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Given the findings of analyst firm IDC, the volume of info that we yield is projected to grow by an astonishing 27% per year. In order to create value from this data, we need to be capable of processing it in a meaningful way. Going by the latest trends, however, data processing can no longer take place inside a cloud infrastructure only. The focus has shifted on gaining insight from where the data sits, i.e., inside storage devices — this is driving the explosive growth of computational storage.

 

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Computational Storage— Taking Processing Closer to Data

Computational storage is a technique of making storage devices intelligent so that they can process information directly on the disk where it is housed. The approach limits the massive volumes of data movements for external processing and renders significant benefits. It includes reduced lag and bandwidth consumption, enhanced security, and energy savings. 

 

Computational storage combines storage operations and processing to run applications locally. This reduces the processing needed on the remote server and curtails data movements. 

 

How does it work? The processors in the drive controller are committed to processing the data held in the drive directly, which enables the remote host processor to perform other activities. In a traditional computing environment, when a device intends to do some data processing, it requests info from storage. Conversely, when it comes to computational storage, the system does not request data, since the operation can be undertaken on the stored data by the drive itself. As data is not required to leave the drive, computational storage is a smart, safe, and energy-efficient solution for next-gen storage applications.

Linux— A key to the expeditious adoption of computational storage

The quantum of stored data and workload software that operates on it to generate awareness and value, are both rapidly expanding. This burgeoning spike is a major bottleneck— Computational storage can help by enabling workloads to move where the data is physically stored.

 

Linux on-drive is seen as a gateway to the widespread and rapid adoption of CSDs (Computational Storage Drives). On a standard SSD, NVMe protocols send data blocks to be saved on the drive and later recover them from the read drive. However, the drive isn’t aware of that. For instance, ten blocks of written data create a JPEG image. In the case of Linux, the drive can ‘deploy’ the file system that is stored on the drive and recognizes the data blocks on it. The CSD requires this info to work independently on the data stored on the drive – for example, defining the images stored on the drive using Machine Learning.

 

Linux facilitates storage players to harness the massive open-source Linux platforms and allows encryption and warehousing without having to re-invent new solutions. It also allows workloads to be easily transferred from the server to the storage drive. 

Uniformity will accelerate the future of computational storage.

CSDs will actually take off when there are certain standards in place. As more and more Computational Storage Drives become available, the market is bound to grow speedily, enabling innovation through the local generation of meaningful insights from the data stored on the drive. The vital requirement is creating awareness and generating value from that data. It can be best accomplished by working on information where it is stored, thus reducing delays and bandwidth requirements. 

 

A linked CSD Ethernet running Linux is essentially a small server. It has internet access, computing power, memory and storage – and can be installed anywhere to store and generate insight and value from the housed data. This brings enormous potential to many markets and lays a path for several startling innovations.

Computational storage building value across an array of applications

Data workloads can be processed directly on the storage controller— courtesy computational storage. This is critical to addressing the processing needs of analytics, machine learning and AI programs. This advantage opens up immense possibilities across applications, including IoT, ML and edge computing. 

Computational storage can have a serious influence on: 

  • Database expedition, where processing is undertaken directly on data 
  • Content delivery networks (CDNs), which make it easy to deliver local content 
  • Artificial Intelligence helps generate deep insights directly from huge volumes of data 
  • Edge computing, where the Linux running CSD is a small self-contained server 
  • Image categorization, facilitating the meta-tagging directly on the data where it is stored 
  • Transit, direct processing of stored telemetry data in a vehicle 

If we take the aviation transport example, modern aircrafts produce several terabytes of data a day, and this info is typically unloaded for analysis. With the advent of computational storage, airlines may conduct real-time data analysis directly on the drive, stored inside the aircraft itself. As a consequence, when a plane lands, this technology ensures that it is safe for the next flight to take off in another 30 mins or so. This  leads to quicker turnaround and increased passenger safety.

The Conclusion

Computational storage allows us to optimize the usefulness of data for organizations. It generates the computational force needed to give fast and easy access to data insights. CSDs are advancing quickly, and we can clearly foresee broader acceptance with creative apps getting ready to endorse this technique in the near future.