Java實(shí)現(xiàn)Consul/Nacos根據(jù)GPU型號(hào)、顯存余量執(zhí)行負(fù)載均衡的步驟詳解

更新時(shí)間：2025年04月07日 15:20:54 作者：程序猿000001號(hào)

這篇文章主要介紹了Java實(shí)現(xiàn)Consul/Nacos根據(jù)GPU型號(hào)、顯存余量執(zhí)行負(fù)載均衡的步驟詳解,本文分步驟結(jié)合實(shí)例代碼給大家介紹的非常詳細(xì),需要的朋友可以參考下

Java實(shí)現(xiàn)Consul/Nacos根據(jù)GPU型號(hào)、顯存余量執(zhí)行負(fù)載均衡

步驟一：服務(wù)端獲取GPU元數(shù)據(jù)

1. 添加依賴

在pom.xml中引入Apache Commons Exec用于執(zhí)行命令：

<dependency>
    <groupId>org.apache.commons</groupId>
    <artifactId>commons-exec</artifactId>
    <version>1.3</version>
</dependency>
<dependency>
    <groupId>com.google.code.gson</groupId>
    <artifactId>gson</artifactId>
    <version>2.8.9</version>
</dependency>

2. 實(shí)現(xiàn)GPU信息采集

import org.apache.commons.exec.CommandLine;
import org.apache.commons.exec.DefaultExecutor;
import org.apache.commons.exec.PumpStreamHandler;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import com.google.gson.Gson;
public class GpuInfoUtil {
    public static List<GpuMeta> getGpuMetadata() throws IOException {
        CommandLine cmd = CommandLine.parse("nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader,nounits");
        ByteArrayOutputStream outputStream = new ByteArrayOutputStream();
        PumpStreamHandler streamHandler = new PumpStreamHandler(outputStream);
        DefaultExecutor executor = new DefaultExecutor();
        executor.setStreamHandler(streamHandler);
        executor.execute(cmd);
        String output = outputStream.toString();
        return parseOutput(output);
    }
    private static List<GpuMeta> parseOutput(String output) {
        List<GpuMeta> gpus = new ArrayList<>();
        for (String line : output.split("\\r?\\n")) {
            String[] parts = line.split(",");
            if (parts.length >= 3) {
                String name = parts[0].trim();
                long total = Long.parseLong(parts[1].trim()) * 1024 * 1024; // MB -> bytes
                long free = Long.parseLong(parts[2].trim()) * 1024 * 1024;
                gpus.add(new GpuMeta(name, total, free));
            }
        }
        return gpus;
    }
    public static class GpuMeta {
        private String name;
        private long totalMem;
        private long freeMem;
        // 構(gòu)造方法、getters、setters省略
    }
}

步驟二：服務(wù)注冊(cè)到Consul/Nacos

1. Consul注冊(cè)實(shí)現(xiàn)

import com.ecwid.consul.v1.ConsulClient;
import com.ecwid.consul.v1.agent.model.NewService;
public class ConsulRegistrar {
    public void register(String serviceName, String ip, int port) throws Exception {
        ConsulClient consul = new ConsulClient("localhost", 8500);
        List<GpuMeta> gpus = GpuInfoUtil.getGpuMetadata();
        NewService service = new NewService();
        service.setId(serviceName + "-" + ip + ":" + port);
        service.setName(serviceName);
        service.setAddress(ip);
        service.setPort(port);
        // 序列化GPU元數(shù)據(jù)
        Gson gson = new Gson();
        service.getMeta().put("gpus", gson.toJson(gpus));
        consul.agentServiceRegister(service);
    }
}

2. Nacos注冊(cè)實(shí)現(xiàn)

import com.alibaba.nacos.api.naming.NamingFactory;
import com.alibaba.nacos.api.naming.NamingService;
import com.alibaba.nacos.api.naming.pojo.Instance;
public class NacosRegistrar {
    public void register(String serviceName, String ip, int port) throws Exception {
        NamingService naming = NamingFactory.createNamingService("localhost:8848");
        List<GpuMeta> gpus = GpuInfoUtil.getGpuMetadata();
        Instance instance = new Instance();
        instance.setIp(ip);
        instance.setPort(port);
        instance.setServiceName(serviceName);
        instance.getMetadata().put("gpus", new Gson().toJson(gpus));
        naming.registerInstance(serviceName, instance);
    }
}

步驟三：動(dòng)態(tài)更新元數(shù)據(jù)

import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.TimeUnit;
public class MetadataUpdater {
    private ScheduledExecutorService scheduler = Executors.newSingleThreadScheduledExecutor();
    private ConsulClient consulClient;
    private String serviceId;
    public void startUpdating() {
        scheduler.scheduleAtFixedRate(() -> {
            try {
                List<GpuMeta> gpus = GpuInfoUtil.getGpuMetadata();
                String gpuJson = new Gson().toJson(gpus);
                // 重新注冊(cè)以更新元數(shù)據(jù)
                NewService service = new NewService();
                service.setId(serviceId);
                service.setMeta(Collections.singletonMap("gpus", gpuJson));
                consulClient.agentServiceRegister(service);
            } catch (Exception e) {
                e.printStackTrace();
            }
        }, 0, 10, TimeUnit.SECONDS);
    }
}

步驟四：客戶端負(fù)載均衡（Spring Cloud示例）

1. 自定義負(fù)載均衡器

import org.springframework.cloud.client.ServiceInstance;
import org.springframework.cloud.loadbalancer.core.ServiceInstanceListSupplier;
import reactor.core.publisher.Flux;
public class GpuAwareServiceSupplier implements ServiceInstanceListSupplier {
    private final ServiceInstanceListSupplier delegate;
    private final Gson gson = new Gson();
    public GpuAwareServiceSupplier(ServiceInstanceListSupplier delegate) {
        this.delegate = delegate;
    }
    @Override
    public Flux<List<ServiceInstance>> get() {
        return delegate.get().map(instances -> 
            instances.stream()
                .filter(instance -> {
                    String gpuJson = instance.getMetadata().get("gpus");
                    List<GpuMeta> gpus = gson.fromJson(gpuJson, new TypeToken<List<GpuMeta>>(){}.getType());
                    return gpus.stream().anyMatch(g -> g.getFreeMem() > 2 * 1024 * 1024 * 1024L); // 2GB
                })
                .collect(Collectors.toList())
        );
    }
}

2. 配置負(fù)載均衡策略

@Configuration
public class LoadBalancerConfig {
    @Bean
    public ServiceInstanceListSupplier discoveryClientSupplier(
        ConfigurableApplicationContext context) {
        return ServiceInstanceListSupplier.builder()
                .withDiscoveryClient()
                .withCaching()
                .withHealthChecks()
                .withBlockingDiscoveryClient()
                .build(context);
    }
}

最終驗(yàn)證

檢查注冊(cè)中心元數(shù)據(jù)

curl http://localhost:8500/v1/catalog/service/my-service | jq .

輸出應(yīng)包含類似：

{
  "ServiceMeta": {
    "gpus": "[{\"name\":\"Tesla T4\",\"totalMem\":17179869184,\"freeMem\":8589934592}]"
  }
}

客戶端調(diào)用驗(yàn)證
客戶端會(huì)自動(dòng)選擇顯存充足的節(jié)點(diǎn)，日志輸出示例：

INFO Selected instance 192.168.1.101:8080 with 8GB free GPU memory

通過以上步驟，即可在Java中實(shí)現(xiàn)基于GPU元數(shù)據(jù)的服務(wù)注冊(cè)與負(fù)載均衡。

到此這篇關(guān)于Java實(shí)現(xiàn)Consul/Nacos根據(jù)GPU型號(hào)、顯存余量執(zhí)行負(fù)載均衡的文章就介紹到這了,更多相關(guān)Java負(fù)載均衡內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

您可能感興趣的文章: