

容量规划：

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// 容量规划
public class CapacityPlanning {
    
    public CapacityPlan createPlan(CapacityAssessmentResult assessment, 
                                   BusinessRequirements requirements) {
        CapacityPlan plan = new CapacityPlan();
        
        // 1. 当前容量
        plan.setCurrentCapacity(assessment.getCurrentState());
        
        // 2. 目标容量
        plan.setTargetCapacity(calculateTargetCapacity(assessment, requirements));
        
        // 3. 容量缺口
        plan.setGap(calculateGap(plan.getCurrentCapacity(), plan.getTargetCapacity()));
        
        // 4. 扩容方案
        plan.setScalingPlan(createScalingPlan(plan.getGap(), requirements));
        
        // 5. 时间计划
        plan.setTimeline(createTimeline(requirements));
        
        // 6. 成本估算
        plan.setCostEstimate(estimateCost(plan.getScalingPlan()));
        
        return plan;
    }
    
    private TargetCapacity calculateTargetCapacity(CapacityAssessmentResult assessment, 
                                                  BusinessRequirements requirements) {
        TargetCapacity target = new TargetCapacity();
        
        // 根据业务需求计算目标容量
        target.setCPU(assessment.getResources().getCPU());
        target.setMemory(assessment.getResources().getMemory());
        target.setStorage(assessment.getResources().getStorage());
        target.setNetwork(assessment.getResources().getNetwork());
        
        // 考虑增长
        double growthRate = requirements.getGrowthRate();
        int timeHorizon = requirements.getTimeHorizon();
        double growthFactor = Math.pow(1 + growthRate, timeHorizon);
        
        target.setCPU(target.getCPU().scale(growthFactor));
        target.setMemory(target.getMemory().scale(growthFactor));
        target.setStorage(target.getStorage().scale(growthFactor));
        target.setNetwork(target.getNetwork().scale(growthFactor));
        
        return target;
    }
    
    private ScalingPlan createScalingPlan(CapacityGap gap, BusinessRequirements requirements) {
        ScalingPlan plan = new ScalingPlan();
        
        // 垂直扩容（Scale Up）
        if (gap.getCPU().getCores() <= 4) {
            plan.addAction(new ScalingAction("Scale Up", "增加单机资源"));
        }
        
        // 水平扩容（Scale Out）
        if (gap.getCPU().getCores() > 4) {
            int instances = (int) Math.ceil(gap.getCPU().getCores() / 4.0);
            plan.addAction(new ScalingAction("Scale Out", 
                "增加实例数量: " + instances));
        }
        
        // 存储扩容
        if (gap.getStorage().getTotalStorage() > 0) {
            plan.addAction(new ScalingAction("Storage Expansion", 
                "扩容存储: " + gap.getStorage().getTotalStorage() / 1024 / 1024 / 1024 + "GB"));
        }
        
        // 网络扩容
        if (gap.getNetwork().getPeakBandwidth() > 0) {
            plan.addAction(new ScalingAction("Network Upgrade", 
                "升级网络带宽: " + gap.getNetwork().getPeakBandwidth() + "Mbps"));
        }
        
        return plan;
    }
    
    private Timeline createTimeline(BusinessRequirements requirements) {
        Timeline timeline = new Timeline();
        
        // 根据业务需求制定时间计划
        int timeHorizon = requirements.getTimeHorizon();
        
        // 短期（1-3个月）
        timeline.addPhase("短期", 3, "快速扩容，满足当前需求");
        
        // 中期（3-6个月）
        timeline.addPhase("中期", 6, "优化架构，提升效率");
        
        // 长期（6-12个月）
        timeline.addPhase("长期", 12, "架构升级，支持未来增长");
        
        return timeline;
    }
    
    private CostEstimate estimateCost(ScalingPlan plan) {
        CostEstimate estimate = new CostEstimate();
        
        // 计算扩容成本
        double totalCost = 0;
        
        for (ScalingAction action : plan.getActions()) {
            double actionCost = calculateActionCost(action);
            totalCost += actionCost;
        }
        
        estimate.setTotalCost(totalCost);
        estimate.setMonthlyCost(totalCost / 12); // 假设按年付费
        
        return estimate;
    }
}

6. 容量监控

6.1 监控指标

6.1.1 容量监控

容量监控：

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// 容量监控
public class CapacityMonitoring {
    
    public void setupCapacityMonitoring(CapacityPlan plan) {
        // 设置容量监控
        scheduler.scheduleAtFixedRate(() -> {
            monitorCapacity(plan);
        }, 0, 1, TimeUnit.HOURS);
    }
    
    private void monitorCapacity(CapacityPlan plan) {
        // 1. 监控资源使用率
        ResourceUtilization utilization = getCurrentUtilization();
        
        // 2. 检查容量告警
        checkCapacityAlerts(utilization, plan);
        
        // 3. 预测容量耗尽时间
        predictCapacityExhaustion(utilization, plan);
        
        // 4. 生成容量报告
        generateCapacityReport(utilization, plan);
    }
    
    private void checkCapacityAlerts(ResourceUtilization utilization, CapacityPlan plan) {
        // CPU告警
        if (utilization.getCpuUsage() > 0.8) {
            alertService.sendAlert("CPU使用率过高: " + utilization.getCpuUsage());
        }
        
        // 内存告警
        if (utilization.getMemoryUsage() > 0.85) {
            alertService.sendAlert("内存使用率过高: " + utilization.getMemoryUsage());
        }
        
        // 存储告警
        if (utilization.getStorageUsage() > 0.8) {
            alertService.sendAlert("存储使用率过高: " + utilization.getStorageUsage());
        }
        
        // 容量耗尽告警
        long daysToExhaustion = calculateDaysToExhaustion(utilization, plan);
        if (daysToExhaustion < 30) {
            alertService.sendAlert("容量将在" + daysToExhaustion + "天内耗尽");
        }
    }
    
    private long calculateDaysToExhaustion(ResourceUtilization utilization, CapacityPlan plan) {
        // 计算容量耗尽时间
        double growthRate = plan.getGrowthRate();
        double currentUsage = utilization.getStorageUsage();
        double capacity = plan.getTargetCapacity().getStorage().getTotalStorage();
        
        if (growthRate <= 0) {
            return Long.MAX_VALUE; // 无增长，不会耗尽
        }
        
        // 使用指数增长模型
        double remainingCapacity = capacity * (1 - currentUsage);
        double dailyGrowth = capacity * growthRate / 365;
        
        return (long) (remainingCapacity / dailyGrowth);
    }
    
    private void generateCapacityReport(ResourceUtilization utilization, CapacityPlan plan) {
        CapacityReport report = new CapacityReport();
        
        report.setCurrentUtilization(utilization);
        report.setTargetCapacity(plan.getTargetCapacity());
        report.setDaysToExhaustion(calculateDaysToExhaustion(utilization, plan));
        report.setRecommendations(generateRecommendations(utilization, plan));
        
        reportService.save(report);
    }
}

7. 实战案例

7.1 电商系统容量评估

7.1.1 完整评估案例

电商系统容量评估案例：

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// 电商系统容量评估案例
public class ECommerceCapacityAssessmentCase {
    
    public void executeAssessment() {
        // 1. 业务需求
        BusinessRequirements requirements = new BusinessRequirements();
        requirements.setExpectedUsers(1000000); // 100万用户
        requirements.setExpectedQPS(10000);      // 1万QPS
        requirements.setGrowthRate(0.2);         // 20%年增长率
        requirements.setTimeHorizon(12);         // 12个月
        
        // 2. 执行容量评估
        CapacityAssessmentProcess process = new CapacityAssessmentProcess();
        CapacityAssessmentResult result = process.executeAssessment(requirements);
        
        // 3. 资源评估
        ResourceRequirement resources = result.getResources();
        System.out.println("CPU需求: " + resources.getCPU().getCores() + " 核心");
        System.out.println("内存需求: " + resources.getMemory().getTotalMemory() / 1024 / 1024 / 1024 + " GB");
        System.out.println("存储需求: " + resources.getStorage().getTotalStorage() / 1024 / 1024 / 1024 + " GB");
        System.out.println("网络需求: " + resources.getNetwork().getPeakBandwidth() + " Mbps");
        
        // 4. 性能评估
        PerformanceMetrics performance = result.getPerformance();
        System.out.println("预期响应时间: " + performance.getResponseTime() + " ms");
        System.out.println("预期吞吐量: " + performance.getThroughput() + " QPS");
        System.out.println("并发能力: " + performance.getConcurrency() + " 并发");
        
        // 5. 容量规划
        CapacityPlanning planning = new CapacityPlanning();
        CapacityPlan plan = planning.createPlan(result, requirements);
        
        // 6. 实施监控
        CapacityMonitoring monitoring = new CapacityMonitoring();
        monitoring.setupCapacityMonitoring(plan);
    }
}

8. 总结

8.1 核心要点

  1. 评估方法：历史分析、基准测试、压力测试、建模分析
  2. 资源评估：CPU、内存、存储、网络等资源评估
  3. 性能评估：响应时间、吞吐量、并发能力评估
  4. 容量规划：基于评估结果制定容量规划
  5. 容量监控：持续监控和调整容量
  6. 持续优化：根据监控结果持续优化容量

8.2 关键理解

  1. 需求驱动：容量评估应该由业务需求驱动
  2. 数据支撑：基于历史数据和监控数据进行评估
  3. 安全余量：保留适当的安全余量应对突发情况
  4. 持续监控：容量不是一次性的，需要持续监控和调整
  5. 成本平衡：在满足需求的前提下控制成本

8.3 最佳实践

  1. 定期评估：定期进行容量评估（每季度或每半年）
  2. 数据驱动：基于实际监控数据进行评估
  3. 预留余量：保留20-30%的安全余量
  4. 自动化监控：建立自动化容量监控和告警
  5. 提前规划：提前规划扩容，避免容量耗尽
  6. 成本优化：在满足需求的前提下优化成本

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