What is edge computing?

Edge computing refers to data processing that's done at the edge of a network.

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 robot arm may send back reports on how many joints it had welded that day or how many gaskets it had picked up and moved onto a conveyor belt. To give a more mundane and everyday example, apps on your phone may relay performance and engagement data back to the developer's data centre (or cloud service) for mass analysis with those of other users of the same product.

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 incoming data pouring into the data centre, rather than the speed with which it needs to be analysed and acted upon. Increasingly, however, connected devices the "things" in the Internet of Things (IoT) -- 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 "thing" itself.

Real life examples of edge computing

Oil rigs provide a good example of how edge computing is used in the real world. Because of their remote offshore locations, they rely on the technology to mitigate lengthy distances to data centre and poor network connections. 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

IDC's report "Edge Computing is Reshaping IoT: Time for European Enterprises to Take Notice" suggests edge computing security is really about IoT security. 

As an extension of a data centre, an 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 can be roped into distributed denial of service (DDoS) attacks, and can 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 IDC.

What next for edge computing?

According to Gartner's Digital Business Will Push Infrastructures to the Edge report, data generated and processed by enterprises outside of a traditional data centre will increase from less than 10% in 2018 to 75% by 2022.

This is hardly surprising given the increasing popularity of the IoT both in business and consumer use. And while we may still be a way off fully-autonomous vehicles those that are on the road already, or will be shortly, still need this type of technology to operate properly.

The analyst house has also predicted in its Hype Cycle for Emerging Technologies 2019 report that additional edge technologies notably AI and analytics will come to play a key role in this technology in the coming five to 10 years.