storage can at first be overwhelming. Gaining the required understanding of all the components, technologies, and vendor-specific proprietary hardware takes time.
This chapter addresses each of these storage components and technologies, and their interactions in the classic storage environment. Upcoming chapters then move on to next-generation VMware storage solutions and the software-defined storage model.
This classic storage model employs intelligent but highly proprietary storage systems to group disks together and then partition and present those physical disks as discrete logical units. Because of the proprietary nature of these storage systems, my intention here is not to address the specific configuration of, for instance, HP, IBM, or EMC storage, but to demonstrate how the vSphere platform can use these types of classic storage devices.
In the classic storage model, the logical units, or storage devices, are assigned a logical unit number (LUN) before being presented to vSphere host clusters as physical storage devices. These LUNs are backed by a back-end physical disk array on the storage system, which is typically served by RAID (redundant array of independent disks) technology; depending on the hardware type, this technology can be applied at either the physical or logical disk layer, as shown in Figure 2.1.
Figure 2.1 Classic storage model
The LUN, or storage device, is a virtual representation of a portion of physical disk space within the storage array. The LUN aggregates a portion of disk space across the physical disks that make up the back-end system. However, as illustrated in the previous figure, the data is not written to a single physical device, but is instead spread across the drives. It is this mechanism that allows storage systems to provide fault tolerance and performance improvements over writing to a single physical disk.
This classic storage model has several limitations. To start with, all virtual disks (VMDKs) on a single LUN are treated the same, regardless of the LUN’s capabilities. For instance, you cannot replicate just a single virtual disk at the storage level; it is the whole LUN or nothing. Also, even though vSphere now supports LUNs of up to 64 terabytes, LUNs are still restricted in size, and you cannot attach more than 256 LUNs to a vSphere host or cluster.
In addition, with this classic storage approach, when a SCSI LUN is presented to the vSphere host or cluster, the underlying storage system has no knowledge of the hypervisor, filesystem, guest operating system, or application. It is left to the hypervisor and vCenter, or other management tools, to map objects and files (such as VMDKs) to the corresponding extents, pages, and logical block address (LBA) understood by the storage system. In the case of a NAS-based NFS solution, there is also a layer of abstraction placed over the underlying block storage to handle file management and the associated file-to-LBA mapping activity.
Other classic storage architecture challenges include the following:
• Proprietary technologies and not commodity hardware
• Low utilization of raw storage resources
• Frequent overprovisioning of storage resources
• Static, nonflexible classes of service
• Rigid provisioning methodologies
• Lack of granular control, at the virtual disk level
• Frequent data migrations required, due to changing workload requirements
• Time-consuming operational processes
• Lack of automation and common API-driven provisioning
• Slow storage-related requests requiring manual human interaction to perform maintenance and provisioning operations
Most storage systems have two basic categories of LUN: the traditional model and disk pools. The traditional model has been the standard mechanism for many years in legacy storage systems. Disk pools have recently provided compatible systems with additional flexibility and scalability, for the provisioning of virtual storage resources.
In the traditional model, when a LUN is created, the number and choice of disks directly corresponds to the RAID type and disk device configured. This traditional model has limitations, especially in virtual environments, which is why it was superseded by the more modern disk pool concept. The traditional model would often have a fixed maximum number of physical disks, which could be combined to form the logical disk. This maximum disk limitation was imposed by storage array systems as a hard limit, but was also linked to the practical considerations around availability and performance.
With this traditional disk-grouping method, it was often possible to expand a logical disk beyond its imposed physical limits by creating some sort of MetaLUN. However, this increased operational complexity and could often be difficult and time-consuming.
An additional consideration with this approach was that the amount of storage provisioned was often far greater than what was required, because of the tightly imposed array constraints. Provisioning too much storage was also done by storage administrators to prevent application outages often required to expand storage, or to cover potential workload requirements or growth patterns that were unknown. Either way, this typically resulted in expensive disk storage lying unutilized for a majority of the time.
On the plus side, this traditional approach to provisioning LUNs provided fixed, predictable performance, based on the RAID and disk type employed. For this reason, this method of disk provisioning is still sometimes a good choice when storage requirements do not have large amounts of expected growth, or have fixed service-level agreements (SLAs) based on strict application I/O requirements.
In more recent years, storage vendors have moved almost uniformly to disk pools. Pools can use far larger groups of disks, from which LUNs can be provisioned. While the disk pool concept still comprises physical disks employing a RAID mechanism to stripe or mirror data, with a LUN carved out from the pool, this device type can be built across a far greater number of disks. As a result of this approach, storage administrators can provision significantly larger LUNs without sacrificing levels of availability.
However, the sacrifice made by employing this more flexible approach is the small level of variability in performance that results. This is due to both the number of applications that are likely to share the storage of this single disk pool, which will inevitably increase over time, and the potential heterogeneous nature of disk pools, which have no requirement for uniformity, as it relates to the speed and capacity of individual physical disks (see Figure 2.2).
Figure 2.2 Storage LUN provisioning mechanisms
Also relevant from a classic storage design perspective are the trade-offs associated with choosing between provisioning a single disk pool or multiple disk pools. If choosing multiple pools, what criteria should a design use to define those pools?
We address tiering and autotiering in more detail later in this chapter, but this is one of the key design factors when considering whether to provision a single pool, with all the disk resources, or to deploy multiple storage pools on the array and to split storage resources accordingly.
Choosing a single pool provides simpler operational and capacity management of the environment. In addition, it allows LUNs or filesystems to be striped across a larger number of physical disks, which improves overall performance of the array system. However, it is also likely that a larger number of hosts and clusters will share the same underlying back-end disk system. Therefore, there is an increased possibility for resource contention and also an increased risk of specific applications not using an optimal RAID configuration, and maximizing I/O, which is likely to result in a degraded performance for those workloads.
Using multiple disk pools offers the flexibility to customize storage resources to meet specific application I/O requirements, and also allows operational teams to isolate specific workloads to specific physical drives, reducing the risk of disk contention.