LHC Tunnel

LHC Tunnel

Thursday, 21 April 2016

Containers and the CERN cloud

In recent years, different groups at CERN started looking at using containers for different purposes, covering infrastructure services but also end user applications. These efforts have been mostly done independently, resulting in a lot of repeated work especially for the parts which are CERN specific: integration with the identity service, networking and storage systems. In many cases, the projects could not complete before reaching a usable state, as some of these tasks require significant expertise and time to be done right. Alternatively, they found different solutions to the same problem which led to further complexity for the supporting infrastructure services. However, the use cases were real, and a lot of knowledge had been built on the available tools and their capabilities.

Based on this, we started a project with the following goals:
  • integrate containers into the CERN OpenStack cloud, building on top of already available tools such as resource lifecycle, quotas, identity and authorization
  • stay container orchestration agnostic, allowing users to select any of the most common solutions (Docker Swarm, Kubernetes, Mesos)
  • allow fast cluster deployment and rebuild
We had done a prototype using the Nova LXC driver in the past but the long term support was not clear and we wanted access to the native container functions using the standard tools.

Looking for other possibilities, OpenStack Magnum seemed to be offering a lot of what we needed, and we decided to try it out. At around the same time we were also heading to the OpenStack Tokyo summit, which was a great opportunity to follow the Magnum sessions and learn more of what it provides.

Magnum relies heavily on Heat for the orchestration part of the container clusters - called bays. Bays are instantiated based on pre-defined bay models, which set how the master and the other nodes should look like (flavor, image, etc) and which container orchestration engine (COE) should be used - among other possible configuration options. Current choices include Docker Swarm, Kubernetes and Mesos. The Magnum homepage gives a lot more details.

At the beginning of November 2015, we started investigating the Magnum project in depth. At that time the project was functional but some of its requirements posed problems in our deployment:
  • Dependency on OpenStack Neutron, something we had not yet deployed (we have nova-network since we started the cloud in 2012). Luckily we were working on it in parallel, and we got a functional control plane just in time. And as we use Nova Cells, we could enable Neutron in a dedicated cell where we would also enable Magnum, reusing the rest of the production infrastructure
  • Requirement on Neutron LBaaS, which we don't have. This is something we plan to try, but it is not obvious how to implement this currently due to the way the CERN network is structured. We made some changes to the Heat templates to remove this requirement
The other pre-requisite projects, such as Keystone, Glance and Heat were already in production in the CERN cloud.

But no real show stoppers and very quickly we got a prototype deployment. For a more detailed evaluation we initially chose 3 internal projects that cover the most common use cases:
  • GitLab CI, a continuous integration service we use internally - it has integration with Docker, making it a perfect example of how to use a Docker Swarm cluster as a drop in replacement for a local Docker daemon
  • Infrastructure services, namely one of the critical services for the data movement between the multiple sites of the LHC Computing Grid (WLCG) - for a nice example of scaling a service by scaling its individual components
  • Jupyter Notebooks - a growing trend for end user analysis in different scientific communities, providing a browser based interactive session running in a remote container
In addition, we are also working with the European Union Horizon 2020 project Indigo Datacloud which is developing an open source data and computing platform targeted at scientific communities, deployable on multiple hardware and provisioned over hybrid, private or public, e-infrastructures. Using Magnum, we can provide the test resources for this project to the partners.

For our users and resource managers, there are significant advantages of the Magnum approach:
  • Native tools - anything that works with Docker will work talking to a Docker Swarm COE or kubectl with a Kubernetes cluster. This allows smaller physics sites to provide native Docker or Kubernetes while the larger sites provide Containers-as-a-Service on-demand. User applications written to work with Docker or Kubernetes can be used without modification against the provisioned resources.
  • Container engine agnostic - with our user community, there is a strong need for flexibility to allow different avenues to be explored. Magnum allows the IT department to offer Kubernetes, Docker Swarm and Mesos at a low cost within the same service at an affordable load for the support team. The users can then prototype different application approaches and select the best combination for them. Enforcing a central IT service decision on the end user community is never easy, especially where there are diverse user requirements being covered within a central cloud.
  • Accounting, quota and permissions remains within the existing framework. Thus, whether resources are used for containers or VMs is a choice for the project user. Capacity planning can be done by cores/RAM rather than segmentation of resources for container or VM resources. Access controls follow the existing admin/member structures for projects. 
  • Elasticity - within the quota limits, containers can scale, with new bays as needed within the quota. This allows the resources to be allocated where there is a user need (and as importantly, shrunk when things are quiet)
  • Repairs - failures in the infrastructure (software or hardware) are looked after by the cloud support team. For the user, the workloads can be scheduled elsewhere. For the hardware repair teams, the operations can be performed in a consistent fashion in bulk rather than on a one-by-one basis. Infrastructure monitoring procedures are the same for VMs and containers.
  • The operating system support teams can provide reference images and follow up issues with the upstream providers. They can be confident that the image is based on supported configurations rather than ad-hoc builds. Rebuilding base images with the appropriate security patches can sometimes be delayed, raising the risk of incidents.
By the end of March, we had the use cases covered, and the few hick-ups covered in blueprints or patches upstream, and had contributed for the missing bits in puppet and documentation. And with a service running on our production resources and thanks to keystone endpoint filtering, we could increase service usage by enabling it for individual projects. Today we have around 15 different projects using Magnum as a pilot service and the number keeps growing.

In just a few months, we got Magnum up and running and it has proved to be a significant addition to the OpenStack cloud. Which makes us excited about what is coming next, including:
  • Integration with Cinder - ready upstream, and we'll be trying it very soon
  • Magnum benchmarks in Rally - we rely on Rally to make sure our cloud is performing as expected
  • Further integration with our local storage systems such as CVMFS and EOS - relying on the ability to add site specific configurations to the bay templates
  • Integration with Barbican - the recommended way to handle the required TLS certificates to talk to the native APIs of the orchestration engines, and the only option today to get Magnum in HA (though that's about to change)
  • Integration with Horizon - this will help as we expand the service into production to communities who are used to using the web interfaces
If you're interested in more details on the available container orchestration technologies or our usage of OpenStack Magnum, or simply want to see some fancy demos, check our recent presentation at a CERN Technical Forum.


  • Mathieu Velten for his work on testing and adapting Magnum at CERN and contributions to Indigo DataCloud
  • Bertrand Noel for all his time spent researching existing container technologies
  • Spyros Trigazis, a fellow in the CERN OpenLab collaboration with Rackspace, for all his work upstream both for features and documentation improvements
  • Jarek Polok for the CERN docker repository
  • The OpenStack Magnum team for their support and collaboration
  • All CERN users that helped us debug and set the service requirements



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