A universal definition for software-defined storage doesn't exist yet; vendors often shape the definition to adhere to their offerings. But experts agree that software-defined storage emphasizes storage-related services rather than storage hardware, and the use of programming and policies to automate data center management. The technology's benefits range from flexibility to cost, but it doesn't come without its challenges -- especially since it's still new.
With software-defined storage (SDS), IT teams can provision and manage storage through software and APIs, which is a more agile approach than manually making changes. But for businesses where change is inherently slow and infrequent, this doesn't add more value. For example, financial and government agencies tend to be relatively static and tightly controlled. The ability to provision storage in seconds with SDS products loses its value when the change approval takes three weeks.
SDS products can suffer from hardware limitations
Many consider the biggest value of SDS to be its software-defined nature. However, software must run on hardware, and the limitations of that hardware can become limitations to the SDS. For example, most SDS products use x86 servers with multicore CPUs. To maximize performance, the SDS product must be efficiently multithreaded -- an inherently difficult programming problem. When functions are tied to single CPU cores, performance can be limited. Faster CPU cores help, but clock speeds haven't increased in a long time. Driving full speed data from 10 gigabit Ethernet through a CPU to a Non-Volatile Memory express flash device requires a lot of careful tuning due to high throughput capabilities. These devices are all expensive and included in the final cost of SDS products.
In one category of SDS, a cluster of commodity x86 servers uses smart software to provide a shared storage array. Together, the group of servers provides enough capacity and performance for a workload, usually a group of virtualization hosts. Sometimes, the storage servers are VMs running on the hypervisor nodes -- a model known as Hyper Converged Infrastructure (HCI).
One of the challenges is that these scale-out storage systems are designed to be consumed by a scale-out workload. A cluster of ten storage nodes might serve a cluster of 30 hypervisor nodes. In effect, the cluster has 10 little pools of resources that each handles an average of three hypervisor nodes. These scale-out SDS products may not be able to deliver extreme performance to a single, high-demand workload. A single, physical database server may need more performance than one node can deliver. It is difficult to make a scale-out storage system aggregate all of its nodes into a single performance pool for a single workload.
Budgeting challenges with SDS products
Another challenge with scale-out storage systems, including HCI, is that they deliver the greatest value if you buy in small increments. Many scale-out storage vendors will suggest that you buy only the storage capacity and performance that you need for the year. As your requirements grow, they'll sell you a few more nodes. This just-in-time delivery means you get immediate, maximum value from the money you spend. Before you buy the next few nodes, a new CPU or faster solid-state drive might become available. The price/performance ratio improves so you get even better value on the next purchase.
However, most IT budget cycles are based on bulk replacement every few years, not incremental replacement spread over years. Organizations end up having to buy enough scale-out SDS for five years all in one block, which reduces the value per dollar spent and may make scale-out storage less financially viable than a monolithic array.
Since SDS is a recent phenomenon, SDS products are either recently developed or bolted onto existing products. Remember that newly developed products do not necessarily have the features of more mature products, such as good operational processes, stable feature sets and reliability.
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