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Lab: Control Groups (cgroups)

Difficulty: Intermediate

Time: Approximately 15 minutes

Control Groups (cgroups) are a feature of the Linux kernel that allow you to limit the access processes and containers have to system resources such as CPU, RAM, IOPS and network.

In this lab you will use cgroups to limit the resources available to Docker containers. You will see how to pin a container to specific CPU cores, limit the number of CPU shares a container has, as well as how to prevent a fork bomb from taking down a Docker Host.

You will complete the following steps as part of this lab.

Prerequisites

You will need all of the following to complete this lab:

  • A Linux-based Docker Host with at least two CPU cores
  • Docker 1.11 or higher
  • The htop tool installed on your Docker Host (apt-get install htop on Ubuntu hosts)
  • Root access on the Docker Host

Step 1: cgroups and the Docker CLI

The docker run command provides many flags that allow you to apply cgroup limitations to new containers. The following flags are of note for this lab:

$ docker run --help
...
--cpu-shares                    CPU shares (relative weight)
...
--cpuset-cpus                   CPUs in which to allow execution (0-3, 0,1)
...
--pids-limit                    Tune container pids limit (set -1 for unlimited)

For more information on cgroup related flags available to the docker run command, see the docker run reference guide.

Step 2: Max-out two CPUs

In this step you'll start a new container that will max out two CPU cores. You will need htop installed on your Docker Host, and you will need to be running a Docker Host that has two or more CPU cores. This step will still work if your Docker Host only has a single core. However, some of the htop outputs will be slightly different.

  1. If you haven't already done so, install htop on your Docker Host.

Ubuntu with apt.

$ sudo apt-get install htop

CentOS with yum.

$ sudo yum install htop

  1. Clone the lab's GitHub repo locally on your Docker Host and change into the cgroups/cpu-stress directory.

    $ sudo git clone https://github.com/docker/labs.git
Cloning into 'labs'...
remote: Counting objects: 1638, done.
Receiving objects: 100% (1638/1638), 8.84 MiB | 2.37 MiB/s, done.
Resolving deltas:  22% (33/148)   d 0 (delta 0), pack-reused 1638
Resolving deltas: 100% (148/148), done.
Checking connectivity... done.
Checking out files: 100% (1389/1389), done.

$ cd labs/security/cgroups/cpu-stress

  2. Verify that the directory has a single Dockerfile and a single docker-compose.yml file.

    $ ls -l
-rw-r--r-- 1 root root 23 Jul 11 09:49 docker-compose.yml
-rw-r--r-- 1 root root 85 Jul 11 09:49 Dockerfile

  3. Inspect the contents of the Dockerfile.

    $ cat Dockerfile
FROM ubuntu:latest

RUN apt-get update && apt-get install -y stress

CMD stress -c 2

As you can see, the Dockerfile describes a simple Ubuntu-based container that runs a single command stress -c 2. This command spawns two processes - both spinning on sqrt(). The effect of this command is to stress two CPU cores.

  1. Build the image specified in the Dockerfile.

    $ sudo docker build -t cpu-stress .
Sending build context to Docker daemon 3.072 kB
Step 1 : FROM ubuntu:latest
latest: Pulling from library/ubuntu
90d6565b970a: Pull complete
40553bdb8474: Pull complete
<snip>
Step 3 : CMD stress -c 2
 ---> Running in b90defcccbb8
 ---> 9e6a3f316e91
Removing intermediate container b90defcccbb8
Successfully built 9e6a3f316e91

  2. Run a new container called stresser based on the image built in the previous step.

    $ sudo docker run -d --name stresser cpu-stress
stress: info: [5] dispatching hogs: 2 cpu, 0 io, 0 vm, 0 hdd

    Be sure to run the container in the background with the -d flag so that you can run the htop command in the next step from the same terminal.

  3. View the impact of the container using the htop command.

The output above shows two stress processes (stress -c 2) maxing out two of the CPUs on the system (CPU 1 and CPU 4). Both stress processes are in the running state, and both consuming 100% of the CPU they are executing on.

  1. Stop and remove the stresser container.

    $ sudo docker stop stresser && sudo docker rm stresser

stresser
stresser

You have seen how it is possible for a single container to max out CPU resources on a Docker Host. You would max out your entire Docker Host if you were to start a stress worker processes for each CPU core.

Step 3: Set CPU affinity

Docker makes it possible to restrict containers to a particular CPU core, or set of CPU cores. In this step you'll see how to restrict a container to a single CPU core using docker run with the --cpuset-cpus flag.

  1. Run a new Docker container called stresser and restrict it to running on the first CPU on the system.

    $ sudo docker run -d --name stresser --cpuset-cpus 0 cpu-stress

0bfbf2d33516065bbcfa56bd8f9df24749312460141bca729f53d66a9b2dba6b

The --cpuset-cpus flag indexes CPU cores starting at 0. Therefore, CPU 0 is the first CPU on the system. You can specify multiple CPU cores as 0-4, 0,3 etc.

  1. Run the htop command to see the impact the container is having on the Docker Host.

There are a few things worth noting about what you have just done:

  • The container is based on the same cpu-stress image that spawns two stress worker processes.
  • The two stress worker processes have been restricted to running against a single CPU core by the --cpuset-cpus flag.
  • Each of the two stress processes is consuming ~50% of available time on the single CPU core they are executing on.
  • htop indexes CPU cores starting at 1 whereas --cpuset-cpus indexes starting at 0.

You have seen how to lock a container to a executing on a single CPU core on the Docker Host. Feel free to experiment further with the --cpuset-cpus flag.

Step 4: Set CPU share constraints

By default, all containers get an equal share of time executing on the Docker Host's CPUs. This allocation of time can be modified by changing the container’s CPU share weighting relative to the weighting of all other running containers.

The --cpu-shares flag takes a value from 0-1024. The default value is 1024, and a value of 0 will also default to 1024. If three containers are running and one has 1024 shares, while the other two have 512, the first container will get 50% of processor time while the other two will each get 25%. However, these shares are only enforced when CPU intensive tasks are running. When a container is not busy, other containers can use the free CPU time.

Shares of CPU time are balanced across all cores on a multi-core Docker Host.

In this step you will use the docker run command with the --cpu-shares flag to influence the amount of CPU time containers get. You will start one container (container-1) with 768 shares, and another container (container-2) with 256 shares. Each container will be locked to the first CPU on the system, and each will be running the stress -c 2 process. Container-1 will receive 75% of the CPU time whereas container-2 will receive 25%.

  1. Start the first container with 768 CPU shares.

    $ sudo docker run -d --name container-1 --cpuset-cpus 0 --cpu-shares 768 cpu-stress

  2. Start the second container with 256 CPU shares.

    $ sudo docker run -d --name container-2 --cpuset-cpus 0 --cpu-shares 256 cpu-stress

  3. Verify that both containers are running with the docker ps command.

    $ sudo docker ps

CONTAINER ID        IMAGE               COMMAND                  CREATED             STATUS              PORTS               NAMES
725dc16fac5a        cpu-stress          "/bin/sh -c 'stress -"   2 minutes ago       Up 2 minutes                            container-2
f82f95757d3f        cpu-stress          "/bin/sh -c 'stress -"   2 minutes ago       Up 2 minutes                            container-1

  4. View the output of htop.

Notice two things about the htop output. First, only a single CPU is being maxed out. Second, there are four stress processes running. The first two in the list equate to ~75% of CPU time, and the second two equate to ~25% of CPU time.

In this step you've seen how to use the --cpu-shares flag to influence the amount of CPU time containers get relative to other containers on the same Docker Host.

Feel free to experiment further by running more containers with different relative weights.

Step 5: Docker Compose and cgroups

In this step you'll see how to set CPU affinities via Docker Compose files.

See this section of the Docker Compose documentation for more information on leveraging other cgroup capabilities using Docker Compose.

Make sure you are in the dockercon-workshop/cgroups/cpu-stress directory of the repo that you pulled in Step 2.

  1. Edit the docker-compose.yml file and add the cpuset: '3' line as shown below:

    cpu-stress:
  build: .
  cpuset: '3'

The above docker-compose.yml file will ensure that containers based from it will run on CPU core #3. You will obviously need a Docker Host with at least 4 CPU cores for this to work.

  1. Bring the application up in the background.

    $ sudo docker-compose up -d

Creating cpustress_cpu-stress_1

  2. Run htop to see the effect of the cpuset parameter.

The htop output above shows the container and it's two stress processes locked to CPU core 4 (cpuset in Docker Compose indexes CPU cores starting at 0 whereas htop indexes CPU cores starting at 1).

In this step you've seen how Docker Compose can set container CPU affinities. Remember that Docker Compose can also set CPU quotas and shares. See the documentation for more detail.

Step 6: Preventing a fork bomb

A fork bomb is a form of denial of service (DoS) attack where a process continually replicates itself with the goal of depleting system resources to the point where a system can no longer function.

In this step you will use the --pids-limit flag to limit the number of processes a container can fork at runtime. This will prevent a fork bomb from consuming the Docker Host's entire process table.

  1. Start a new container and limit the number of processes it can create to 200 with the following command.

    $ sudo docker run --rm -it --pids-limit 200 debian:jessie bash

Unable to find image 'debian:jessie' locally
jessie: Pulling from library/debian
5c90d4a2d1a8: Pull complete
Digest: sha256:8b1fc3a7a55c42e3445155b2f8f40c55de5f8bc8012992b26b570530c4bded9e
Status: Downloaded newer image for debian:jessie

root@c0eb76d2481c:/#

Do not proceed to the next step if you receive the following warning about pids limit support - "WARNING: Your kernel does not support pids limit capabilities, pids limit discarded". If you receive this warning, you can watch the demo as a video instead.

  1. Run the following command to create a fork bomb in the container you just started.

The previous command will have attached your terminal to the bash shell inside of the container created in the previous step.

Do not complete this step if you received the error that your kernel does not support the pids limit feature...

root@c0eb76d2481c:/# :(){ :|: & };:

[1] 6
root@a3eeb655f301:/# bash: fork: retry: No child processes
bash: fork: retry: No child processes
bash: fork: retry: No child processes
bash: fork: retry: No child processes
bash: fork: retry: No child processes
bash: fork: retry: Resource temporarily unavailable
bash: fork: retry: No child processes
<snip>
bash: fork: retry: No child processes
bash: fork: retry: Resource temporarily unavailable
bash: fork: retry: No child processes
^C
[1]+   Done                : | :

Note: The command above is :(){ :|: & };:.

You will need to press Ctrl-C to stop the process.

In this step you have seen how to set a process limits on a container that will prevent it from consuming all process table resources on the underlying Docker Host.

Summary

Congratulations. You've seen how to use some of the cgroups features supported by Docker that allow you to limit and constrain a container's access to Docker Host system resources.

Additional Resources

You can refer to the following resources for more information and help: