6th January 2021 Edge Computing

It’s time to get serious about edge computing

At first there were giant computers. Then we connected terminals to those computers. We then developed personal computers. After that came the internet, which changed computing forever.

We now have an even smarter way of computing: the cloud. We work with data remotely from the server, giving us more flexibility to access centralised services and apps like Dropbox and Outlook.

Now data is kept in the cloud for us to access whenever we want. We have certainly come a long way from sticking a CD-ROM into our PC, but are we now over-reliant on big cloud service providers?

What is edge computing?

Edge computing lies at the “edge” of the limits of what cloud can provide. It is a way of computing that works alongside, and in-between, you and the cloud to improve the experience and quality.

Edge computing is also a literal geographic position, located at or near the source. Edge computing brings computation, processing, and data storage closer to the location where it is needed.

The growth of IoT has been the biggest driver in the need for edge computing. We now demand fast, real-time computing power and applications to process, handle, and deliver information.

What are the benefits of edge computing?

As a networking philosophy, edge computing has several benefits, both practical and financial.


Edge computing improves latency, or ‘lag’. This is the delay between computers speaking to each other. If they are very far apart (like the other side of the world), there will inevitably be a delay.

This can be especially noticeable for voice activation, which is typically resolved through the cloud. It would still make a Victorian faint, but to us, we are tapping our fingers and rolling our eyes.

Edge computing models significantly improve latency. It allows algorithms to run locally on an edge gateway or on the device. This works especially well with biometric scanning like facial recognition.


Bandwidth is the volume of information that can be sent over your connection in a measured time. In fact, the cost of bandwidth was one of the early drivers of edge computing for businesses.

The more devices you have sending data to the cloud, the more it struggles to cope with its bandwidth. Edge computing only stores and processes what it needs to, allowing for more devices.

Edge computing can also be used with websites, where edge devices can create a version of the site that you can use offline and save changes, before syncing back to the cloud.

Security and privacy

Edge computing can either be near the source or at source, like the smartphone in your pocket. Its security relies on storing your private info such as fingerprinting and codes onto the device itself.

This might not sound like edge computing, but it is. After all, you’re not managing the hardware and software on a computer yourself, you’re simply accessing the data stored on your device.

These IoT devices have always been a hotbed of security concerns. But edge computing can localise the storage of data to make it harder to hack from a central source and affect multiple devices

How has Covid affected edge computing?

The demand for edge computing has been primarily driven by IoT, 5G, and AR/VR. But the world pandemic has sent millions of people into a remote-working situation. Predictions about steady growth have been thrown out in the face of unprecedented demand for high-speed networks.

The modern workplace

With working from home becoming the new normal for many enterprises, there is now a huge demand for video and online communication. Video conferencing and streaming is at record highs, and there is no room for poor latency. Even the standard 5 milliseconds delay can seem slow now.

Needing better networks Networks are now crucial in reducing lagging, poor resolution, and slow data caching. Edge computing helps by bringing the data ingestion point closer to the source. The more applications that respond in real time, the smoother work communications and data transfers can become.

AI solutions

One solution lies in AI, more specifically machine learning. This powers the orchestration solutions that deliver predictive and scalable operations across workloads. Combining this with real-time network monitoring could provide the insights necessary to power automated tools.

Driving investment

Increasing global mobile data traffic, caused by global digitalization, automation technologies, use of social networking platforms, and IoT devices is driving investments in edge data centres. Placing edge data centres in hotspots of emerging traffic will ease congestion and speed up the service.

The future is edge

Edge computing is changing the way service providers are thinking about their infrastructure, with a big focus on machine learning and AI. But demand breeds innovation, and it is likely that what we thought was not possible with edge computing will be within grasp sooner than we think.

Explore how our vendors have developed a solution to suit the modern way of working.