如何让Docker容器获得稳定一致的执行时间

我需要用Docker隔离特定进程，该进程在多核虚拟机上重复运行多次，每次执行以挂钟时间测量记录，目标是将执行时间差控制在200ms以内，但Docker环境下最优与最差执行时间差约1秒，达不到要求。

附图表说明：蓝色柱为原生执行时间（稳定性良好），橙色柱为Docker进程执行时间（波动大）。

最小复现示例 #

1. mem.cpp（内存密集型C++程序） #

#include <bits/stdc++.h>
#include <vector>

using namespace std;
string CustomString(int len)
{
    string result = "";
    for (int i = 0; i<len; i++)
        result = result + 'm';

    return result;
}
int main()
{
   int len = 320;
   std::vector< string > arr;
   for (int i = 0; i < 100000; i++) {
       string s = CustomString(len);
       arr.push_back(s);
   }
   cout<<arr[10] <<"\n";
   return 0;
}

2. script.sh（Docker容器启动脚本，编译并运行程序，记录挂钟时间） #

#!/bin/bash

# compile the file
g++ -O2 -std=c++17 -Wall -o _sol mem.cpp

# execute file and record execution time (wall clock)
ts=$(date +%s%N)
./_sol
echo $((($(date +%s%N) - $ts)/1000000)) ms

3. Python并行执行脚本（通过ProcessPoolExecutor启动多个Docker容器执行任务） #

import docker
import logging
import os
import tarfile
import tempfile
from concurrent.futures import ProcessPoolExecutor

log_format = '%(asctime)s %(threadName)s %(levelname)s: %(message)s'
dkr = docker.from_env()

def task():
    ctr = dkr.containers.create("gcc:12-bullseye", command="/home/script.sh", working_dir="/home")
    # copy files into container
    cp_to_container(ctr, "./mem.cpp", "/home/mem.cpp")
    cp_to_container(ctr, "./script.sh", "/home/script.sh")
    # run container and capture logs
    ctr.start()
    ec = ctr.wait()
    logs = ctr.logs().decode()
    ctr.stop()
    ctr.remove()
    # handle error
    if (code := ec['StatusCode']) != 0:
        logging.error(f"Error occurred during execution with exit code {code}")
    logging.info(logs)

def file_to_tar(src: str, fname: str):
    f = tempfile.NamedTemporaryFile()
    abs_src = os.path.abspath(src)
    with tarfile.open(fileobj=f, mode='w') as tar:
        tar.add(abs_src, arcname=fname, recursive=False)
    f.seek(0)
    return f

def cp_to_container(ctr, src: str, dst: str):
    (dir, fname) = os.path.split(os.path.abspath(dst))
    with file_to_tar(src, fname) as tar:
        ctr.put_archive(dir, tar)

if __name__ == "__main__":
    # set logging level
    logging.basicConfig(level=logging.INFO, format=log_format)
    # start ProcessPoolExecutor
    ppex = ProcessPoolExecutor(max_workers=max(os.cpu_count()-1,1))
    for _ in range(21):
        ppex.submit(task)

已尝试的优化措施 #

• 限制CPU核心数（8核虚拟机中使用4核及以下），执行时间波动无明显改善，推测问题可能出在Docker Engine层面。

补充发现 #

更换为新版gcc:13-bookworm镜像后，容器内执行时间比原生更优且稳定性大幅提升，推测原问题与镜像配置相关，希望彻底解决Docker容器内执行时间一致性问题。

内容的提问来源于stack exchange，提问作者Stefan Zhelyazkov