MAAS is Metal As A Service. It lets you treat physical servers like virtual machines (instances) in the cloud. Rather than having to manage each server individually, MAAS turns your bare metal into an elastic cloud-like resource.
Machines can be quickly provisioned and then destroyed again as easily as you can with instances in a public cloud like Amazon AWS, Google GCE, and Microsoft Azure, among others.
MAAS can act as a standalone PXE/preseed service or it can be integrated with other technologies. In particular, it is designed to work especially well with Juju, the service and model management service. It's a perfect arrangement: MAAS manages the machines and Juju manages the services running on those machines.
Note: KVM guests can also act as MAAS nodes as long as they are set to boot from the network (PXE).
MAAS provides management of a large number of physical machines by creating a single resource pool out of them. Participating machines can then be provisioned automatically and used as normal. When those machines are no longer required they are "released" back into the pool. MAAS integrates all the tools you require in one smooth experience. It includes:
- a beautiful web UI
- full API/CLI support
- high availability (optional)
- IPv6 support
- open source IP address management (IPAM)
- Ubuntu, CentOS, Windows, RHEL and SUSE installation support
- inventory of components
- DHCP and DNS for other devices on the network
- VLAN and fabric support
Note: Windows, RHEL and SUSE images require Ubuntu Advantage to work properly with MAAS.
MAAS manages a pool of nodes. After registering ("Enlisting" state) a new system and preparing it for service ("Commissioning" state), the system joins the pool and is available for use ("Ready" state).
MAAS controls machines through IPMI (or another BMC) or converged chassis controller such as Cisco UCS.
Warning: A machine destined for MAAS will have its disk space overwritten. A node in the pool is under MAAS's sole control and should not be provisioned using other methods.
Users of the MAAS then allocate them for their own use ("Acquire") when they go into use. Any subsequently installed operating system will contain the user's SSH public key for remote access (the user's MAAS account first needs to import the key). The web UI also allows for manual allocation in the sense of reserving hardware to specific users for later use.
When allocating from the API/CLI, you can specify requirements ("constraints") for a machine. Common constraints are: memory, CPU cores, connected networks, and what physical zone they should be in.
An allocated MAAS node is not like a virtual instance in a cloud: you get complete control, including hardware drivers and root access. To upgrade a BIOS, for example, an administrator could allocate a node to themselves, and run a vendor-supplied upgrade utility.
Once you are done with a node you have allocated you send it back to the pool for re-use.
Note that Juju is designed to work with MAAS. In this case, MAAS becomes a sort of backend (resource pool) for Juju, or a "cloud provider" in Juju terminology. However, everything that was stated earlier still applies. For instance, if Juju removes a machine then MAAS will, in turn, release that machine to the pool.
The key components of a MAAS installation are the region controller and the rack controller. See Concepts and terms for how each are defined.
Unless there is specific reason not to, it is recommended to have both
controllers residing on the same system. A no-fuss way to achieve this is by
maas metapackage, or by installing from the Ubuntu Server ISO.
Multiple region and rack controllers are required if high availability and/or load balancing (see HA page) is desired.
It's important to note that the all-in-one solution will provide a DHCP service. Review your existing network design in order to determine whether this will cause problems. See DHCP for more on this subject.
There are three ways to install MAAS:
- From the Ubuntu Server ISO
- From software packages ("debs")
- As a self-contained LXD environment
These methods, and their respective advantages, are fleshed out on the Installation page.
The minimum requirements for the machines that run MAAS vary widely depending on local implementation and usage.
Below, resource estimates are provided based on MAAS components and operating system (Ubuntu Server). A test (or proof of concept) and a production environment are considered.
This is a proof of concept scenario where all MAAS components are installed on a single host. Two complete sets of images (latest two Ubuntu LTS releases) for a single architecture (amd64) have been assumed.
|Memory (MB)||CPU (GHz)||Disk (GB)|
|Region controller (minus PostgreSQL)||512||0.5||5|
|Ubuntu Server (including logs)||512||0.5||5|
Therefore, the approximate requirements for this scenario are: 2 GB memory, 2 GHz CPU, and 20 GB of disk space.
This is a production scenario that is designed to handle a high number of sustained client connections. Both high availability (region and rack) and load balancing (region) have been implemented.
Even though extra space has been reserved for images (database and rack controller) some images such as those for Microsoft Windows may require a lot more (plan accordingly).
|Memory (MB)||CPU (GHz)||Disk (GB)|
|Region controller (minus PostgreSQL)||2048||2.0||5|
|Ubuntu Server (including logs)||512||0.5||20|
Therefore, the approximate requirements for this scenario are:
- A region controller (including PostgreSQL) is installed on one host: 4.5 GB memory, 4.5 GHz CPU, and 45 GB of disk space.
- A region controller (including PostgreSQL) is duplicated on a second host: 4.5 GB memory, 4.5 GHz CPU, and 45 GB of disk space.
- A rack controller is installed on a third host: 2.5 GB memory, 2.5 GHz CPU, and 40 GB of disk space.
- A rack controller is duplicated on a fourth host: 2.5 GB memory, 2.5 GHz CPU, and 40 GB of disk space.
Note: Figures in the above two tables are for the MAAS infrastructure only. That is, they do not cover resources needed on the nodes that will subsequently be added to MAAS. That said, node machines should have IPMI-based BMC controllers for power cycling, see BMC Power Types.
Examples of factors that influence hardware specifications include:
- the number of connecting clients (client activity)
- the manner in which services are distributed
- whether high availability is used
- whether load balancing is used
- the number of images that are stored (disk space affecting PostgreSQL and the rack controller)
Equally not taken into account is a possible local image mirror which would be a large consumer of disk space.
One rack controller should not be used to service more than 1000 nodes (whether on the same or multiple subnets). There is no load balancing at the rack level so further independent rack controllers will be needed with each one servicing its own subnet(s).