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Remember those light beer commercials back in the 1980s with competing contingents shouting “Tastes great!” and “Less filling!” at each other? The idea was that a beer could have fewer calories without sacrificing taste. Perhaps advocates of automated storage tiering (AST) are taking a similar approach: its two goals -- lower cost and higher performance -- seem to be just as diametrically opposed. Historically, if you wanted higher I/O performance (data throughput) you bought high-end Fibre Channel (FC) arrays and disk devices. If budget was a bigger issue, you gravitated toward IP storage and SATA drives.
In practice, most companies use both types of storage in an effort to match application throughput requirements with budget constraints. That effectively represents tiered storage, and how that tiering is managed boils down to whether the staff chooses de facto manual tiering or implements an automated system. Given the increasing complexity of data storage environments, data growth and the typically poor utilization of storage, it’s hard to imagine how manual tiering management is tenable for the long term.
A delicate balance: cost and performance
When storage vendors speak of their AST solutions, they all tout higher performance and lower cost. Given the dichotomy between lower cost and higher performance, one wonders whether they’ve somehow discovered a way to repeal the laws of physics. Fortunately for Newtonian
Storage tiering review
Most IT professionals generally understand storage tiering, but it’s worth a brief review of the concept. Tiers are defined predominantly by the performance characteristics of the underlying media. Solid-state drives (SSDs) and flash memory are referred to as tier 0; high-speed FC drives such as 15K rpm disks are tier 1; 10K rpm FC and SAS disks are tier 2; and less than 10K rpm SATA disks are tier 3. These aren’t absolute rules, but they’re typical tier differentiators.
Tiers are implemented in two different ways. The first is intra-array, in which a single array is populated with two or more media types. The second is inter-array, in which arrays with different media types are associated to facilitate data movement. It’s also possible to have both simultaneously in the same configuration.
Automating the tiering process
Neither storage tiering nor AST are new technologies. In fact, Hewlett-Packard (HP) Co. claims to have implemented automated storage tiering in 1996. Nevertheless, the adoption of AST has been relatively slow. That’s because the earliest implementations required a significant effort to classify data and develop the policies that governed data movement between tiers. Most often, data was moved based on age, which is rarely the best arbiter of value.
Current AST implementations use sophisticated algorithms that calculate the usage of data chunks ranging in size from a 4 KB block up to a 1 GB block, depending on vendor and settings. This calculation is done based on access demand relative to other chunks, as there’s no definition of “high demand.” Data can be elevated to a higher tier during high demand periods and demoted when demand lessens. The quality of the algorithm determines the value of the product and the size of the block determines workload suitability. Smaller block sizes are generally better for random I/O, while larger sizes are better for sequential I/O.
Both established vendors and emerging vendors offer AST capabilities. Some of the newer vendors, such as Dell Compellent, have made automated storage tiering a cornerstone of their product architecture. With the company’s Storage Center product line and its Fluid Data Architecture, there’s only one array architecture and AST is an integrated part of it. Fluid Data Architecture data movement block size is a relatively granular 2 MB.
Similarly, for Avere Systems Inc., AST isn’t an optional feature in its FXT appliances. However, it adds the ability to use any network-attached storage (NAS) or JBOD array as tier 3 storage. Thus, Avere offers both inter- and intra-array tiering. In addition, Avere uses its own file system, which gives it an additional measure of control over data movement in its algorithm. FXT is a “set-and-forget” model that doesn’t allow user modification of movement policies, although tiers can be scaled separately to match workload changes.
This was first published in May 2011