Disruption in storage technology is constant, and the hyperconverged infrastructure (HCI) market is no different. According to a MarketsAndMarkets report, the HCI market is expected to grow 32.9 percent between 2018 and 2023, to $17.1 billion.
The main reason for such an increase in uptake is likely due to the advantages HCI provides companies, including single-pane-of-glass management, greener data centers by reducing the rack space and power required, and improved disaster recovery capabilities to name a few.
The next logical step for HCI to continue accelerating its evolution is to move to the edge of the network. With the growing demand for supercharged instances of data use, such as artificial intelligence (AI), it’s no wonder that enterprises are looking to edge computing and HCI to enable them to catch data from the very beginning of their projects. By amalgamating edge computing with HCI, AI tools are able to make more intelligent decisions.
Why is edge computing important?
Long gone are the days where everything we did was with pen and paper. We’re now faced with digitalization across industries – and what that means is that we’re creating a ton of data, which of course needs to be stored somewhere. More often than not, this data is stored on-site at the edge of a network – not your traditional data center architecture.
What’s key about edge computing is that it takes up much less hardware space than traditional hardware storage. By deploying this infrastructure at the edge of the network, it has the ability to not only handle and compile the data, but also compress the large amount of data so that it can be easily transferred into the cloud or into a centralized data center at another site.
This method grants access for the data to be handled and reviewed closer to where it was created, rather than trying to transmit it further away. This is why edge computing is often used by various distributed enterprises like fast-food restaurants, supermarkets, and petrol stations as well as industrial surroundings such as mines and solar energy plants.
It should be noted that the data collated at the edge of the network is often not being utilized to its full capacity. Take AI, albeit still at the beginning of its journey, calls for vast quantities of resources to develop and train its models. However, with edge computing the data is able to move freely into the cloud. From there the data can be analyzed and the AI models can be trained before then extending it back to the edge. The best way for AI to be optimally used to generate these models is to make use of the data center or the cloud.
One such example of this is the silicon chip company, Cerebras, which dedicates its work to accelerating deep learning. It has recently introduced its new “Wafer Scale Engine” which has been purposefully built for deep learning. This new chip is incredibly fast and 56 times bigger than the largest Graphics Processing Unit. Despite its grand size, however, it does mean that its power consumption is at such a high capacity that most edge deployments would not be able to handle it.
But there is still hope because businesses are able to amalgamate edge computing tasks using hyperconverged infrastructure, enabling them to build and make the most of data lakes. By placing the data within a data lake, companies are able to use this to analyze it against all applications. The machine learning aspect is also able to unveil new insights through the use of its shared data against the diverse applications and devices.
In comparison to edge computing, HCI has made it much easier to use by combining servers, storage, and networking all in one box. Not to mention, it doesn’t face the configuration or networking issues it previously had. To add to this, the platform can administer integrated management for a high quantity of edge devices located in different parts of the country, with various forms of networks and interfaces, and thereby undoubtedly decrease operational expenses.
Taking AI to the next level
The launch of smart home devices, self-driving cars, and wearable technology means AI is already prevalent in our everyday lives. According to Gartner, AI will continue to flourish with 80 percent of smart devices to contain on-device AI capabilities by 2022.
The problem with AI’s data collection, however, is that most of the technology powering it is hugely reliant on the cloud, and therefore can only come to a conclusion based on the data it has access to in the cloud. This results in is a delayed response because the data first has to travel to the cloud, before heading back to the device. In the case of technologies like self-driving cars, which require instantaneous decision making, any lag could result in huge complications.
This is where edge computing has the upper hand to the cloud and could take AI to the next level. Any data required for that AI application is able to reside in close proximity to the device, therefore increasing the speed in which it is able to access and process the data. AI devices which are dependent on data conversion benefit the most from this application because they won’t always be able to connect to the cloud as it requires access to bandwidth and network availability.
Another advantage of amalgamating edge computing with HCI for AI is that it requires a smaller amount of storage space. The best operational feature about HCI is that technology is able to function, within a smaller hardware design. It will soon be commonplace to find companies launching highly available HCI edge compute clusters which are comparable to the size of a cup of tea.
For AI to truly flourish, it fully depends on HCI and edge computing to marry and work together side by side, as this will mean AI can function on its own merit, requiring minimal support. AI will be able to make the most of its deep learning asset, as well as improve its ability to make better decisions.
Technological advances in the cloud have already given AI the ability to be accessible on a vast majority of technological devices such as smart TVs. However, it is the joining of HCI and edge computing which will give AI the means required to advance into unchartered territory, providing more intelligent and efficient methods to find a solution for all companies.
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