Java實現(xiàn)Consul/Nacos根據(jù)GPU型號、顯存余量執(zhí)行負載均衡的步驟詳解
Java實現(xiàn)Consul/Nacos根據(jù)GPU型號、顯存余量執(zhí)行負載均衡
步驟一:服務端獲取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;
// 構造方法、getters、setters省略
}
}步驟二:服務注冊到Consul/Nacos
1. Consul注冊實現(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注冊實現(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:客戶端負載均衡(Spring Cloud示例)
1. 自定義負載均衡器
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);
}
}最終驗證
檢查注冊中心元數(shù)據(jù)
curl http://localhost:8500/v1/catalog/service/my-service | jq .
輸出應包含類似:
{
"ServiceMeta": {
"gpus": "[{\"name\":\"Tesla T4\",\"totalMem\":17179869184,\"freeMem\":8589934592}]"
}
}客戶端調用驗證
客戶端會自動選擇顯存充足的節(jié)點,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中實現(xiàn)基于GPU元數(shù)據(jù)的服務注冊與負載均衡。
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