鸿蒙机器学习负载均衡测试方案设计与实现 原创

鸿蒙机器学习负载均衡测试方案设计与实现

一、系统架构设计

基于HarmonyOS的分布式能力，我们设计了一套机器学习负载均衡系统，用于优化跨设备场景下的AI推理任务分配。

!https://example.com/ml-loadbalance-arch.png

系统包含三大核心模块：
设备性能评估模块 - 实时监测各设备计算能力

任务调度模块 - 动态分配AI推理任务

负载均衡测试模块 - 验证调度策略有效性

二、核心代码实现
设备性能评估服务（Java）

// DevicePerformanceService.java
public class DevicePerformanceService extends Ability {
private static final String TAG = “DevicePerformance”;
private static final int UPDATE_INTERVAL = 5000; // 5秒更新一次

private Map<String, DevicePerf> devicePerfMap = new ConcurrentHashMap<>();
private ScheduledExecutorService scheduler;

@Override
public void onStart(Intent intent) {
    super.onStart(intent);
    startMonitoring();

private void startMonitoring() {

    scheduler = Executors.newSingleThreadScheduledExecutor();
    scheduler.scheduleAtFixedRate(() -> {
        updateLocalDevicePerf();
        updateRemoteDevicesPerf();
    }, 0, UPDATE_INTERVAL, TimeUnit.MILLISECONDS);

private void updateLocalDevicePerf() {

    DevicePerf perf = new DevicePerf();
    perf.deviceId = getLocalDeviceId();
    perf.cpuUsage = getCpuUsage();
    perf.memAvailable = getAvailableMemory();
    perf.gpuCapability = getGpuCapability();
    perf.networkLatency = getNetworkLatency();
    
    // 计算综合性能评分
    perf.score = calculatePerformanceScore(perf);
    
    devicePerfMap.put(perf.deviceId, perf);

private void updateRemoteDevicesPerf() {

    DistributedDeviceManager.getDevices().forEach(device -> {
        if (!devicePerfMap.containsKey(device.id)) {
            DevicePerf perf = fetchRemoteDevicePerf(device.id);
            devicePerfMap.put(device.id, perf);

});

private float calculatePerformanceScore(DevicePerf perf) {

    // 加权计算性能评分
    return perf.cpuUsage * 0.3f + 
           perf.memAvailable * 0.2f + 
           perf.gpuCapability * 0.4f + 
           (1 - perf.networkLatency) * 0.1f;

// 获取设备性能推荐

public DevicePerf getDevicePerf(String deviceId) {
    return devicePerfMap.getOrDefault(deviceId, new DevicePerf());

// 获取最优设备

public String getBestDevice() {
    return devicePerfMap.entrySet().stream()
        .max(Comparator.comparingDouble(e -> e.getValue().score))
        .map(Map.Entry::getKey)
        .orElse(getLocalDeviceId());

public static class DevicePerf {

    public String deviceId;
    public float cpuUsage;       // CPU使用率(0-1)
    public float memAvailable;   // 可用内存比例(0-1)
    public float gpuCapability;  // GPU能力评分(0-1)
    public float networkLatency; // 网络延迟(0-1)
    public float score;          // 综合评分
    
    public boolean isAvailable() {
        return cpuUsage < 0.8f && memAvailable > 0.2f;

}

负载均衡调度器（Java）

// LoadBalancer.java
public class LoadBalancer {
private static final String TAG = “LoadBalancer”;
private DevicePerformanceService perfService;
private Map<String, Integer> taskCountMap = new ConcurrentHashMap<>();

public LoadBalancer(Context context) {
    perfService = new DevicePerformanceService(context);

// 分配推理任务

public String assignTask(MLTask task) {
    // 获取可用设备列表
    Map<String, DevicePerformanceService.DevicePerf> availableDevices = 
        perfService.getAvailableDevices();
    
    // 负载均衡策略
    String deviceId = chooseDevice(availableDevices, task);
    
    // 记录任务分配
    taskCountMap.merge(deviceId, 1, Integer::sum);
    HiLog.info(TAG, "任务分配: " + task.taskId + " → " + deviceId);
    
    return deviceId;

private String chooseDevice(Map<String, DevicePerformanceService.DevicePerf> devices,

                           MLTask task) {
    // 根据任务类型选择策略
    switch (task.priority) {
        case HIGH:
            // 高性能优先
            return devices.entrySet().stream()
                .max(Comparator.comparingDouble(e -> e.getValue().score))
                .map(Map.Entry::getKey)
                .orElse(perfService.getLocalDeviceId());
            
        case LOW:
            // 低负载优先
            return devices.entrySet().stream()
                .min(Comparator.comparingInt(e -> taskCountMap.getOrDefault(e.getKey(), 0)))
                .map(Map.Entry::getKey)
                .orElse(perfService.getLocalDeviceId());
            
        default:
            // 混合策略
            return devices.entrySet().stream()
                .max(Comparator.comparingDouble(e -> 
                    e.getValue().score  0.7 - taskCountMap.getOrDefault(e.getKey(), 0)  0.3))
                .map(Map.Entry::getKey)
                .orElse(perfService.getLocalDeviceId());

}

// 任务完成回调
public void onTaskCompleted(String deviceId) {
    taskCountMap.computeIfPresent(deviceId, (k, v) -> v > 0 ? v - 1 : 0);

public static class MLTask {

    public String taskId;
    public Priority priority;
    public long dataSize;
    
    public enum Priority {
        HIGH, MEDIUM, LOW

}

负载测试界面（ArkTS）

// LoadTestUI.ets
import lb from ‘…/services/LoadBalancer’;
import devicePerf from ‘…/services/DevicePerformanceService’;

@Entry
@Component
struct LoadTestUI {
@State devices: DeviceInfo[] = [];
@State testCases: TestCase[] = [];
@State testResults: TestResult[] = [];
@State isTesting: boolean = false;
@State currentStrategy: string = ‘performance’;

aboutToAppear() {
this.loadTestCases();
this.loadDevices();
private loadTestCases() {

this.testCases = [

id: ‘light’, name: ‘轻量级任务’, complexity: 1 },

id: ‘medium’, name: ‘中等任务’, complexity: 2 },

id: ‘heavy’, name: ‘重量级任务’, complexity: 3 }

];

private loadDevices() {

devicePerf.getDevices((devices) => {
  this.devices = devices.map(d => ({
    id: d.deviceId,
    name: d.deviceName,
    score: d.score
  }));
});

build() {

Column() {
  // 策略选择
  this.buildStrategySelector()
  
  // 设备状态
  this.buildDeviceStatus()
  
  // 测试用例
  this.buildTestCases()
  
  // 测试结果
  if (this.testResults.length > 0) {
    this.buildTestResults()

}

@Builder

private buildStrategySelector() {
Row() {
Text(‘调度策略:’)
.fontSize(16)

  Select({
    options: [

value: ‘performance’, name: ‘性能优先’ },

value: ‘balance’, name: ‘负载均衡’ },

value: ‘hybrid’, name: ‘混合模式’ }

    ],
    selected: this.currentStrategy
  })
  .onSelect((value: string) => {
    this.currentStrategy = value;
    lb.setStrategy(value);
  })

.padding(10)

@Builder

private buildDeviceStatus() {
Column() {
Text(‘设备状态’)
.fontSize(18)
.margin(10)

  Grid() {
    ForEach(this.devices, (device) => {
      GridItem() {
        DeviceStatusCard({ device })

})

.columnsTemplate(‘1fr 1fr’)

  .columnsGap(10)
  .rowsGap(10)

}

@Builder
private buildTestCases() {
Column() {
Text(‘测试用例’)
.fontSize(18)
.margin(10)

  ForEach(this.testCases, (testCase) => {
    Button(testCase.name)
      .onClick(() => this.runTest(testCase))
      .width('80%')
      .margin(5)
  })

}

@Builder
private buildTestResults() {
Column() {
Text(‘测试结果’)
.fontSize(18)
.margin(10)

  List() {
    ForEach(this.testResults, (result) => {
      ListItem() {
        TestResultItem({ result })

})

.height(200)

}

private runTest(testCase: TestCase) {
this.isTesting = true;

// 模拟任务
const task = {
  id: test_{testCase.id}_{Date.now()},
  complexity: testCase.complexity
};

// 分配任务
const startTime = Date.now();
const deviceId = lb.assignTask(task);
const assignTime = Date.now() - startTime;

// 模拟任务执行
setTimeout(() => {
  const endTime = Date.now();
  const execTime = endTime - startTime;
  
  this.testResults = [...this.testResults, {
    testId: task.id,
    testName: testCase.name,
    deviceId,
    assignTime,
    execTime,
    strategy: this.currentStrategy
  }];
  
  lb.onTaskCompleted(deviceId);
  this.isTesting = false;
}, testCase.complexity * 1000);

}

@Component
struct DeviceStatusCard {
@Prop device: DeviceInfo

build() {
Stack() {
Column() {
Text(this.device.name)
.fontSize(16)

    Text(性能评分: ${this.device.score.toFixed(2)})
      .fontSize(12)
      .fontColor('#666666')

.width(‘100%’)

  .padding(10)
  
  // 性能指示器
  Circle()
    .width(10)
    .height(10)
    .fill(this.getStatusColor())
    .position({ x: '85%', y: '10%' })

.height(80)

.borderRadius(8)
.backgroundColor('#F5F5F5')

private getStatusColor(): Color {

if (this.device.score > 0.7) return Color.Green;
if (this.device.score > 0.4) return Color.Yellow;
return Color.Red;

}

@Component
struct TestResultItem {
@Prop result: TestResult

build() {
Row() {
Column() {
Text(this.result.testName)
.fontSize(16)

    Text(设备: ${this.result.deviceId})
      .fontSize(12)
      .fontColor('#666666')

.layoutWeight(1)

  Column() {
    Text(${this.result.execTime}ms)
      .fontSize(14)
    
    Text(this.result.strategy)
      .fontSize(12)
      .fontColor('#409EFF')

}

.padding(10)

}

interface DeviceInfo {
id: string;
name: string;
score: number;
interface TestCase {

id: string;
name: string;
complexity: number;
interface TestResult {

testId: string;
testName: string;
deviceId: string;
assignTime: number;
execTime: number;
strategy: string;

三、关键技术实现
负载均衡算法

// 负载均衡策略实现
function balanceStrategy(devices: DeviceInfo[], tasks: Task[]): AllocationResult {
const deviceScores = devices.map(d => ({
id: d.id,
score: d.cpuScore 0.4 + d.memScore 0.3 + d.gpuScore * 0.3
}));

// 按任务复杂度排序
const sortedTasks = […tasks].sort((a, b) => b.complexity - a.complexity);

const allocations: AllocationResult = {};

for (const task of sortedTasks) {
// 找出最适合的设备
const bestDevice = deviceScores.reduce((prev, curr) => {
const prevLoad = allocations[prev.id]?.load || 0;
const currLoad = allocations[curr.id]?.load || 0;

  // 平衡分数和当前负载
  const prevScore = prev.score - prevLoad * 0.5;
  const currScore = curr.score - currLoad * 0.5;
  
  return currScore > prevScore ? curr : prev;
});

// 分配任务
if (!allocations[bestDevice.id]) {
  allocations[bestDevice.id] = { tasks: [], load: 0 };

allocations[bestDevice.id].tasks.push(task.id);

allocations[bestDevice.id].load += task.complexity;

return allocations;

设备性能评估模型

// 设备性能评估算法
public class DeviceEvaluator {
private static final float CPU_WEIGHT = 0.4f;
private static final float GPU_WEIGHT = 0.4f;
private static final float MEM_WEIGHT = 0.2f;

public static float evaluatePerformance(DeviceSpec spec) {
    // 标准化各指标
    float cpuScore = normalize(spec.cpuBenchmark, 1000, 5000);
    float gpuScore = normalize(spec.gpuBenchmark, 500, 3000);
    float memScore = normalize(spec.memSize, 2, 8);
    
    // 加权计算
    return cpuScore * CPU_WEIGHT + 
           gpuScore * GPU_WEIGHT + 
           memScore * MEM_WEIGHT;

private static float normalize(float value, float min, float max) {

    return Math.min(1, Math.max(0, (value - min) / (max - min)));

public static class DeviceSpec {

    public float cpuBenchmark; // CPU基准分
    public float gpuBenchmark; // GPU基准分
    public float memSize;      // 内存大小(GB)

}

动态任务调度流程

sequenceDiagram
participant 客户端
participant 调度器
participant 设备A
participant 设备B

客户端->>调度器: 提交AI任务(优先级HIGH)
调度器->>设备A: 检查设备状态
设备A-->>调度器: 返回性能指标
调度器->>设备B: 检查设备状态
设备B-->>调度器: 返回性能指标
调度器->>调度器: 选择最优设备(设备B)
调度器->>设备B: 分配任务
设备B->>设备B: 执行AI推理
设备B-->>客户端: 返回结果
设备B-->>调度器: 通知任务完成

四、测试方案
基准测试用例（Java）

public class LoadBalancerTest {
private LoadBalancer loadBalancer;
private DevicePerformanceService perfService;

@Before
public void setup() {
    perfService = new DevicePerformanceService();
    loadBalancer = new LoadBalancer(perfService);

@Test

public void testPerformanceStrategy() {
    // 模拟3个设备
    perfService.updateDevicePerf("device1", 0.9f); // 高性能
    perfService.updateDevicePerf("device2", 0.7f);
    perfService.updateDevicePerf("device3", 0.5f);
    
    // 创建高优先级任务
    LoadBalancer.MLTask task = new LoadBalancer.MLTask();
    task.taskId = "high_priority_task";
    task.priority = LoadBalancer.MLTask.Priority.HIGH;
    
    // 验证分配结果
    String assignedDevice = loadBalancer.assignTask(task);
    assertEquals("device1", assignedDevice);

@Test

public void testBalanceStrategy() {
    // 模拟3个设备
    perfService.updateDevicePerf("device1", 0.8f);
    perfService.updateDevicePerf("device2", 0.7f);
    perfService.updateDevicePerf("device3", 0.6f);
    
    // 让device1已经有3个任务
    loadBalancer.taskCountMap.put("device1", 3);
    
    // 创建中优先级任务
    LoadBalancer.MLTask task = new LoadBalancer.MLTask();
    task.taskId = "medium_priority_task";
    task.priority = LoadBalancer.MLTask.Priority.MEDIUM;
    
    // 验证分配结果
    String assignedDevice = loadBalancer.assignTask(task);
    assertEquals("device2", assignedDevice);

}

端到端负载测试（ArkTS）

// LoadTest.ets
@Entry
@Component
struct LoadTest {
@State testResults: LoadTestResult[] = [];
@State isTesting: boolean = false;

build() {
Column() {
Button(this.isTesting ? ‘测试中…’ : ‘开始负载测试’)
.onClick(() => this.runLoadTest())
.disabled(this.isTesting)
.width(‘80%’)
.margin(10)

  if (this.testResults.length > 0) {
    this.buildResults()

}

@Builder

private buildResults() {
Column() {
Text(‘负载测试结果’)
.fontSize(18)
.margin(10)

  Grid() {
    ForEach(this.testResults, (result) => {
      GridItem() {
        TestResultCard({ result })

})

.columnsTemplate(‘1fr 1fr’)

  .columnsGap(10)
  .rowsGap(10)

}

private async runLoadTest() {
this.isTesting = true;
this.testResults = [];

// 模拟100个任务
const tasks = this.generateTasks(100);
const startTime = Date.now();

// 执行任务分配
const allocations: Record<string, number> = {};
for (const task of tasks) {
  const deviceId = lb.assignTask(task);
  allocations[deviceId] = (allocations[deviceId] || 0) + 1;
  
  // 模拟任务执行
  await new Promise(resolve => 
    setTimeout(resolve, task.complexity * 10));
  
  lb.onTaskCompleted(deviceId);

// 记录结果

const totalTime = Date.now() - startTime;
this.testResults = Object.entries(allocations).map(([deviceId, count]) => ({
  deviceId,
  taskCount: count,
  efficiency: count / totalTime
}));

this.isTesting = false;

private generateTasks(count: number): TestTask[] {

const tasks: TestTask[] = [];
for (let i = 0; i < count; i++) {
  tasks.push({
    id: task_${i},
    complexity: Math.floor(Math.random() * 3) + 1,
    priority: ['high', 'medium', 'low'][Math.floor(Math.random() * 3)]
  });

return tasks;

}

@Component
struct TestResultCard {
@Prop result: LoadTestResult

build() {
Column() {
Text(this.result.deviceId)
.fontSize(16)

  Text(任务数: ${this.result.taskCount})
    .fontSize(14)
  
  Text(效率: ${this.result.efficiency.toFixed(2)})
    .fontSize(12)
    .fontColor('#409EFF')

.padding(10)

.borderRadius(8)
.backgroundColor('#F5F5F5')

}

interface TestTask {
id: string;
complexity: number;
priority: string;
interface LoadTestResult {

deviceId: string;
taskCount: number;
efficiency: number;

五、总结与展望

本方案实现了以下核心功能：
智能任务分配：根据设备性能和当前负载动态分配AI任务

多策略支持：支持性能优先、负载均衡等多种调度策略

实时监控：持续跟踪设备状态和任务执行情况

弹性扩展：可轻松集成新设备和AI模型

未来优化方向：
增加预测性调度算法

支持联邦学习场景

集成能耗优化策略

增强异常处理能力

通过本方案，可以充分发挥鸿蒙分布式能力，在跨设备场景下实现高效的机器学习任务协同，为游戏、AR/VR等高性能应用提供有力支撑。