Edge computing, as its name suggests, refers to data processing that’s done at the edge of a network, near the source of the data.
In a network, data is typically sent from devices be that computers, smartphones or assembly-line robots, back to a central data centre for processing and analysis. For example, a robotic arm will send back reports on how many gaskets it had picked up and moved onto a conveyor belt, or to give a more mundane example, apps on your phone may relay performance data back to the developer’s data centre (or cloud service) for mass analysis.
The endpoints and data centre in a traditional network may be located quite far apart, potentially even on different continents. Historically, this hasn’t posed a problem; the “velocity” aspect of big data‘s “three vs” refers to the torrents of data pouring into the data centre, rather than the speed with which it needs to be analysed and acted upon. Increasingly, however, connected devices – such as Internet of Things (IoT) gadgets – require instantaneous, or near-instantaneous feedback.
In these situations, the latency involved in sending data to and from the data centre is too great, even if it’s analysed and turned around immediately. This is where edge computing comes in.
Data processing and analysis in edge computing can be done in a small, on-site data centre (such as a micro data centre) or, increasingly, in the device itself.
Real life examples of edge computing
Oil rigs are a good example of how edge computing is used in the real world. Because they are typically based on remote offshore locations, they rely on the technology to mitigate lengthy distances to the data centre and poor network connections, and it’s also costly, inefficient and time-consuming for rigs to send real-time data to a centralised cloud. Having a localised data processing facility helps a rig to run without delay or interruption
Similarly, autonomous vehicles, which operate with low connectivity, need real-time data analysis to navigate roads. Gateways hosted within the vehicle can aggregate data from other vehicles, traffic signals, GPS devices, proximity sensors, onboard control units and cloud applications, and can process and analyse this information locally.
Security risks at the edge
As an extension of a data centre, edge computing infrastructure naturally increases the surface area exposed to threats. Many edge computing devices aren’t built with traditional IT security protocols, meaning that unsecured endpoints could be roped into distributed denial of service (DDoS) attacks, and could even offer hackers access to the wider network they connect to.
Physical security also needs to be a consideration, if devices are accessible to bad actors or people who could tamper with them.
However, if appropriate precautions are taken, edge computing can actually reduce IoT security and privacy risks by limiting the data flow between the collection point and the core storage centre, according to research from IDC.
What next for edge computing?
Edge computing is an area of technology that is constantly evolving and rapidly growing, and is quickly becoming one of the key components in enterprise infrastructure, according to recent forecast by Gartner. The analyst house predicts that, by the end of 2023, one in five installed edge computing platforms will be delivered and managed by hyper-scale cloud providers, compared to less than 1% last year.
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It also recommended basing an enterprise’s edge computing architecture and topology on use-case requirements as well as opting for edge computing partnerships and ecosystems which are capable of delivering a total solution, as opposed to a single-vendor approach.
Moreover, a 2019 Gartner forecast in emerging technologies found that, in the next five to ten years, AI and analytics will play a prominent role in edge computing. Prior to that, we can expect enterprise data generated and processed outside of a traditional data centre to increase to 75% by 2022, from less than 10% in 2018.
However, this isn’t too shocking, especially if you consider the ever-growing popularity of IoT devices among both businesses and consumers. According to last year’s study by Jupiter Research, the number of Industrial IoT connections will increase from 17.7 billion in 2020 to 36.8 billion in 2025, representing an overall growth rate of 207%.
Edge computing will also be instrumental in the rollout of autonomous vehicles, with a number of tech giants gearing up to tap into this emerging market: Volkswagen recently extended its partnership with Microsoft to further boost the development of its self-driving car software, while last year Amazon snapped up the autonomous vehicle startup Zoox for more than $1 billion.
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See the original article here: ITPro