Edge Computing brings computation and data storage closer to the devices where it’s being gathered, rather than relying on a central location that can be thousands of miles away. This is done so that data, especially real-time data, does not suffer latency issues that can affect an application’s performance. In addition, companies can save money by having the processing done locally, reducing the amount of data that needs to be processed in a centralized or cloud-based location.
Gartner defines edge computing as “a part of a distributed computing topology in which information processing is located close to the edge – where things and people produce or consume that information.”
Ubiquitous computing is a concept in software engineering and computer science where computing is made to appear anytime and everywhere. In contrast to desktop computing, ubiquitous computing can occur using any device, in any location, and across any format.
And we are probably seeing this in our own everyday life. For example, at home, we might be using an Alexa device from Amazon or we might be using Google home. We might even have an intelligent fridge or a car we can talk to.
As companies increasingly leverage ubiquitous computing to support multiple types of applications and systems, a massive amount of data is generated for decision making. However, sending all the data to the cloud can result in latency. Edge computing can drive sub-second responses by moving both computing and data closer to the user. This will reduce latency, minimize data threats, and boost bandwidth. Here are some interesting use cases across industries:
Evolution of Computing
To understand Edge Computing, we need to travel back a few decades and see how Computing has evolved in the past 50 years. The below picture provides a quick recap of the evolution of Computing.
How Edge Computing works
Edge computing was developed due to the exponential growth of IoT devices, which connect to the internet for either receiving information from the cloud or delivering data back to the cloud. And many IoT devices generate enormous amounts of data during the course of their operations.
Think about devices that monitor manufacturing equipment on a factory floor or an internet-connected video camera that sends live footage from a remote office. While a single device producing data can transmit it across a network quite easily, problems arise when the number of devices transmitting data at the same time grows. Instead of one video camera transmits live footage, multiply that by hundreds or thousands of devices. Not only will quality suffer due to latency, but the costs in bandwidth can be tremendous.
Edge-computing hardware and services help solve this problem by being a local source of processing and storage for many of these systems. An edge gateway, for example, can process data from an edge device and then send only the relevant data back through the cloud, reducing bandwidth needs. Or it can send data back to the edge device in the case of real-time application needs.
These edge devices can include many different things, such as an IoT sensor, an employee’s notebook computer, their latest smartphone, the security camera, or even the internet-connected microwave oven in the office break room. Edge gateways themselves are considered edge devices within an edge-computing infrastructure.
Why does Edge Computing matter
For many companies, the cost savings alone can be a driver towards deploying an edge-computing architecture. Companies that embraced the cloud for many of their applications may have discovered that the costs in bandwidth were higher than they expected.
Increasingly, though, the biggest benefit of edge computing is the ability to process and store data faster, enabling more efficient real-time applications that are critical to companies. Before edge computing, a smartphone scanning a person’s face for facial recognition would need to run the facial recognition algorithm through a cloud-based service, which would take a lot of time to process. With an edge computing model, the algorithm could run locally on an edge server or gateway, or even on the smartphone itself, given the increasing power of smartphones. Applications such as virtual and augmented reality, self-driving cars, smart cities, even building-automation systems require fast processing and response.
Computing as close as possible to the point of use has always been important for applications requiring low-latency data transmission, very high bandwidth, or powerful local processing capabilities — particularly for machine learning (ML) and other analytics.
Here are some interesting use cases across industries:
Use Case (a) Autonomous vehicles
One of the leading current uses is for autonomous vehicles, which need data from the cloud. If access to the cloud is denied or slowed, they must continue to perform; there is no room for latency. The amount of data produced by all sensors on a vehicle is prodigious and must not only be processed locally, but anything sent up to the cloud must be compressed and transmitted on an as-needed basis to avoid overwhelming available bandwidth and taking precious time. IoT applications in general are important drivers of edge computing because they share a similar profile.
Use Case (b) In-hospital patient monitoring
Healthcare contains several edge opportunities. Currently, monitoring devices (e.g. glucose monitors, health tools, and other sensors) are either not connected, or where they are, large amounts of unprocessed data from devices would need to be stored on a 3rd party cloud. This presents security concerns for healthcare providers.
An edge on the hospital site could process data locally to maintain data privacy. Edge also enables right-time notifications to practitioners of unusual patient trends or behaviours (through analytics/AI), and the creation of 360-degree view patient dashboards for full visibility.
Use Case (c) Remote monitoring of assets in the oil and gas industry
Oil and gas failures can be disastrous. Their assets, therefore need to be carefully monitored.
However, oil and gas plants are often in remote locations. Edge computing enables real-time analytics with processing much closer to the asset, meaning there is less reliance on good quality connectivity to a centralized cloud.
Privacy and Security
However, as is the case with many new technologies, solving one problem can create others. From a security standpoint, data at the edge can be troublesome, especially when it’s being handled by different devices that might not be as secure as a centralized or cloud-based system. As the number of IoT devices grows, it’s imperative that IT understand the potential security issues around these devices, and make sure those systems can be secured. This includes making sure that data is encrypted, and that the correct access-control methods are implemented.
What about 5G
Around the world, carriers are deploying 5G wireless technologies, which promise the benefits of high bandwidth and low latency for applications, enabling companies to go from a garden hose to a firehose with their data bandwidth. Instead of just offering faster speeds and telling companies to continue processing data in the cloud, many carriers are working edge-computing strategies into their 5G deployments to offer faster real-time processing, especially for mobile devices, connected cars, and self-driving cars.
The Future of Edge Computing
Shifting data processing to the edge of the network can help companies take advantage of the growing number of IoT edge devices, improve network speeds, and enhance customer experiences. The scalable nature of edge computing also makes it an ideal solution for fast-growing, agile companies, especially if they are already making use of colocation data centers and cloud infrastructure.
By harnessing the power of edge computing, companies can optimize their networks to provide flexible and reliable service that bolsters their brand and keeps customers happy.
Edge computing offers several advantages over traditional forms of network architecture and will surely continue to play an important role for companies going forward. With more and more internet-connected devices hitting the market, innovative organizations have likely only scratched the surface of what’s possible with edge computing.