We take a look at how Computational Storage Drives are re-defining storage architecture with better performance in Big Data applications. We understand what the technology is, and what are the different business benefits driven by it.
It is quite noticeable that the amount of data generated per day around the globe has increased significantly. So, expecting yesteryear’s technology to handle today’s data deluge for big data applications is an absurd expectation.
Even the reports generated by an analyst firm, IDC, stated that the volume of data generation is expected to grow by an astounding 27% each year. And in order to produce business value from this data, we need to be proficient of processing it into valuable insights, and seemingly, the cloud can no longer be relied on for data processing. The focus will be on high-performance data processing mechanisms and sophisticated data centers.
And that is why; there is a sustained interest around moving processing to the source where data resides. This is precisely where the concept of computational storage is being developed rapidly.
So, What Is Computational Storage?
Computational storage is nothing but a storage subsystem that comprises multiple CPUs that are housed in storage media or the controllers. And this is termed as Computational Storage Devices (CSDs). CSDs are in charge of offering computational storage services. So, the concept here is of migrating processing to the data and not the other way around.
The notion here is to utilize CSDs to forestall some workloads, and this way only fewer data will be transferred to the prime CPU, meaning your CPU will only undertake fewer tasks. The rise of distributed processing platforms like Hadoop has further fueled the interest in this revolutionary storage architecture.
Computational storage architecture
The computational storage is situated near the ARM cortex or same type of processor that is found in front of the storage controller, typically NVMe-based. But some CSDs utilize integrated compute controller or module.
When it comes to SNIA, it categorized the computational storage in two extensive categories: Programmable computational Storage (PCSS) and Fixed Computational Storage Services (FCSS).
PCSS has the capacity of running host operating systems like Linux, meaning it is more flexible. FCSS, on the other hand, are created for the computer-intensive or particular task. But FCSS can offer the best performance with minimum investment. Its architecture also tells if the APIs (Application Programming Interfaces) or drivers are required or if the applications are capable of running just on CSD.
Some systems can use just a CSD while others can prefer a mix of CSDs along with traditional storage. But at present, most systems have the mix for effective performance.
How is computational storage improving storage game?
1- It brings processing power to traditional storage architecture:
We are certainly in the era where we want things to happen in real-time, meaning we want to capture, process as well as act on data at the same time. With computational storage, it has become possible. In simple terms, computational storage behaves like a bridge that closes the gap between data storage and data processing.
When you innate the power of computing from CPUs to processors and traditional storage architectures, you are allowing the system to process the data faster, thus, fostering faster impact and accelerated analysis.
And also, as only the required data is sent to the CPU for processing, the bottlenecks are reduced, leading to minimized load on processing engines.
2- It aids the organizations to enhance processing speed and performance:
AI, IoT and other edge devices are generating an extensive volume of data that becomes extremely hard for the organizations to manage. But, with computational storage, the network tide on performance and latency is organized effectively.
Throughout the continuous real-time integration happening between the storage architectures and compute resources, the computational storage:
- Decreases storage issue, traffic and latency problem
- Enhances infrastructure efficiency and application performance
- Assists in parallel computation, meaning improving the data processing speed
- Reduces regular limitations in regards to memory, traditional compute, I/O and memory
- Maintains energy consumptions for significant power savings
3- The organizations are able to manage the growing data effectively:
IoT has significantly impacted the way everyone interacts and operates. In fact, it is common to see every person interacting with at least one or more IoT devices on a daily basis. These devices can be factory equipment, health monitor or even a smart home appliance. And all of these devices generate data every second, meaning the data is not just massive but also hard to process and manage.
Besides analytics, Big data, machine learning and even AI, the organizations can pay attention to innovations like computational storage to conquer common obstacles.
Let’s see how CSDs can change the storage game:
- With traditional storage, the data was required to travel from the source to the storage. This process increased high levels of data transfer delays. But with computational storage, the distance between the storage and compute is minimized, thus improving process speed.
- With traditional storage, it was tough to capture, store and analyze data in real-time, thus, leading to performance issues. But with CSDs, it has become possible to capture and process the data at the same time. This means increased efficiency in the performance of data-intensive workloads.
With traditional storage, the CPU was always bottlenecked with processing requests. But with CSD, it has become possible to offload the CPU and offer it only required data for processing.
To sum it up
As the business grows, it struggles to keep up with undulating volumes of data. But thankfully, computational storage assists in solving unregulated computing constraints of today.
In fact, it promises to offer a sound impact on various business processes by minimizing the time and distance used by the data to move, decreasing the load on computing resources. And this process, in turn, promotes the faster and efficient process of IoT as well as other edge devices, thus, improving the accuracy and speed of operation.
We are hopeful that our blog was able to offer you with the vital insights.