What is Fog Computing?

Fog computing, also known as fogging, is a decentralised infrastructure that is located between the Cloud and the data source. The cloud is like a cloud, centrally hovering over all end devices, and Fog Computing is like a fog, closer to the end devices. By processing and storing data in mini data centres (the Fog Nodes) on site, there is no longer a need to route all data to the cloud. Fog computing thus brings the advantages and performance of the cloud closer to the end devices and thus offers Reduced latency and processing times and also lowers bandwidth utilisation through the pre-processed data volumes.

How does Fog Computing work?

Fog computing uses so-called Fog Nodes, which act between the cloud and the end devices. These Fog Nodes act as mini data centresto store and / or analyse collected data from the end devices. This means that not all data has to be sent to the cloud, but can be Used closer to the location of the data source to ensure real-time decisions. For complex analyses, the data is forwarded to the cloud. This structure of Fog Nodes can be seen as a local cloud. The Fog Nodes can interact and communicate with each other.

What are applications and examples of Fog Computing?

IoT and IIoT

Since large amounts of data are generated by sensors and control devices, Fog computing is very important for the IoT (Internet of Things) makes a lot of sense, just like the IIoT (Industrial Internet of Things). Through the Fog Nodes, data is already processed on site, which means less data has to be sent to the cloud. This saves time and money, as the communication between the end devices and the Fog Nodes is faster and enables timely decisions.

Autonomous driving

For the autonomous driving becomes a Combination of Fog and Edge computing used. Large amounts of data are generated by control units, sensors and actuators, up to 20 terabytes per day are possible. By using Fog Computing, a local data analysis (code to data) is carried out in a mobile mini computer centre, the data is evaluated on site and only the results are forwarded. By processing the required data in real time, quick decisions are possible, because delays can be life-threatening in ongoing road traffic.

Fog Computing vs. Cloud Computing

Fog computing complements the Cloud computing and can thus be seen as an intermediary of the cloud infrastructure. While in cloud computing the data is processed in a central IT structure, the cloud, in fogging this is done in the fog nodes closer to the data source. This means that In Fog Computing, short-term and real-time analyses are possible, while in the cloud, time- and resource-intensive analyses of big data are possible. (in English Big Data) take place. Fog computing thus figuratively brings the cloud closer to the end devices and offers faster decisions and shorter latency times.

Fog Computing vs. Edge Computing

Fog and edge computing are often used as synonyms, although they describe different approaches. Edge computing describes decentralised data processing at the edge of the network. Here, the data generated at the end device is pre-filtered and, if necessary, simple analyses are made. This data can then be forwarded to Fog Nodes to be stored or further analysed, for example. Since the Fog Nodes can communicate with each other and more computing power is available, more complex analyses are possible than with edge computing.

Fog, edge and cloud computing work particularly well together. First, edge computing is used to pre-filter and reduce the amount of data. Then, initial analyses are carried out in the Fog Nodes and finally, time-consuming and complex tasks are handled by Cloud Computing. In this way, the respective strengths of the different models can be profited from.