Introduction
Fog systems are becoming more and more popular in many industrial applications. As a result, understanding the technology, workflow, and other characteristics of fog systems is increasingly important. This paper examines fog system processes and characteristics that impact their success and failure.
Fog System Technology and Workflow
Fog systems are made up of small computing devices or nodes that contain and operate a number of technologies and components. Each node contributes to the computing workload within a distributed and coordinated system. This type of nature of computing splits and distributes data sets over the network of devices for processing, which is then compiled and the results sent back to a single device or the cloud for actionable insights.
The workflow of fog systems follows the same ‘contribution => computation => result’ pattern as distributed architectures. However, the difference lies in where the computation takes place. In fog systems, the nodes take on the computational load while the computing is done “on the edge” of the system, as close to the real-time environment as possible. This allows the system to process data right where it is being received, and to carry out tasks quickly, reduce latency and also meets the performance needs of different types of applications.
Although the nodes in fog systems are often referred to as ‘embedded’ devices, they are actually miniature computers that extend from traditional data centers to the edge of the network. They are equipped with a variety of computing, network and communication capabilities, including graphical processing units (GPUs), field-programmable gate arrays (FPGAs), and even general-purpose processors like the Intel Quark and ARM Cortex.
These components allow the nodes of the system to receive data from sensors and other sources, process the data in real-time and provide actionable insights. The nodes can optionally also act as gateways or bridges that remotely connect the physical systems with cloud services. Furthermore, they also support system scheduling, data storage, and endpoint authentication services.
Characteristics of Fog Systems
Due to their structure and technology, fog systems benefit from several characteristics that make them particularly well-suited for various types of applications. These characteristics include:
Scalability: The nodes of a fog system are designed to be easily added or removed. This allows the system to be scaled to match the available data and processing needs while accommodating a large number of devices, without being limited by the resources of a single device or cloud service.
Low Latency: By processing data near the source, fog systems are able to achieve latency times that are about one-tenth of that of a centralized cloud system.
Real-time Insights: fog systems are able to deliver real-time insights from data sets that have been processed near the source, allowing businesses to get a clear picture of what is happening in their environment and make decisions quickly.
Data Reliability: fog systems store and process data locally, allowing for data redundancy that can be used to ensure the accuracy of results and the overall robustness of the system.
Security: Due to the distributed and distributed nature of fog systems, the risk of a single point of failure is minimized. This ensures that the system is secure and reliable, even in cases where the source nodes are vulnerable.
Conclusion
Fog systems are an increasingly popular type of distributed computing architecture that combines a wide range of technologies and components, to provide an efficient and scalable solution for processing large data sets in real-time. The characteristics of fog systems, from scalability to low latency and security, make them a valuable component in many industrial applications, allowing businesses to get meaningful insights from complex and quickly changing data.
