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README.md

OpenStack on OpenStack, or TripleO

Welcome to our TripleO incubator! TripleO is our pithy term for OpenStack deployed on and with OpenStack. This repository is our staging area, where we incubate new ideas and new tools which get us closer to our goal.

As an incubation area, we move tools to permanent homes in https://github.com/stackforge once they have proved that they do need to exist. Other times we will propose the tool for inclusion in an existing project (such as nova or devstack).

What is TripleO?

TripleO is the use of a self hosted OpenStack infrastructure - that is OpenStack bare metal (nova and cinder) + Heat + diskimage-builder + in-image orchestration such as Chef or Puppet - to install, maintain and upgrade itself.

This is combined with Continuous Integration / Continuous Deployment (CICD) of the environment to reduce the opportunity for failures.

Finally end user services such as Openstack compute virtual machine hosts, or Hadoop are deployed as tenants of the self hosted infrastructure. These can be deployed using any orchestration layer desired.

Current status

TripleO is a work in progress : we're building up the facilities needed to deliver the full story incrementally. Proof of concept implementations exist for all the discrete components - sufficient to prove the design, though (perhaps) not what will be used in production. We track bugs affecting TripleO itself at https://bugs.launchpad.net/tripleo/.

Diskimage-builder

The lowest layer in the dependency stack, diskimage-builder can be used to customise generic disk images for use with Nova bare metal. It can also be used to provide build-time specialisation for disk images. Diskimage-builder is quite mature.

Nova bare-metal

The next layer up, In OpenStack Grizzly Nova bare-metal is able to deliver ephemeral instances to physical machines with multiple architectures. By ephemeral instances, we mean that local storage is lost when a new image is deployed / the instance is rebuilt. So the machines operate in exactly the same fashion as if one installed a regular operating system instance on the machine. Nova depends on a partition image to copy into the machine, though the image can be totally generic.

Caveats / limitations:

Heat

Heat is the orchestration layer in TripleO - it glues the various services together in the cluster, arbitrates deployments and reconfiguration.

Heat is quite usable in Grizzly, though some additional features are planned to make the TripleO story easier and more robust. Heat depends on the Nova API to provision and remove instances in the cluster it is managing.

Caveats / limitations:

  • deployments/reconfigurations currently take effect immediately, rather than keeping a fraction of the cluster capacity unaffected. Workaround by defining multiple redundant groups to provide an artificial coordination point. A special case of this is HA pairs, where ideally Heat would know to take one side down, then the other.
  • deployments/reconfigurations only pay attention to the Nova API status rather than also coordinating with monitoring systems. Workaround by tying your monitoring back into Heat to trigger rollbacks.

os-config-applier/os-refresh-config

These tools work with the Heat delivered metadata to create configuration files on disk (os-config-applier), and to trigger in-instance reconfiguration including shutting down services and performing data migrations. These tools are new but very simple and very focused.

os-config-applier reads a JSON metadata file and generates templates. It can be used with any orchestration layer that generates a JSON metadata file on disk.

os-refresh-config subscribes to the Heat metadata we're using, and then invokes hooks - it can be used to drive os-config-applier, or Puppet or Chef or other configuration management tools.

tripleo-image-elements

These diskimage-builder elements create build-time specialised disk/partition images for TripleO. The elements build images with software installed but not configured - and hooks to configure the software with os-config-applier. Much of OpenStack is deployable via the elements that have been written but it is not yet setup for full HA.

Caveats/Limitations:

  • No support for image based updates yet. (Requires separating out updateable configuration and persistent data from the image contents - which depends on cinder for baremetal).
  • Full HA is not yet implemented https://bugs.launchpad.net/quantum/+bug/1174132
  • Bootstrap installation is not yet implemented (depends on full HA).
  • Currently assumes two clouds: under cloud and over cloud. Long term story is to have a single cloud, which is primarily (but not entirely) configuration.

Deploying

As TripleO is not finished, deploying it is tricky. Additionally as by definition it will replace existing facilities (be those manual or automated) within an organisation, some care is needed to make migration, or integration smooth.

This is a sufficiently complex topic, we've created a dedicated document for it - Deploying TripleO.

Design

We start with an image builder, and rules for that to build OpenStack images. We then use Heat to orchestrate deployment of those images onto bare metal using the Nova baremetal driver.

The Heat instance we use is hosted in the same cloud we're deploying, taking advantage of rolling deploys + a fully redundant deployment to avoid needing any manually maintained infrastructure.

Within each machine we use small focused tools for converting Heat metadata to configuration files on disk, and handling updates from Heat. It is possible to replace those with e.g. Chef or Puppet if desired.

Finally, we use this self contained bare metal cloud to deploy a kvm (or Xen or whatever) OpenStack instance as a tenant of the bare metal cloud. In future we would like to consolidate this into one cloud, but there are technical and security issues to overcome first.

We have future worked planned to perform cloud capacity planning, node allocation, and other essential operational tasks.

Why?

Driving the cost of operations down, increasing reliability of deployments and consolidating on a single API for deploying machine images, to get great flexibility in hardware use.

The use of gold images allows us to test precisely what will be running in production in a test environment - either virtual or physical. This provides early detection of many issues. Gold image building also ensures that there is no variation between machines in production - no late discovery of version conflicts, for instance.

Using CI/CD testing in the deployment pipeline gives us:

  • The ability to deploy something we have tested.

  • With no variation on things that could invalidate those tests (kernel version, userspace tools OpenStack calls into, ...)

  • While varying the exact config (to cope with differences in e.g. network topology between staging and production environments).

None of the existing ways to deploy OpenStack permit you to move hardware between being cloud infrastructure to cloud offering and back again. Specifically, a given hardware node has to be either managed by e.g. Crowbar, or not managed by Crowbar and enrolled with OpenStack - and short of doing shenanigans with your switches, this actually applies at a broadcast domain level. Virtualising the role of hardware nodes provides immense freedom to run different workloads via a single OpenStack cloud.

Fitting this into any of the existing deployment toolchains is problematic:

  • you either end up with a circular reference (e.g. Crowbar having to drive Quantum to move a node out of OpenStack and back to Crowbar, but Crowbar brings up OpenStack.

  • or you end up with two distinct clouds and orchestration requirements to move resources between them. E.g. MAAS + OpenStack, or even - as this demo repository does, OpenStack + OpenStack.

Using OpenStack as the single source of control at the hardware node level avoids this awkward hand off, in exchange for a bootstrap problem where OpenStack becomes its own parent. We believe that having a single tool chain to provision and deploy onto hardware is simpler and lower cost to maintain, and so are choosing to have the bootstrap problem rather than the handoff between provisioning systems problem.

Broad conceptual plan

Stage 1

OpenStack on OpenStack with two distinct clouds:

  1. The under cloud, runs baremetal nova-compute and deploys instances on bare metal, is managed and used by the cloud sysadmins, starts deployed onto a laptop or other similar device in a VM.
  2. The over cloud, which runs using the same images as the under cloud, but as a tenant on the undercloud, and delivers virtualised compute machines rather than bare metal machines.

Flat networking will be in use everywhere: the bootstrap cloud will use a single range (e.g. 192.0.2.0/26), the virtualised cloud will allocate instances in another range (e.g. 192.0.2.64/26), and floating ips can be issued to any range the cloud operator has available. For demonstration purposes, we can issue floating ips in the high half of the bootstrap ip range (e.g. 192.168.2.129/25).

Infrastructure like Glance and Swift will be duplicated - both clouds will need their own, to avoid issues with skew between the APIs in the two clouds.

The under cloud will, during its deployment, include enough images to bring up the virtualised cloud without internet access, making it suitable for deploying behind firewalls and other restricted networking environments.

Stage 2

Use Quantum to provide VLANs to the bare metal, permitting segregated management and tenant traffic.

<...>

Stage N

OpenStack on itself: OpenStack on OpenStack with one cloud:

  1. The under cloud is used ts in Stage 1.
  2. KVM or Xen Nova compute nodes are deployed into the cloud as part of the admin tenant, and offer their compute capacity to the under cloud.
  3. Low overhead services can be redeployed as virtual machines rather than physical (as long as they are machines which the cluster can be rebooted without.

Quantum will be in use everywhere, in two layers: The hardware nodes will talk to Openflow switches, allowing secure switching of a hardware node between use as a cloud component and use by a tenant of the cloud. When a node is being used a cloud component, traffic from the node itself will flow onto the cloud's own network (managed by Quantum), and traffic from instances running on that node will participate in their own Quantum defined networks.

Infrastructure such as Glance, Swift and Keystone will be solely owned by the one cloud: there is no duplication needed.

Caveats

It is important to consider some unresolved issues in this plan.

Security

Nova baremetal does nothing to secure transfers via PXE on the network. This means that a node spoofing DHCP and TFTP on the provisioning network could potentially compromise a new machine. As these networks should be under full control of the user, strategies to eliminate and/or detect spoofing are advised.

Also requests from baremetal machines to the Nova/EC2 meta-data service may be transmitted over an unsecured network. This carries the same attack vector as the PXE problems noted above, and so should be given similar consideration.

Machine State

Currently there is no way to guarantee preservation of any of the drive contents on a machine if it is deleted in nova baremetal.

See also

https://github.com/tripleo/incubator/blob/master/notes.md - for technical setup walk-thru. and https://github.com/tripleo/incubator-bootstrap contains the scripts we run on the devstack based bootstrap node.

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