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Is Computational Storage a Newfound Solution to Edge Storage Problems?

November 23, 2021

In an era of increased data flow, everyone wants to get value from quickly processed data. Yet, edge storage delays the processing because of an existing gap between data storage and processing. It is increasingly important to bridge this gap to improve data processing speeds.

Computational storage is key to bridging that gap. It enables storage devices to process data directly at the point of storage. It reduces data overload at the edge and improves processing speeds. Applying computer storage reduces real-time data processing challenges.

This article will discuss how computational storage is interlinked with edge storage, how it is serving data centers, and the storage issues it is solving for edge devices.

What is computational storage?

Traditionally, data storage and processing occurred at different locations in the data center. You had to move data from the storage point to the processor to create value. This computation paradigm increased data traffic and made the process slow. With an increased data transfer rate on the internet today, you cannot rely on the traditional paradigm. It creates bandwidth limitations and undesirable network disruptions.

Computational storage is a replacement for the traditional paradigm. It closes the gap between storage and processing by bringing the processor closer to the data source and eliminating the inconvenience of moving data to the processor each time you need it. In simplest terms, computational storage decentralizes the data center by making its features accessible at the edge. Instead of transferring data to a centralized processing system, you can now have it processed at the point of generation.

Why computational storage makes sense for the edge

If you have used heavy-running computer systems, the adoption of computer storage for the edge should be a no-brainer. Edge computing reduces the physical distance between the data source and the processor. In this way, data processing occurs at faster rates than before. Implementing computational storage at the edge reduces the time lag (latency). The faster speeds are essential for the optimization of time-critical industrial processes.

Computational storage reduces the cost of moving data to the system. Today, small and big firms and even individuals deal with big data applications. The demand placed on data centers to optimize their performance when processing massive data is high. A reliance on traditional computation models means that firms spend more on storage system upgrades to accommodate the influx of new data. The cost keeps on increasing with more data coming in.

Adopting edge computing allows quicker processing of data as it comes in. This is why platforms like Apache Hadoop and others that have adopted computational storage are gaining popularity. Computational storage efficiently deals with the large and quick influx of data. So, there is no need to have more extensive data centers using traditional computation models.

Besides, computation storage enhances flexibility in data processing. If you have to collect data at one point and later process it somewhere else, you need to operate close to the processors. This scenario imposes limitations on your ability to create value from data when distant from processors. Computation at the edge allows you to collect and process data instantly.

For instance, edge computing makes it easier to process crime reports using surveillance camera images. Such data can undergo processing to reveal geometric figures instantly. This kind of flexibility ensures that data processing occurs at any point and time.

The flexibility that comes with computation at the edge enhances performance. It ensures that data arrives on time and in a stable form. So, there is no risk of losing crucial computational information, making edge computing a reliable option.

Computational storage is a step closer to cyber-security and greater privacy. Computation at the edge enables firms to limit their usage of cloud computing. With computational storage at the edge, you can process much of the data without connecting to the internet.

Edge computing devices are designed such that they can perform several functions offline. In this way, firms only incorporate internet services like cloud computing if necessary. Ultimately, they reduce the risk of becoming victims of cybersecurity attacks.

How computational storage drives can best serve the needs of data centers

Reducing energy consumption

Computational storage drives ensure sustainable energy consumption in data centers. Research findings show that cloud computing in data centers could triple their energy consumption in the next ten years. Energy needs could rise to untenable levels. This trend spells doom for the computation world because computational costs would increase proportionally.

Using computational storage significantly reduces the amount of energy consumed in data centers. Through edge computing, data processing comes closer to the data source. In that way, data centers deal with less trivial tasks, reducing data overload.

Ensuring efficiency in hyper-scale data centers

Adopting edge computing paves the way for efficiency in hyper-scale data centers. Firms like Facebook have large data centers that deal with a massive data influx. The ability of these data centers to run efficiently depends on their ability to optimize physical space and resource utilization.

They must also minimize power and cooling needs. Constantly relying on the traditional model of data processing would derail data processing. Considering the large amount of data coming in and leaving, it would create a time lag. Data centers must adopt edge computing to remove these unnecessary processing hitches. Edge computing transforms them into efficient data processing centers.

Enhancing processing of real-time analytics

Computational storage at the edge enhances the processing of real-time analytics in data centers. This data helps firms determine market trends to optimize their product promotion. It is also essential in various decision-making scenarios. Thus, data centers must deliver real-time data analytics efficiently.

There is a challenge in obtaining real-time analytics, it entails scanning massive data. And companies cannot afford the delays associated with the traditional computation paradigm. So, using data centers that utilize edge computing is the best option for quick data processing.

It enhances real-time data analysis, eliminating a time lag. Besides, computational storage in data centers ensures an immediate disqualification of data irrelevant to the analytics query. This action reduces the volume of data entering the central processor unit, enhancing its performance.

Optimizing CDNs performance

Because of computational storage, Content Delivery Networks (CDNs) experience better performance. CDNs refer to servers and file storage devices that replicate services on many geographically distributed surrogate systems.

They aim to improve the quality and scalability of services provided through the internet by enhancing efficiency and reducing a time lag. But, since they handle large data volumes and should work at fast speeds, the traditional computation paradigm is unsustainable.

Computational storage at the edge optimizes the performance of CDNs significantly. They process data faster than ever at a cost-effective rate. Consequently, the value they gain from data processing transforms into greater profitability. Besides, computational storage at the edge reduces infrastructural costs and enhances transactional processing.

How computational storage solves edge storage issues

Implementing computational storage at the edge eliminates problems of limited data storage in edge storage devices. Surging data volumes require systems that process data quickly and leave room to accommodate more. Edge computing is critical because it eliminates the need for larger space to accommodate new data.

Computational storage improves data processing speeds and data efficiency. Edge storage devices can be pretty slow because they need to transfer data to the central processor. And the massive data generated daily requires fast processing to avoid latency.

Delays can prove costly to a company’s activities. Edge computing allows instant data processing, eliminates latency, and promotes efficiency. It supports parallel computation, which further improves data processing speeds.

Conclusion

Computational storage is an effective solution to edge storage problems. It eliminates a time lag created by moving data from the source to the processor. Data processing now happens faster and more efficiently. Besides, data security is now better because of the limited usage of cloud computing. Even more significantly, computational storage reduces operations costs.

A firm that implements computational storage at the edge experiences a better return on investment. Its teams become more efficient due to quicker data processing at the firm. It obtains real-time data analytics quickly. Such information enables it to undertake an effective decision-making process and eliminates avoidable errors.

Happy learning!

Further reading


Peer Review Contributions by: Willies Ogola