Edge Computing

Edge computing is a distributed computing infrastructure that is used to bring a computing unit closer to a data source, such as IoT devices or local servers. By placing computing units close to the data sources, i.e. at the edge of the network (hence Edge), the user gets faster response times, better availability and no bandwidth issues.

Why the need to use Edge Computing?

Edge Computing solves several important problems. Many of them involve delays that arise when data must travel long distances. Companies using cloud solutions are often doomed by the delays that occur when data travels from where it is generated to where it is processed. Edge processing, on the other hand, allows this data to be processed at the place where it is actually created. This increases the user experience for network users and speeds up operations.

How does Edge Computing work?

There are many possibilities for the use of edge computing, they can be very complex systems operating in a gigantic network environment mainly in IT networks. However, the basic concepts remain the same. First of all, client devices connect to the nearest Edge module, where some logic is implemented to process this data.

What could be customer devices connected to the Edge infrastructure?

Customer devices connected to the Edge computing module can be basically anything. The list includes, for example, autonomous robots on industrial plants, IoT sensor suites, specialized CCTV cameras for security systems and much more. Edge computing will help these devices achieve their operational goals without the delays caused by the need for connectivity to a specialized or central server on the plant or the cloud.

Examples of using Edge Computing

Examples of the use of such solutions include sophisticated IoT systems. The increasing demand in recent years for such devices used, for example, for various measurements of environmental conditions, makes it increasingly necessary to process the data collected from them.

In today's world, where entire cities are investing in a variety of IT + IoTinfrastructure to streamline the operations of, for example, municipal companies, one can see an increasing reluctance to cloud solutions. Not surprisingly, after all, customers value the security of their data and the speed of its processing, as well as full control over it. Edge Computing solutions tailored to IoT systems come to the rescue here. Thanks to the extensive infrastructure that aggregates and processes data at the edge of the network, fully secure, autonomous and fast operation is possible.

Other examples where Edge Computing will prove helpful could be:

Using artificial intelligence for robots and autonomous vehicles

The operation of autonomous devices runs smoothly thanks to Edge computing. If data had to be routed to the cloud first, latency would prove too great to operate efficiently.

Controlling the operation of machinery at industrial plants

With the help of Edge computing, it is possible to control various types of equipment in an industrial plant's network. If it were not for data processing at the edge of the network, many production processes would be greatly hampered.

Smart grid

With real-time monitoring and consumption data processing, it is possible to reduce energy consumption.

Smart Office and Smart Home

Using Edge computing, it is possible to create an intelligent living and working space. Starting with simple examples, such as self-adjusting air conditioning and heating systems to room reservation systems. All this is made possible by processing data at the edge of the network.

Security systems

Intelligent AI platforms for managing security vision systems are impacting our daily lives. Processing such data at the edge of the network is needed due to data protection issues and speed of response to emergency situations.

Public transport

Edge Computing systems are present in public transportation vehicles, where they are responsible for determining data on ride safety and controlling in-vehicle systems such as ticket vending machines, displays, etc.

Health care

In modern clinics and hospitals, IoMT systems continuously monitor the condition and health of patients. By using Edge computing, lives can be saved in such cases. All thanks to data processing at the edge of the network.

The Role of Machine Learning

Machine learning, or Machine Learning, deals with the creation of statistical algorithms that are adapted to learn from the data given to it and make predictions or decisions on their own based on the data. Machine learning algorithms can be used successfully in Edge Computing.

With machine learning models, we can introduce additional functionality and flexibility into our application at the edge of the network that was not feasible before.

To support artificial intelligence and machine learning, it is best to use hardware platforms specially prepared for these purposes, which you can read more about in our separate post on these devices: Edge AI Computers

Challenges facing Edge Computing

When building edge data centers, it is very important to pay attention to many things that can significantly affect its operation. First of all, care should be taken to secure such infrastructure. When building edg systems, you should secure them as well as centralized systems.

The best possible components should also be used. Edge devices are often affected by various environmental conditions that are not conducive to their operation, such as high dust levels on an industrial plant.

Thus, these devices should have an adequate level of enclosure protection, preferably a cooling system with no moving parts to negate the possibility of failure and loudness, and should have a user-friendly interface.

Devices that allow the implementation of Edge Computing

At Stovaris, we provide a full spectrum of devices suitable for data edge processing from renowned suppliers from around the world. With our offerings, you will be able to build an Edge system using components from vendors such as Seeed Studio, Vertiv and Milesight

Implementation of Edge Computing

Depending on the application, an edge computing implementation can look different ways. However, some general factors affecting our implementation should be noted first:

Demand for delays

Many business applications need the lowest possible latency. When building mission-critical infrastructure, low latency must be considered. This will affect the type of edge network being built and where it is placed.

Environmental conditions

An important factor to consider is the conditions in which our infrastructure will stand. If it is a facility like an office, factory, etc. Instead of a dedicated data center, issues such as heat, humidity, airflow, security, etc. should be considered.

Available space

When designing an Edge network, it is important to pay attention to the space needed. If a company does not have the needed space, leasing space for infrastructure should also be considered.

Scale

An important factor for the type of edge infrastructure to be deployed is also the number of devices to be deployed. A network deployed in one location will look different, while a network that is to cover thousands of locations will look different.

Automation

To simplify routine processes it is worth considering automating device configuration and updates. In many off-site edge locations, this will be a major time and resource saver.

Remote management

An important aspect is the ability to remotely monitor and manage your locations from a single location. The appropriate choice of solutions should be based on the ease of use of user dashboards and their functionality.

Resistance

Like all other networks, edge infrastructure should be designed to withstand unexpected accidents. This allows us to protect against, for example, power outages and the like.

The Future of Edge Computing

5G networks currently appearing on the market will make Edge Computing continue to develop rapidly. Thanks to the use of very fast processing, it will be possible with the help of 5G to create Egde systems that will be able to reprocess and return data to the user in less than 1 millisecond.

Computing at the edge of the network together with the support of 5G technology and using private 5G networks will be able to help enterprises process data faster than ever before. As a result, the importance of edge technologies will grow significantly in the coming years.

This group of products is based on the companies' solutions:

Eaton | Milesight | Opengear | Seeed Studio | Tripp-Lite | Vertiv