What is Edge Computing and why is edge computing an escape from the cloud

Possible you read how the oil and gas industry uses IoT to host hundreds and thousands of sensors at its facilities. With so much input data that all these multiple IoT devices transmit, it is no longer feasible to transfer data to the cloud or a shared data Center for processing: some mechanisms for analyzing and responding to data from the IoT can be implemented locally, in a place where this telemetry is removed, and the data itself is produced. That is, the term "Edge Computing “or” Boundary computing "does not mean some physical boundary, not some abstract type of “cutting edge technology”, but that all actions take place “somewhere out there on the ground”, and not in the local data center and not in the cloud.

Typical examples of boundary calculations include:

  • Analysis of video from surveillance cameras or other sources
  • Self-driving cars
  • Robot control

That is, what is long/expensive/pointless to transfer somewhere, loading communication channels at a time when the application is running “in real time”. In addition, in a sense, on-premises data processing saves money by reducing the amount of data transferred and stored in a cloud location. The concept of Edge computing has emerged thanks to the exponential growth of IoT devices that connect to the Internet either to get information from the cloud or to deliver data back to the cloud. And many IoT devices generate huge amounts of data in the course of their operation that are inherently unnecessary after they are processed.


Think of devices that monitor production equipment in a factory, or an Internet-connected video camera that sends real-time video from a remote office. While a single device producing data can transfer it over the network quite easily, with the growth of such devices, there are already problems with delays. Instead of a single video camera transmitting live footage, multiply it by hundreds or thousands of devices. Not only will quality suffer because of the delay, but the bandwidth costs can be huge.

Peripheral computing equipment and services help solve this problem by providing 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 data you need back through the cloud, reducing the need for bandwidth. Or it can send data back to the edge device in case the application needs to run in real time.

What applies to Edge devices

In General, there is nothing new here: from the point of view of “iron” equipment for Edge Computing is more like old-school iron, created before the era of virtualization and the transition to the clouds. “Dte” devices can include a smartphone, a laptop, IoT sensors, a desktop server,and a microwave. Of course, if you have a border laptop, then you should have a border network gateway, through which the local network of the object is connected to the cloud and a centralized data center. As you can guess, this is a normal network gateway, most likely with an integrated controller for controlling Wi-Fi access points.

What is the difference between Edge equipment?

“It is in your office coolers and air conditioners, and we at the facility and the toilet may not be,” - this principle should be guided by the approach to Edge Computing. Where you are processing data, there may not just be no data center, but there may not even be a place to install a server Cabinet. Server, too, may not be, its role may well perform a laptop, workstation or smartphone. That is, if you remember the first offices in which the 1C server was installed right in the accounting Department in the middle of the office to make it easier to pull the wires, you can say that these people foresaw Edge Computing long before it became mainstream.

That is, Edge-equipment distinguishes:

  • Using simple interfaces (RJ45 for twisted pair + WiFi)
  • Compactness, allowing you to place the device even on a stool, at least on the desktop
  • Low noise to work near staff
  • Low power consumption, because a permanent source of electricity can be a generator or a solar panel

A striking example of an Edge server can be considered Lenovo ThinkSystem SE350:


In the photo above you can see as many as three such servers installed on the operator’s desktop in the warehouse. The connection to the network is via Wi-Fi, and even the antennas are folded, obviously so that they are not hooked and broken, and stacking on an ordinary steel plate is like a casual style in clothes: the more brutal, the better. Interestingly, the server is built on Intel Xeon 2100 SoC, andin our review we considered Xeon-D 2143, and it is a very good processor, having up to 16 physical cores + 1 AVX2 block + South bridge + 10G LAN in one case.

In practice, modern processors have become so powerful that Edge Computing can make not only warehouse accounting, but also accounting, reporting, telemetry processing and even machine learning tasks on ready-made models, or where the use of GPU is impractical.

Why Edge Computing is a new trend

For many companies, the use of the cloud is impractical due to the high cost of data channels. Increasingly, the biggest advantage of Edge Computing is the ability to process and store data faster than in the cloud, allowing you to create more efficient real-time applications.

Once upon a time, a smartphone scanning a person’s face would have to run a facial recognition algorithm through a cloud service, which would take a long time. With the edge computing model, the algorithm can run locally on the edge server or gateway, or even in the smartphone itself, given the growing power of smartphones. Applications such as virtual and augmented reality, self-driving cars, smart cities and even building automation systems require rapid processing and response.

“Edge computing has evolved significantly since the days of Robo [Remote Office Branch Office] isolated it centers,” says Cuba Stoljarski, research Director at IDC, in the report " global edge infrastructure (computing and storage) forecast 2019-2023." "With improved connectivity enabling better edge access to more core applications, and with new IoT and industry business precedents, edge infrastructure is poised to become one of the major growth engines in the server and storage market over the next decade and beyond.”
Companies like NVIDIA have recognized the need for more “on-the-ground” processing, so we’re seeing new system modules that incorporate artificial intelligence features. The company’s latest Jetson Xavier NX module, for example, is smaller than a credit card and can be embedded in smaller devices such as drones, robots and medical devices. Artificial intelligence algorithms require a lot of computing power, so most of them work through cloud services.

Privacy and security

However, as with many new technologies, solving one problem can lead to others. As the number of IoT devices grows, it is critical to understand the potential security issues around these devices and to make sure that these systems can be protected. This includes verifying that the data is encrypted and that the correct access control methods are used and even tunneling the VPN.

What about 5G?

Around the world, operators are deploying 5G wireless technologies that promise the benefits of high bandwidth and low latency for applications. Rather than simply offering faster speeds and telling companies to continue processing data in the cloud, many Telecom operators are developing edge computing strategies in their 5G deployments to enable faster real-time data processing, which is especially important for mobile devices, cars and robots.

Clearly, while edge computing’s original goal was to reduce bandwidth costs for IoT devices over long distances, the growth of real-time applications that require local processing and storage will drive technology development in the coming years.