Types of computational storage

Currently, there aretwo different types of computational storage:

• Fixed computational storage services or fixed function devices have an onboard processor with a dedicated function (such as data compression) pre-programmed into them. Although they are easier to set up and install, they are function-specific and lack flexibility. One example of a fixed-function device is the SSD by Flexxon that monitors disk access for potential malware attacks.
• Programmable computational storage services or general-purpose devices are computational storage devices with an operating system that is typically Linux-based and needs to be programmed like a standard server. These have an on-board processor that can be programmed to perform specific data processing tasks. While general-purpose devices are far more suitable to meet specific business needs, they naturally take longer to set up.

Challenges remain

Despite the potential benefits of computational storage, there remain significant challenges to be addressed. The underlying technology of computational storage is in its infancy, and it’s currently only available from a limited number of manufacturers.

While SSDs are interchangeable and can easily interface with each other, this interoperability is lost with computational storage. Each supplier’s approach to computational storage is sufficiently different that interchangeability is not yet currently available.

As recently as late 2022, theStorage Networking Industry Association (SNIA) released new hardware and software architectural standards, as well as a preliminary standard for theapplication programming interface needed to access computational storage devices.

The potential security risks of computational storage are also not yet fully understood. The implications of potential threats and the security requirements of having a processor built into the storage device are yet to be fully considered.

To take full advantage of computational storage, existing applications and services may need to be re-factored to integrate with the new systems. This could make it difficult to use computational storage with existing applications, and development teams will need a thorough understanding of potential pitfalls. Modifying code always carries the risk of unintended consequences that some organisations may be unwilling to chance.

The question remains whether applications will be adapted to use computational storage in the future. If they are, then computational storage could reduce processing time. However, whether applications will be written to take advantage of on-board processing is dependent on how widespread computational storage becomes.

Use cases

Computational storage will not be a universal performance cure-all, as a single computational device will only offer performance gains in specific areas and could be costly. However, the onboard processors of computational storage make them ideal for specific data-intensive processing tasks. Some examples include real-time data analysis, machine learning and video compression for content distribution networks.

Given the limited processing power available to computational storage, tasks that are compute-intensive, such as modelling complex simulations, remain best performed by a dedicated processor.

Some of the companies that have already adopted computational storage include Tesla, Google, Facebook and Yahoo Mail. Some large-scale datacentres that offer processing resources, such as AWS and Alibaba, have also adopted computational storage.

Computational storage devices are already commercially available, and any system equipped with this new technology will potentially be less CPU-intensive than conventional architectures.

With data-intensive tasks performed by the computational storage processor, the system processor can focus on compute-intensive segments of the workload. Managing a system in such a way increases overall performance, and allows tasks to be conducted faster and more efficiently. This would also enable time-critical workloads to become more viable.