Siva Prasanna
Cloud computing is storing and processing of data in data centres which are placed in far away location. With the introduction of IOT, the data generated from many devices is large. Hence fog computing came into existence. Fog computing is a distributed infrastructure, some of the applications processes will be managed by the smart devices present at the edge of the network. While other applications are still managed in the cloud. In simple terms, all the process which needs to be done within the cloud is replaced with smart devices which are placed at the edge of the network. For the most efficient processing of data ‘fog’ is acting as a middle layer between cloud and hardware. It will reduce the amount of data transferring to the cloud. Fog computing is also bringing an additional layer of security to the cloud thus acting as a firewall.
Origin of fog computing:
‘Cloud’ refers to be present in the sky whereas ‘fog’ refers to be present near to the earth.’ Fog computing’ is associated with Cisco which is often referred to as ‘Cisco Fog Computing’.The term ‘edge computing’ was introduced in OpenFog Consortium which is lead by 4 foundation members Cisco, Dell, ARM, Intel, Microsoft and Princeton University.
Benefits of fog computing:
More choices for processing the data is made possible for organisations with the development of fog computing frameworks. For example, some applications need to be responded quickly like connected machines which some takes have to respond quickly for the incident. Low latency network connections are established between analytical endpoints and devices. Even with a slow network connection, the data can be sent to analytical endpoints which are not possible with data centres. With an added benefits network can be protected with an extra layer of security firewall.
Applications of fog computing:
1.Connected cars:
Self-driven or self-autonomous cars are now available in the market and they produce a large amount of data. The data needs to be analysed and processed quickly based on the information provided like traffic, driving conditions, climate etc., All this data is processed quickly with the help of fog computing. Other data like vehicle maintenance, tracking is sent directly to the manufacturer. Both edge and endpoint communication is made possible with the help of connected cars.
2.Smart grids and smart cities:
For effectively running of systems, utility systems are using real-time data. It is essential to process the remote data close to the place where it is created. It is also possible the data is generated from many sensors. Fog computing is designed in such a way that it can sort both the issues.
3.Real-time analytics:
Data can be transferred from the place it is created to different places using fog computing deployments. Fog computing is used for real-time analytics which transfers the data from manufacturing systems to financial institutions which use real-time data.
The smart electric grid is the best example of grid computing. Electrical grids are smart and dynamic these days. It will be responsive while needing less production and electrical consumption. Fog computing is ideal in a situation where the data is generated from a remote location, it can be processed there itself than to carry it to data centres. Some of the data may be generated from single sensors or a group of sensors and it can be processed there to avoid overloading of the cloud. Electric meters is one best example of this.
Car-to-Car Consortium in Europe, next-generation smarter transportation network in the US comes under the fog computing into IoT applications. It provides the smooth moment of traffic with the help of ‘internet of vehicles’ where each vehicle and traffic environment devices are IOT. The data produces from these IoT devices will help to make a safer moment of our vehicle in the traffic. Data comes from a moving vehicle, it needs to be sent wirelessly at a frequency of 5.9 GHz in the US, if not done properly, the amount of data can easily burden limited cellular bandwidth. Each vehicle has the potential to produce little data only at speed and direction, and transmits to other vehicles when braking, and how hard. The main component of sharing limited mobile bandwidth is processing data at the vehicle level through a mist computing approach through the installed vehicle processing unit.
How fog computing works:
Fog computing contains various devices like fog computing gateway which accepts data from IoT devices. A variety of wired and wireless endpoints which includes switching equipment and ruggedized routers. Gateways and Customer premise equipment can access the edge nodes. OpenFog Consortium, the group that developed the reference architecture, has outlined three objectives for developing a fog framework. The fog environment must be scalable horizontally, meaning that it will support many cases of industrial vertical use, can work across the cloud to various things; and become system-level technology, which extends from various things, crosses network boundaries, to the cloud and crosses various network protocols. Fog computing architectures may sometimes touch routers and core networks eventually servers and global cloud services.
Cloud computing along with fog computing will leads to a hybrid approach. Fog computing will extend the concept of cloud computing lead to a network edge. Real-time interactions of IoT devices is ideal with fog computing.
