Storage is found in many parts of the OpenStack stack, and the differing types can cause confusion to even experienced cloud engineers. This section focuses on persistent storage options you can configure with your cloud. It's important to understand the distinction between ephemeral storage and persistent storage.
If you deploy only the OpenStack Compute Service (nova), your users do not have access to any form of persistent storage by default. The disks associated with VMs are "ephemeral," meaning that (from the user's point of view) they effectively disappear when a virtual machine is terminated.
Persistent storage means that the storage resource outlives any other resource and is always available, regardless of the state of a running instance.
Today, OpenStack clouds explicitly support two types of persistent storage: object storage and block storage.
With object storage, users access binary objects through a REST API. You may be familiar with Amazon S3, which is a well-known example of an object storage system. Object storage is implemented in OpenStack by the OpenStack Object Storage (swift) project. If your intended users need to archive or manage large datasets, you want to provide them with object storage. In addition, OpenStack can store your virtual machine (VM) images inside of an object storage system, as an alternative to storing the images on a file system.
OpenStack Object Storage provides a highly scalable, highly available storage solution by relaxing some of the constraints of traditional file systems. In designing and procuring for such a cluster, it is important to understand some key concepts about its operation. Essentially, this type of storage is built on the idea that all storage hardware fails, at every level, at some point. Infrequently encountered failures that would hamstring other storage systems, such as issues taking down RAID cards, or entire servers are handled gracefully with OpenStack Object Storage.
A good document describing the Object Storage architecture is found within the developer documentation (http://docs.openstack.org/developer/swift/overview_architecture.html)—read this first. Once you understand the architecture, you should know what a proxy server does and how zones work. However, some important points are often missed at first glance.
When designing your cluster, you must consider durability and availability. Understand that the predominant source of these is the spread and placement of your data, rather than the reliability of the hardware. Consider the default value of the number of replicas, which is three. This means that before an object is marked as having been written, at least two copies exists—in case a single server fails to write, the third copy may or may not yet exist when the write operation initially returns. Altering this number increases the robustness of your data, but reduces the amount of storage you have available. Next, look at the placement of your servers. Consider spreading them widely throughout your data center's network and power-failure zones. Is a zone a rack, a server, or a disk?
Object Storage's network patterns might seem unfamiliar at first. Consider these main traffic flows:
Among object, container, and account servers
Between those servers and the proxies
Between the proxies and your users
Object Storage is very "chatty" among servers hosting data—even a small cluster does megabytes/second of traffic, which is predominantly "Do you have the object?"/"Yes I have the object!." Of course, if the answer to the aforementioned question is negative or the request times out, replication of the object begins.
Consider the scenario where an entire server fails and 24 TB of data needs to be transferred "immediately" to remain at three copies—this can put significant load on the network.
Another fact that's often forgotten is that when a new file is being uploaded, the proxy server must write out as many streams as there are replicas—giving a multiple of network traffic. For a 3-replica cluster, 10Gbps in means 30Gbps out. Combining this with the previous high bandwidth demands of replication is what results in the recommendation that your private network is of significantly higher bandwidth than your public need be. Oh, and OpenStack Object Storage communicates internally with unencrypted, unauthenticated rsync for performance—you do want the private network to be private.
The remaining point on bandwidth is the public facing portion. The swift-proxy service is stateless, which means that you can easily add more and use HTTP load-balancing methods to share bandwidth and availability between them.
More proxies means more bandwidth, if your storage can keep up.
Block storage (sometimes referred to as volume storage) provides users with access to block-storage devices. Users interact with block storage by attaching volumes to their running VM instances.
These volumes are persistent: they can be detached from one instance and re-attached to another, and the data remains intact. Block storage is implemented in OpenStack by the OpenStack Block Storage (cinder) project, which supports multiple back ends in the form of drivers. Your choice of a storage back end must be supported by a Block Storage driver.
Most block storage drivers allow the instance to have direct access to the underlying storage hardware's block device. This helps increase the overall read/write IO.
Experimental support for utilizing files as volumes began in the Folsom release. This initially started as a reference driver for using NFS with cinder. By Grizzly's release, this has expanded into a full NFS driver as well as a GlusterFS driver.
These drivers work a little differently than a
traditional "block" storage driver. On an NFS or
GlusterFS file system, a single file is created and
then mapped as a "virtual" volume into the instance.
This mapping/translation is similar to how OpenStack
utilizes QEMU's file-based virtual machines stored in
/var/lib/nova/instances.
Table 6.1, “OpenStack Storage” explains the different storage concepts provided by OpenStack.
| Ephemeral storage | Block storage | Object storage | |
|---|---|---|---|
Used to… |
Run operating system and scratch space |
Add additional persistent storage to a virtual machine (VM) |
Store data, including VM images |
Accessed through… |
A file system |
A block device that can be partitioned, formatted, and mounted (such as, /dev/vdc) |
REST API |
Accessible from… |
Within a VM |
Within a VM |
Anywhere |
Managed by… |
OpenStack Compute (nova) |
OpenStack Block Storage (cinder) |
OpenStack Object Storage (swift) |
Persists until… |
VM is terminated |
Deleted by user |
Deleted by user |
Sizing determined by… |
Administrator configures size settings, known as flavors |
Specified by user in initial request |
Amount of available physical storage |
Example of typical usage… |
10 GB first disk, 30 GB second disk |
1 TB disk |
10s of TBs of dataset storage |
Users will indicate different needs for their cloud use cases. Some may need fast access to many objects that do not change often, or they want to set a Time To Live (TTL) value on a file. Others may access only storage that is mounted with the file system itself, but want it to be replicated instantly when starting a new instance. For other systems, ephemeral storage—storage that is released when a VM attached to it is shut down— is the preferred way. When you select storage back ends, ask the following questions on behalf of your users:
Do my users need block storage?
Do my users need object storage?
Do I need to support live migration?
Should my persistent storage drives be contained in my compute nodes, or should I use external storage?
What is the platter count I can achieve? Do more spindles result in better I/O despite network access?
Which one results in the best cost-performance scenario I'm aiming for?
How do I manage the storage operationally?
How redundant and distributed is the storage? What happens if a storage node fails? To what extent can it mitigate my data-loss disaster scenarios?
To deploy your storage by using entirely commodity hardware, you can use a number of open-source packages, as follows:
| Object | Block | File-level* | |
|---|---|---|---|
Swift |
|
|
|
LVM |
|
|
|
Ceph |
|
|
Experimental |
Gluster |
|
|
|
NFS |
|
|
|
ZFS |
|
|
|
* This list of open source file-level shared storage solutions is not exhaustive; other open source solutions exist (MooseFS). Your organization may already have deployed a file-level shared storage solution that you can use.
Also, you need to decide whether you want to support object storage in your cloud. The two common use cases for providing object storage in a compute cloud are:
To provide users with a persistent storage mechanism
As a scalable, reliable data store for virtual machine images
This section provides a high-level overview of the differences among the different commodity storage back-end technologies. Depending on your cloud user's needs, you can implement one or many of these technologies in different combinations.
OpenStack Object Storage (swift). The official OpenStack Object Store implementation. It is a mature technology that has been used for several years in production by Rackspace as the technology behind Rackspace Cloud Files. As it is highly scalable, it is well-suited to managing petabytes of storage. OpenStack Object Storage's advantages are better integration with OpenStack (integrates with OpenStack Identity, works with the OpenStack dashboard interface) and better support for multiple data center deployment through support of asynchronous eventual consistency replication.
Therefore, if you eventually plan on distributing your storage cluster across multiple data centers, if you need unified accounts for your users for both compute and object storage, or if you want to control your object storage with the OpenStack dashboard, you should consider OpenStack Object Storage. More detail can be found about OpenStack Object Storage in the section below.
Ceph. A scalable storage solution that replicates data across commodity storage nodes. Ceph was originally developed by one of the founders of DreamHost and is currently used in production there.
Ceph was designed to expose different types of storage interfaces to the end-user: it supports object storage, block storage, and file-system interfaces, although the file-system interface is not yet considered production-ready. Ceph supports the same API as swift for object storage and can be used as a back end for cinder block storage as well as back-end storage for glance images. Ceph supports "thin provisioning," implemented using copy-on-write.
This can be useful when booting from volume because a new volume can be provisioned very quickly. Ceph also supports keystone-based authentication (as of version 0.56), so it can be a seamless swap in for the default OpenStack swift implementation.
Ceph's advantages are that it gives the administrator more fine-grained control over data distribution and replication strategies, enables you to consolidate your object and block storage, enables very fast provisioning of boot-from-volume instances using thin provisioning, and supports a distributed file-system interface, though this interface is not yet recommended (http://ceph.com/docs/master/faq/) for use in production deployment by the Ceph project.
If you want to manage your object and block storage within a single system, or if you want to support fast boot-from-volume, you should consider Ceph.
Gluster. A distributed, shared file system. As of Gluster version 3.3, you can use Gluster to consolidate your object storage and file storage into one unified file and object storage solution, which is called Gluster For OpenStack (GFO). GFO uses a customized version of swift that enables Gluster to be used as the back-end storage.
The main reason to use GFO rather than regular swift is if you also want to support a distributed file system, either to support shared storage live migration or to provide it as a separate service to your end-users. If you want to manage your object and file storage within a single system, you should consider GFO.
LVM. The Logical Volume Manager, a Linux-based system that provides an abstraction layer on top of physical disks to expose logical volumes to the operating system. The LVM back end implements block storage as LVM logical partitions.
On each host that will house block storage, an administrator must initially create a volume group dedicated to Block Storage volumes. Blocks are created from LVM logical volumes.
![[Note]](../common/images/admon/note.png)
Note LVM does not provide any replication. Typically, administrators configure RAID on nodes that use LVM as block storage to protect against failures of individual hard drives. However, RAID does not protect against a failure of the entire host.
ZFS. The Solaris iSCSI driver for OpenStack Block Storage implements blocks as ZFS entities. ZFS is a file system that also has the functionality of a volume manager. This is unlike on a Linux system, where there is a separation of volume manager (LVM) and file system (such as, ext3, ext4, xfs, and btrfs). ZFS has a number of advantages over ext4, including improved data-integrity checking.
The ZFS back end for OpenStack Block Storage supports only Solaris-based systems such as Illumos. While there is a Linux port of ZFS, it is not included in any of the standard Linux distributions, and it has not been tested with OpenStack Block Storage. As with LVM, ZFS does not provide replication across hosts on its own; you need to add a replication solution on top of ZFS if your cloud needs to be able to handle storage-node failures.
We don't recommend ZFS unless you have previous experience with deploying it, since the ZFS back end for Block Storage requires a Solaris-based operating system, and we assume that your experience is primarily with Linux-based systems.
We hope that you now have some considerations in mind and questions to ask your future cloud users about their storage use cases. As you can see, your storage decisions will also influence your network design for performance and security needs. Continue with us to make more informed decisions about your OpenStack cloud design.
