What is Fog Computing?
Fog Computing is a decentralized IT infrastructure in which data, computing, storage, and applications locate between the data source and the cloud. Like edge computing, fog computing brings the benefits and power of the cloud to the place where data is created and used.
Many people use Fog Computing and Edge Computing interchangeably, as both involve bringing intelligence and processing closer to where the information makes. This often does to improve efficiency, although it can also do for security and compliance reasons.
The fog metaphor comes from the meteorological term for a cloud close to the ground, much like fog is concentrated at the edge of the web. The time is often associated with Cisco; The company’s product line director, Ginny Nichols, has coined the term. Cisco Fog Computing is a registered name; Computing in the fog is open to the entire community.
History of Fog Computing
In 2015, Cisco partnered with Microsoft, Dell, Intel, Arm, and Princeton University to form the OpenFog consortium. Other organizations, including General Electric (GE), Foxconn, and Hitachi, also contributed to this consortium. The main goals of the consortium were to promote and standardize computing in fog. The consortium merged with the Industrial Internet Consortium (IIC) in 2019.
Advantages of Fog Computing
Developing computing frameworks in the fog gives organizations more options to process data where it is most appropriate to do so. For some applications, data may need to process as quickly as possible, for example, in an industrial use case where connected machines need to respond to an event as soon as possible.
It can make low-latency network connections between plans and analytics endpoints. It can also be used in scenarios with no bandwidth connection to send data. So it needs to be processed close to where it was created. This architecture, in turn, decreases the amount of bandwidth required compared to whether that data had to send back to a data center or the cloud for processing.
How Fog Computing Works?
The complete fog network does not replace cloud computing; Fog enables short-term scans at the edge while the cloud performs resource-intensive long-term scans. Although edge devices and sensors are where data generate and collected.
They sometimes do not have the computing and storage resources to perform advanced analytics and machine learning tasks. While cloud servers have the power to do this, they are often too far away to process data and respond on time.
In addition, the fact that all devices connect and send raw data to the cloud over the Internet can have legal, security, and privacy implications. Especially when it comes to sensitive data subject to regulations in different countries. Its popular applications include intelligent grids, smart cities, intelligent buildings, vehicle networks, and software-defined networks.
Fog Computing vs Edge Computing
According to the Open Fog consortium launched by Cisco, the main difference between edge computing and it is the intelligence and computing power location. In a strictly foggy environment, intelligence resides on the local area network (LAN). Data pass from endpoints to a foggy gateway, where it then gives to sources for processing and transmission back.
In edge computing, intelligence and power can find at the endpoint or a gateway. Proponents of advanced computing praise its reduced points of failure, as each device works independently. And determines what data to store locally and what data to send to a gateway or the cloud for more detailed analysis.
Proponents of computing in the fog versus computing on edge say it is more scalable. And offers a better overview of the network because multiple data points feed. However, it should note that some network engineers consider being simply a Cisco brand for one approach to edge computing.
Applications of Fog Computing
It is the initial implementation stage in traditional deployments, but various use cases identify as possible ideal situations for it.
Connected Cars: The arrival of semi-autonomous and self-driving cars will only add to the already large amount of data created by vehicles. Running cars independently requires analyzing real-time data locally, such as surroundings, driving conditions, and directions. Other data may need to submit to a manufacturer to help improve vehicle maintenance or track vehicle usage. A hazy IT environment would allow communications for all of these data sources at the periphery (in the car) and its end (the builder).
Smart Cities and Smart Grids: Like linked cars, usefulness systems increasingly use real-time data to run systems more efficiently. Sometimes this data is in distant areas, so it is essential to process it close to its creation. Other times, data must aggregate from a large number of sensors. Cloud computing architectures could design to solve both of these problems.
Real-Time Analysis: Many use cases require real-time analysis. From manufacturing systems that need to react to events as they occur, financial institutions use real-time data to inform business decisions or monitor fraud. Cloud computing implementations can help facilitate the transfer of data between where it creates and a variety of places it needs to go.
Fog Computing and 5G Mobile Computing
Some experts believe that the planned launch of 5G mobile connections in 2018. And beyond could create more opportunities for it. “5G technology in some cases needs very dense antenna deployments.” In some circumstances. The antennas must be within 20 kilometers of each other. In a use case like this, a cloud computing architecture could create between these stations.
Conclusion
Fog computing is the idea of a network structure that stretches from the outer edges of where data create to where it will eventually store, whether in the cloud or the center of customer data.
Fog is another layer of a dispersed network environment. And is closely associated with cloud computing and the Internet of Things (IoT). Public Infrastructure as a Service (IaaS) cloud providers can view as a high-level global endpoint for data. The edge of the network is where IoT devices create.