The term fog computing is created by Cisco which is an extension to the edge of an enterprise's network. It is also known as Edge Computing or fogging. Fog computing facilitates the operation of computing, storage and networking services between devices and cloud computing data centers. It is an architect that uses one or more collaborative end-user clients or near user edge devices to carry out a substantial amount of storage, communication, control, configuration, and management.

Purpose of Cloud Computing

Its main goal is to improve the efficiency and reduce the amount of data transported to the cloud for processing, analysis, and storage. Along with improving the efficiency, it is also used for security and compliance purpose. Smart city, smart buildings, smart grid, vehicle networks and software-defined network are some of the popular fog computing appliances. It gives more choices for processing data wherever it is most appropriate to do for the organizations. Sometimes, retrieval of data needs to be quick because of its need for a quick response. It reduces the bandwidth needed compared to what happens when that data is sent all the way back to a data center or cloud for the procession. It can also be used in scenarios where there is no bandwidth connection to send data.

How Fog Computing Works

Edge devices and sensors store data where they do not have the compute and storage resources to perform advanced analytics and machine-learning tasks. On the contrary cloud servers have the ability to do all these things. They can process the data and respond in a timely manner. Along with this, having all the endpoints connecting to and sending raw data to the cloud over the internet can have privacy, security and illegal implications. The processing takes place in a data hub on a smart device or in a smart router or gateway which reduces the amount of data sent to the cloud. Fog networking complements not replaces, cloud computing. Fogging allows for short-term analytics at the edge, and the cloud performs resource intensive, longer-term analytics.

Difference between Fog Computing and Edge Computing

Some people think that both of these are same and use these terms interchangeably but this is not the reality. Both of these techniques are used to bring the intelligence and processing closer to where the data is created. Difference between both is where the intelligence and compute power are placed.

In edge computing, intelligence and power of the gateway or appliance are in devices such as programmable automation controllers. While in fog computation, it is at the local area network. Data is transmitted from endpoints to a gateway where it is then transmitted to sources for processing and return transmission.

Applications of Fog Computing

Fog computing can be used for these scenarios:

Connected Cars: Self-driven and semi-autonomous vehicles will increase a large volume of cars which operate independently and requires a capability to locally analyze certain data in real-time, such as surroundings, driving conditions, and directions. So, fog computing can help them by giving the information quickly.

Real-time Analytics: From manufacturing systems that need to be able to react to events as they happen, to financial institutions that use real-time data to inform trading decisions or check for fraud.

Smart Cities and Smart Grids: Sometimes the data is not approachable quickly. For example, the data is in remote areas, so processing close to where it's created is essential. Other times the data needs to be aggregated from a large number of sensors.

Conclusion                      

Fog computing has helped organizations to increase efficiency, save time and increase the utilities. It has also increased the security aspect in dealing with data. It is kind of computing where the retrieval of data is very quick and easy. This leads to increase the approach of the Internet of things, as a result, increases automation.