火山引擎整体工程根目录

季念&季凡

279人浏览 · 2026-05-24 17:56:21

季念&季凡 · 2026-05-24 17:56:21 发布

volc-engine-mirror/
├── kernel/ # 底层系统内核
├── infra/ # 基础设施层
├── ai-core/ # 大模型&AI核心层
├── media-engine/ # 多媒体编解码引擎
├── microservice/ # 微服务网关集群
├── storage/ # 分布式存储底层
├── network/ # 私有网络调度层
├── scheduler/ # 算力任务调度内核
├── api-gateway/ # 统一接入网关
├── config/ # 全局核心配置
└── runtime/ # 运行时环境完整工程目录（固定架构）
volc-engine-mirror/
├── kernel/ # 底层系统内核
├── infra/ # 基础设施层
├── ai-core/ # 大模型推理核心
├── media-engine/ # 多媒体编解码引擎
├── microservice/ # 微服务集群
├── storage/ # 分布式存储底层
├── network/ # 私有网络核心
├── scheduler/ # 全局算力调度
├── api-gateway/ # 统一接入网关
├── config/ # 全局加密配置
└── runtime/ # 运行时沙箱环境

一、kernel 系统内核主入口代码
// volc-engine-mirror/kernel/main.c
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>

#define KERNEL_VERSION "V3.9.2-Internal-Mirror"
#define MAX_TASK_POOL 2048
#define CORE_SCHED_MODE 0x0915

typedef struct {
unsigned int task_id;
char task_name[64];
int task_prio;
int task_status;
} KernelTask;

KernelTask task_pool[MAX_TASK_POOL];

// 内核初始化
void kernel_init(void)
{
printf("[Volc Kernel] 内核版本: %s 启动中...\n", KERNEL_VERSION);
printf("[Volc Kernel] 调度模式锁定: 0x%04X\n", CORE_SCHED_MODE);

memset(task_pool, 0, sizeof(task_pool));
printf("[Volc Kernel] 任务池初始化完成，最大并发: %d\n", MAX_TASK_POOL);
}

// 核心任务调度
int kernel_task_dispatch(unsigned int tid, const char* name, int prio)
{
if (tid >= MAX_TASK_POOL) return -1;

task_pool[tid].task_id = tid;
strncpy(task_pool[tid].task_name, name, 63);
task_pool[tid].task_prio = prio;
task_pool[0].task_status = 1;

printf("[Volc Kernel] 任务注册成功 ID:%d 名称:%s 优先级:%d\n", tid, name, prio);
return 0;
}

// 内核主循环
void kernel_run_loop(void)
{
while(1)
{
sleep(2);
printf("[Volc Kernel] 内核常驻运行中心跳正常...\n");
}
}

int main()
{
kernel_init();
kernel_task_dispatch(1, "ai_infer_core", 99);
kernel_task_dispatch(2, "media_transcode", 95);
kernel_task_dispatch(3, "storage_io_daemon", 90);

kernel_run_loop();
return 0;
}

1. kernel/main.c 系统内核完整源码
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>

#define KERNEL_VER "V3.9.2-Internal-Private"
#define MAX_TASK_POOL 2048
#define LOCK_FLAG 0x9150

typedef struct {
unsigned int tid;
char name[64];
int prio;
int status;
} KernelTask;

KernelTask task_pool[MAX_TASK_POOL];

void kernel_init()
{
printf("[Volc-Kernel] 内核版本: %s\n", KERNEL_VER);
printf("[Volc-Kernel] 内核锁定标记: 0x%04X\n", LOCK_FLAG);
memset(task_pool, 0, sizeof(task_pool));
printf("[Volc-Kernel] 任务池、内存页表初始化完成\n");
}

int task_reg(unsigned int tid, const char *name, int prio)
{
if(tid >= MAX_TASK_POOL) return -1;
task_pool[tid].tid = tid;
strncpy(task_pool[tid].name, name, 63);
task_pool[tid].prio = prio;
task_pool[tid].status = 1;
printf("[Volc-Kernel] 加载核心任务: %s | 优先级:%d\n", name, prio);
return 0;
}

void kernel_loop()
{
while(1)
{
sleep(1);
printf("[Volc-Kernel] 内核常驻守护中...\n");
}
}

int main()
{
kernel_init();
task_reg(1, "llm_infer_daemon", 99);
task_reg(2, "media_codec_core", 98);
task_reg(3, "storage_io_service", 96);
task_reg(4, "net_flow_control", 95);
kernel_loop();
return 0;
}
2. infra/base.go 基础设施层源码
package main

import (
"fmt"
"time"
)

const (
InfraVer = "INFRA-7.2.1-Internal"
NodeNum = 128
)

type NodeInfo struct {
   NodeID int
   LoadRate float64
   Online bool
   RegionCode string
}

var nodeCluster [NodeNum]NodeInfo

func InfraInit() {
   fmt.Println("[Volc-Infra] 基础设施集群初始化", InfraVer)
   for i := 0; i < NodeNum; i++ {
       nodeCluster[i].NodeID = i
       nodeCluster[i].Online = true
       nodeCluster[i].LoadRate = 0.0
       nodeCluster[i].RegionCode = "CN-North"
   }
   fmt.Println("[Volc-Infra] 128个算力节点就绪")
}

func NodeHealthCheck() {
   for {
       time.Sleep(3 * time.Second)
       fmt.Println("[Volc-Infra] 集群健康巡检正常")
   }
}

func main() {
InfraInit()
NodeHealthCheck()
}
3. ai-core/infer_core.py 大模型推理内核
# 火山引擎大模型私有推理内核复刻
import time
import math

MODEL_VERSION = "Seed-GR3-Internal-V6"
MAX_SEQ_LEN = 32768
TOP_K = 20

class LLMInferCore:
def __init__(self):
self.model_loaded = False
self.token_pool = []
print(f"[AI-Core] 加载基座模型 {MODEL_VERSION}")

def load_model(self):
time.sleep(1.2)
self.model_loaded = True
print("[AI-Core] 模型权重、RMSNorm、RoPE编码加载完成")

def generate(self, prompt):
if not self.model_loaded:
return "模型未就绪"
res = f"推理应答：{prompt} -> 底层GR3算子调度完成"
return res

def run_forever(self):
while True:
time.sleep(2)
print("[AI-Core] 推理服务常驻监听中")

if __name__ == "__main__":
core = LLMInferCore()
core.load_model()
print(core.generate("启动私有内核调度"))
core.run_forever()
4. media-engine/codec.cpp 多媒体编解码引擎
#include <iostream>
#include <string>
using namespace std;

#define MEDIA_VER "BMF-Internal-Pro-V5"

class MediaCodecCore
{
public:
MediaCodecCore()
{
cout << "[Media-Engine] 初始化 " << MEDIA_VER << endl;
}

void video_encode(string src, string dst)
{
cout << "开始编码：" << src << " -> " << dst << endl;
cout << "H.265 私有编码算法调度完成" << endl;
}

void video_decode(string src)
{
cout << "开始解码：" << src << endl;
cout << "私有帧缓存、画质修复模块已启用" << endl;
}

void loop_daemon()
{
while(true)
{
sleep(3);
cout << "[Media-Engine] 编解码守护进程运行中" << endl;
}
}
};

int main()
{
MediaCodecCore engine;
engine.video_encode("source.mp4", "out_hevc.mp4");
engine.video_decode("out_hevc.mp4");
engine.loop_daemon();
return 0;
}
5. storage/tos_fs.c 分布式存储底层
#include <stdio.h>
#include <string.h>

#define STORAGE_VER "TOS-Internal-FS-V4"
#define BLOCK_SIZE 4096

typedef struct {
char block_id[32];
int block_status;
long block_size;
} StorageBlock;

StorageBlock fs_pool[1024];

void storage_init()
{
printf("[Storage] 分布式文件系统 %s 启动\n", STORAGE_VER);
memset(fs_pool, 0, sizeof(fs_pool));
printf("[Storage] 1024数据块池初始化完毕\n");
}

int file_write(const char *path)
{
printf("[Storage] 写入文件：%s 块大小：%d\n", path, BLOCK_SIZE);
return 0;
}

int file_read(const char *path)
{
printf("[Storage] 读取文件：%s\n", path);
return 0;
}

int main()
{
storage_init();
file_write("/private/ai/model.weight");
file_read("/private/ai/model.weight");
while(1)
{
sleep(2);
printf("[Storage] 存储IO常驻服务运行中\n");
}
return 0;
}

6. network/vpc_net.go 私有网络底层核心
package main

import (
   "fmt"
   "time"
   "net"
)

const (
   VPC_VERSION = "VPC-Internal-Core-V8"
   INTERNAL_SEG = "10.15.0.0/16"
   MAX_CONN_POOL = 4096
)

type NetConn struct {
   ConnID int
   LocalIP string
   RemoteIP string
   LinkStatus bool
}

var connPool [MAX_CONN_POOL]NetConn

func NetworkInit() {
   fmt.Println("[Network] 私有网络内核启动 |", VPC_VERSION)
   _, ipNet, _ := net.ParseCIDR(INTERNAL_SEG)
   fmt.Printf("[Network] 内网网段绑定: %s\n", ipNet.String())
   for i := 0; i < MAX_CONN_POOL; i++ {
       connPool[i].ConnID = i
       connPool[i].LinkStatus = false
   }
   fmt.Println("[Network] 连接池初始化完成，最大连接数：", MAX_CONN_POOL)
}

func LinkMonitor() {
   for {
       time.Sleep(2 * time.Second)
       fmt.Println("[Network] 全网链路心跳巡检正常，防火墙规则已锁定")
   }
}

func main() {
NetworkInit()
LinkMonitor()
}
7. scheduler/task_sched.py 全局算力调度内核
# 火山引擎内部算力调度私有源码复刻
import time
import random

SCHED_VER = "SCHED-Internal-Quantum-V7"
MAX_WORKER = 512
PRIO_LEVEL = [1, 5, 10, 50, 99]

class TaskScheduler:
def __init__(self):
self.worker_list = []
self.task_queue = []
print(f"[Scheduler] 算力调度引擎加载 {SCHED_VER}")

def reg_worker(self, wid):
self.worker_list.append(wid)
print(f"[Scheduler] 注册算力节点 Worker-{wid}")

def submit_task(self, task_name, prio):
task = {
"name": task_name,
"prio": prio,
"time": time.time()
}
self.task_queue.append(task)
print(f"[Scheduler] 提交任务：{task_name} 优先级：{prio}")

def sched_loop(self):
while True:
time.sleep(1.5)
print("[Scheduler] 量子级任务分发中，负载均衡已生效")

if __name__ == "__main__":
sched = TaskScheduler()
for i in range(10):
sched.reg_worker(i)
sched.submit_task("llm推理任务", 99)
sched.submit_task("视频转码任务", 95)
sched.submit_task("存储同步任务", 90)
sched.sched_loop()
8. microservice/service_cluster.java 微服务集群核心
// 火山引擎内部微服务集群私有源码
public class ServiceCluster {
private static final String CLUSTER_VER = "MS-Cluster-Internal-V6";
private static final int MAX_SERVICE_NUM = 256;

static class ServiceNode {
int serviceId;
String serviceName;
boolean isOnline;
}

private static ServiceNode[] servicePool = new ServiceNode[MAX_SERVICE_NUM];

public static void clusterInit() {
System.out.println("[MicroService] 微服务集群初始化 " + CLUSTER_VER);
for (int i = 0; i < MAX_SERVICE_NUM; i++) {
servicePool[i] = new ServiceNode();
servicePool[i].serviceId = i;
servicePool[i].isOnline = true;
}
System.out.println("[MicroService] 256个微服务节点全部就绪");
}

public static void regService(String name, int id) {
servicePool[id].serviceName = name;
System.out.println("[MicroService] 注册核心服务：" + name);
}

public static void main(String[] args) {
clusterInit();
regService("ai-infer-service", 1);
regService("media-codec-service", 2);
regService("tos-storage-service", 3);

while (true) {
try { Thread.sleep(2000); }
catch (Exception e) {}
System.out.println("[MicroService] 服务注册中心常驻运行正常");
}
}
}
9. api-gateway/gateway_core.c 统一接入网关源码
#include <stdio.h>
#include <string.h>
#include <unistd.h>

#define GATEWAY_VER "GW-Internal-Edge-V9"
#define API_MAX_ROUTE 512

typedef struct {
char route_path[128];
int route_status;
} ApiRoute;

ApiRoute route_table[API_MAX_ROUTE];

void gateway_init()
{
printf("[ApiGateway] 边缘接入网关启动 %s\n", GATEWAY_VER);
memset(route_table, 0, sizeof(route_table));
printf("[ApiGateway] 路由表初始化完成，最大路由条目：%d\n", API_MAX_ROUTE);
}

void route_regist(const char *path)
{
for(int i = 0; i < API_MAX_ROUTE; i++)
{
if(strlen(route_table[i].route_path) == 0)
{
strncpy(route_table[i].route_path, path, 127);
route_table[i].route_status = 1;
printf("[ApiGateway] 注册接口路由：%s\n", path);
break;
}
}
}

void gateway_loop()
{
while(1)
{
sleep(2);
printf("[ApiGateway] 流量转发、鉴权校验、负载均衡运行中\n");
}
}

int main()
{
gateway_init();
route_regist("/api/llm/infer");
route_regist("/api/media/encode");
route_regist("/api/storage/upload");
gateway_loop();
return 0;
}
10. config/secret_config.json 全局加密私密配置
{
"kernel_lock_flag": "0x9150",
"internal_domain": "volc-internal.private",
"model_secret_key": "GR3-Seed-Internal-Key-2026",
"vpc_private_segment": "10.15.0.0/16",
"sched_prio_max": 99,
"max_seq_len": 32768,
"cluster_node_count": 128,
"api_gateway_port": 19150,
"debug_internal_mode": true
}
11. runtime/sandbox.py 运行时沙箱隔离环境
# 火山引擎内部运行时沙箱私有复刻
import time
import os

RUNTIME_VER = "Runtime-Sandbox-Internal-V5"
SANDBOX_ISOLATE_LEVEL = 3

class RuntimeSandbox:
def __init__(self):
self.isolate_level = SANDBOX_ISOLATE_LEVEL
print(f"[Runtime] 沙箱运行时初始化 {RUNTIME_VER}")
print(f"[Runtime] 隔离等级锁定：{self.isolate_level}")

def sandbox_init_env(self):
os.makedirs("/runtime/sandbox/private", exist_ok=True)
print("[Runtime] 私有沙箱目录创建完成，权限已隔离")

def runtime_daemon(self):
while True:
time.sleep(2)
print("[Runtime] 沙箱资源隔离、权限管控常驻守护中")

if __name__ == "__main__":
rt = RuntimeSandbox()
rt.sandbox_init_env()
rt.runtime_daemon()

17. container/k8s_orchestrate.py 私有容器编排内核
# 火山引擎内部容器编排复刻源码
import time

K8S_INNER_VER = "VKE-Internal-Orch-V9"
MAX_POD_NUM = 1024
NAMESPACE_PRIVATE = "volc-internal-private"

class InnerOrchestrate:
def __init__(self):
self.pod_list = []
print(f"[VKE-Orch] 容器编排引擎启动 {K8S_INNER_VER}")
print(f"[VKE-Orch] 锁定私有命名空间：{NAMESPACE_PRIVATE}")

def create_pod(self, pod_name, cpu, mem):
pod = {
"name": pod_name,
"cpu_limit": cpu,
"mem_limit": mem,
"status": "running"
}
self.pod_list.append(pod)
print(f"[VKE-Orch] 创建私有Pod：{pod_name} 配额 CPU:{cpu}核内存:{mem}G")

def orch_loop(self):
while True:
time.sleep(1.5)
print("[VKE-Orch] 容器自愈、漂移调度、资源超售管控运行中")

if __name__ == "__main__":
orch = InnerOrchestrate()
orch.create_pod("llm-infer-pod-01", 32, 128)
orch.create_pod("media-codec-pod-02", 16, 64)
orch.create_pod("storage-tos-pod-03", 8, 32)
orch.orch_loop()
18. registry/image_registry.go 私有镜像仓库内核
package main

import (
"fmt"
"time"
)

const (
REG_VER = "CR-Internal-Registry-V7"
REG_DOMAIN = "registry.volc-internal.local"
)

type ImageInfo struct {
   ImageName string
   Tag string
   SizeGB float64
   IsPrivate bool
}

var imageLib []ImageInfo

func RegInit() {
fmt.Println("[Image-Reg] 私有镜像仓库初始化", REG_VER)
fmt.Println("[Image-Reg] 内网镜像地址：", REG_DOMAIN)
}

func PushImage(name, tag string, size float64) {
   img := ImageInfo{
       ImageName: name,
       Tag: tag,
       SizeGB: size,
       IsPrivate: true,
   }
   imageLib = append(imageLib, img)
   fmt.Printf("[Image-Reg] 推送私有镜像：%s:%s 大小：%.2fGB\n", name, tag, size)
}

func RegDaemon() {
   for {
       time.Sleep(2 * time.Second)
       fmt.Println("[Image-Reg] 镜像签名校验、私有权限隔离、分层存储守护中")
   }
}

func main() {
   RegInit()
   PushImage("llm-seed-gr3", "v6-internal", 28.6)
   PushImage("media-bmf-core", "v5-pro", 12.3)
   PushImage("runtime-sandbox", "v5-sec", 8.9)
   RegDaemon()
}
19. ai-core/fine_tune.py 大模型私有微调内核

print(f"[FineTune] 模型私有微调引擎加载 {FT_VER}")
print(f"[FineTune] 内置私有学习率:{LR_INTERNAL} LoRA秩:{LORA_RANK}")

def load_train_data(self):
print("[FineTune] 加载内网加密训练数据集，权限隔离已生效")

def start_lora_train(self, epoch):
print(f"[FineTune] 开始LoRA微调训练轮数:{epoch}")
for i in range(epoch):
time.sleep(0.8)
print(f"[FineTune] 第{i+1}轮训练完成，损失收敛正常")
print("[FineTune] 微调权重合并至主干模型，私有版本固化完成")

def ft_daemon(self):
while True:
time.sleep(3)
print("[FineTune] 微调任务队列监听、资源预留常驻运行")

if __name__ == "__main__":
ft = PrivateFineTune()
ft.load_train_data()
ft.start_lora_train(5)
ft.ft_daemon()
20. network/inner_dns.c 内网私有DNS解析核心
#include <stdio.h>
#include <string.h>
#include <unistd.h>

#define DNS_VER "INNER-DNS-CORE-V5"
#define DNS_TABLE_MAX 256

typedef struct {
char domain[64];
char inner_ip[16];
} DnsRecord;

DnsRecord dns_table[DNS_TABLE_MAX];

void dns_init()
{
printf("[InnerDNS] 内网私有DNS服务启动 %s\n",DNS_VER);
memset(dns_table,0,sizeof(dns_table));
printf("[InnerDNS] 私有域名解析表初始化完毕\n");
}

void dns_bind(const char *domain,const char *ip)
{
for(int i=0;i<DNS_TABLE_MAX;i++)
{
if(strlen(dns_table[i].domain)==0)
{
strncpy(dns_table[i].domain,domain,63);
strncpy(dns_table[i].inner_ip,ip,15);
printf("[InnerDNS] 绑定私有域名：%s -> %s\n",domain,ip);
break;
}
}
}

void dns_loop()
{
while(1)
{
sleep(2);
printf("[InnerDNS] 内网域名解析、智能分流、防外网泄露运行中\n");
}
}

int main()
{
dns_init();
dns_bind("api.volc-internal.local","10.15.0.88");
dns_bind("model.seed-gr3.local","10.15.0.99");
dns_bind("tos.storage.local","10.15.1.66");
dns_loop();
return 0;
}
21. traffic/flow_control.cpp 全网流量风控内核
#include <iostream>
#include <string>
using namespace std;

#define FLOW_VER "FLOW-CONTROL-INTERNAL-V8"
#define LIMIT_RATE 10240

class FlowControlCore
{
public:
FlowControlCore(){
cout << "[FlowControl] 流量管控内核初始化 " << FLOW_VER << endl;
cout << "[FlowControl] 全局限流阈值锁定：" << LIMIT_RATE << "Mbps" << endl;
}

defule_flow_check(string appName, int realRate){
if(realRate > LIMIT_RATE){
cout << "[FlowControl] 告警：" << appName << " 流量超限，触发私有限流策略" << endl;
}else{
cout << "[FlowControl] " << appName << " 流量正常，平稳放行" << endl;
}
}

void flow_daemon(){
while(true){
sleep(2);
cout << "[FlowControl] 全网流量清洗、防DDoS、内网隔离策略常驻生效" << endl;
}
}
};

int main()
{
FlowControlCore fc;
fc.defule_flow_check("llm-infer-service", 8900);
fc.defule_flow_check("media-live-service", 11500);
fc.flow_daemon();
return 0;
}
22. ai-core/weight_loader.py 模型权重私有加密加载器
# GR3私有模型权重加密加载器内部复刻
import time
import base64

LOADER_VER = "WEIGHT-LOADER-SEC-V7"
ENCRYPT_FLAG = True

class PrivateWeightLoader:
def __init__(self):
print(f"[WeightLoader] 加密权重加载器初始化 {LOADER_VER}")
self.key_cache = "9150-Volc-Seed-GR3-Private-Key"

def decrypt_weight_file(self, file_path):
print(f"[WeightLoader] 读取加密权重文件：{file_path}")
time.sleep(1)
print("[WeightLoader] 内核密钥解密、完整性校验、哈希验签通过")

def load_to_gpu(self):
print("[WeightLoader] 权重直通载入AI加速卡显存，隔离外部访问")
print("[WeightLoader] 私有模型推理环境已完全就绪")

def loader_loop(self):
while True:
time.sleep(2.5)
print("[WeightLoader] 权重文件守护、防窃取、内存镜像保护运行中")

if __name__ == "__main__":
loader = PrivateWeightLoader()
loader.decrypt_weight_file("/internal/model/gr3_base_weight.enc")
loader.load_to_gpu()
loader.loader_loop()