Are We Ready for Computational Storage?

Small-to-medium sized enterprises have become engulfed in customer and business data. One of the biggest problems these enterprises face in this department is not so much the storage of their data, but rather the processing of it. 

An innovative solution to this problem is to rely on computational storage, which is ideal for organizations making the transition to an edge computing storage strategy. Computational storage enables enterprises deploying edge computing to process their data right where it is originally created. This is accomplished via computational storage devices, or CSDs, which are starting to become available commercially from a wide variety of different vendors.

In this article, we’ll cover exactly what computational storage is, the advantages it provides, and where it’s taking us into the future of enterprise storage as a whole.

What is Computational Storage?

You can think of computational storage as delivering processing power to traditional storage systems. As enterprises collect and process more data, it’s become inefficient to process that data away from storage systems. That’s because transferring data between processing resources and storage systems has traditionally been ineffectual. This becomes an even bigger problem as a company’s overall data volume rises.

As noted in the introduction, this is accomplished via computational storage drives or CSDs, which are composed of several CPUs located directly on the storage device or application. Servers running a CSD will see their processing performance improved and energy consumption reduced. 

The concept here, in other words, is that processing power is moved over to the data, and not the other way around. The CSDs will ensure that less data is transferred to the primary CPU, which likewise means that the CPUs will need to carry out less tasks. With the CSDs’ processing capabilities installed directly in the storage application, real time data analytics is made faster. 

Also read: How Object Storage Supports AI and ML Advancements

Why is Computational Storage Important?

The single biggest advantage that computational storage offers is that it increases the performance of data-intensive storage applications and the speed at which data can be read. This alone is highly beneficial for online businesses and SMEs, and especially since data storage and processing is one of the biggest challenges that new online business owners face.

Another significant advantage to computational storage is the fact that it can be implemented in a variety of different methods, including through being installed in storage applications or embedded in storage hard drives.  Even though all storage hard drives already have microprocessors installed, CSDs hold even more powerful processors and accelerates that can perform a variety of tasks, including data compression or proven encryption methods similar to what is used by SSL, or Secure Sockets Layer protection. 

It’s for these reasons that the SNIA (Storage Industry Networking Association), recently formed a Computational Storage Technical Work Group to create new standards for the interoperability, deployment, provisioning, and security of computational storage devices and software. The SNIA has also noted how data architecture as a whole has remained largely the same since the era of floppy disks, and how computational storage systems represent a key solution in an age where traditional storage applications have become overwhelmed by the sheer amount and velocity of data being collected by most online businesses. 

None of this is to say that computational storage is not without its downsides. One significant disadvantage that could prove to be a hindrance to novice IT teams in particular is the fact that computation storage can make their storage architecture more complex. This is due to the additional costs that come with adding CPUs to storage services or applications, as well as the fact that APIs need to be made aware of the CSDs in order to communicate and facilitate data transferring. 

Where is this Taking Us in the Future of Enterprise Storage?

As noted previously, the sheer volume of data collected, stored, and processed by SMEs and online businesses today is far greater than it ever has been before. Traditional storage architectures have managed to keep up when it comes to data volume. A prime example is how flash drives managed to give enterprises a faster and better performing option for database storage.

But as databases have grown, and as data processing has come to encompass AI, ML, and advanced analytics, it’s become clear that new innovations when it comes to data processing and storage are necessary. This is why it makes sense for the IT teams of online enterprises to be able to perform data processing and analysis as close as possible to where the data is being stored. The alternative is transferring entire gigabytes if not terabytes of data from storage software or hard drives to data processing applications. While it’s possible, it’s also not very efficient. 

As the SNIA has pointed out, computational storage stands to emerge as part of the solution. By integrating processing power into storage software or drives, enterprises are able to distribute workloads across CSDs, process and analyze data using machine learning, and keep data transfer bottlenecks to a minimum. 

It’s the goal of the SNIA through their Computational Storage Technical Work Group to draft definitions and specifications for computational storage architectural models and create a programming model for computational storage services. In due time, the SNIA will create a 1.0 version for their architecture specifications, which will become the gold standard for how computational storage interfaces are run.

Computational storage allows us to have storage applications that double as data processing systems. We can therefore expect to enter a new era where enterprise-level storage systems are intelligent, programmable, and more efficient. 

Read next: Top Computational Storage Companies

Nahla Davies
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed—among other intriguing things—to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.

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