VMware Software-Defined Storage. Martin Hosken
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Название: VMware Software-Defined Storage
Автор: Martin Hosken
Издательство: John Wiley & Sons Limited
Жанр: Зарубежная образовательная литература
isbn: 9781119292784
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• 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 This traditional model has limitations, especially in virtual environments, which is why it was superseded by the more modern disk pool concept.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. However, as the pools are inevitably smaller in this type of architecture, some systems may experience lower levels of performance than with a single larger pool. In addition, with multiple smaller pools, capacity planning becomes more complex, as growth across disk pools may not be consistent, and there is likely to be an increase in overall disk resources not being used.
Neither of these options is without its advantages and drawbacks, and there is no one perfect solution. However, designing a solution that uses multiple smaller pools over one universal disk pool will likely come down to one or more of the following key design factors:
• Disk pools based on function, such as development, QA, production, and so on. This option may be preferred if you are concerned with performance for specific environments, and want to isolate them from impacting the production system.
• In multitenanted environments, whether public or based on internal business units, each tenant can be allocated its own pool. However, depending on the environment and SLAs, each tenant might end up with multiple pools in order to address specific I/O characteristics of various applications.
• Application-based pools, such as database or email systems. This can provide optimum performance as applications of similar type often have similar I/O characteristics. For this reason, it may be worth considering designing pools based on application type. However, this also carries the risk of some databases, for instance, generating very high volumes of I/O and potentially impacting other databases residing on the same disk pool.
• Drive technology and RAID type. This allows you to place data on the storage type that best matches the application I/O characteristics, such as reads versus writes versus sequential. However, this approach can also increase costs and does not address any specific application I/O intensity requirement.
• Storage tier–based pools (such as Gold, Silver, and Bronze) could allow you to mix drive technologies and/or RAID types within each pool, therefore reducing the number of pools required to support most application types, configurations, and SLAs.
RAID Sets
The term RAID has already been used multiple times in different contexts, so let’s address this technology next.
RAID (redundant array of independent disks) combines two or more disk drives into a logical grouping, typically known as a RAID set. Under the control of a RAID controller (or in the case of a storage system, the storage processors or controllers), the RAID set appears to the connected hosts as a single logical disk drive, even though it is made up of multiple physical disks. RAID sets provide four primary advantages to a storage system:
• Higher data availability
• Increased capacity
• Improved I/O performance
• Streamlined management of storage devices
Typically, the storage array management software handles the following aspects СКАЧАТЬ