What is the goal of fog computing?

The fogging (Fog Computing) is to improve efficiency and reduce the amount of data transported to the cloud for processing, analysis and storage. This is often done to improve efficiency, though it may also be used for security and compliance reasons.

Popular fog computing applications include smart grid, smart city, smart buildings, vehicle networks and software-defined networks.

The metaphor fog comes from the meteorological term for a cloud close to the ground, just as fog concentrates on the edge of the network. The term is often associated with Cisco; the company’s product line manager, Ginny Nichols, is believed to have coined term. “Cisco Fog Computing” is a registered name; fog computing is open to the community at large.

The OpenFog Consortium was founded in November 2015 by members from Cisco, Dell, Intel, Microsoft, ARM and Princeton University; its mission is to develop an open reference architecture and convey the business value of fog computing.

“Fog Computing and Networking because cloud can only go so far -OpenFog”

How Fog Computing Works?

While edge devices and sensors are where data is generated and collected, they don’t have the compute and storage resources to perform advanced analytics and machine-learning tasks. Though cloud servers have the power to do these, they are often too far away to process the data and respond in a timely manner. In addition, having all endpoints connecting to and sending raw data to the cloud over the internet can have privacy, security and legal implications, especially when dealing with sensitive data subject to regulations in different countries.

In a fog environment, the processing takes place in a data hub on a smart device, or in a smart router or gateway, thus reducing the amount of data sent to the cloud. It is important to note that 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.

Fog Computing vs Edge Computing

Many use the terms fog computing and edge computing interchangeably, as both involve bringing intelligence and processing closer to where the data is created. However, the key difference between the two is where the intelligence and compute power is placed.

In a fog environment, intelligence 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. In edge computing, intelligence and power of the edge gateway or appliance are in devices such as programmable automation controllers.

Proponents of edge computing tout its reduction of points of failure, as each device independently operates and determines which data to store locally and which data to send to the cloud for further analysis. Proponents of fog computing over edge computing say it is more scalable and gives a better big-picture view of the network as multiple data points feed data into it.

Fog Computing and Internet of Things (IoT)

The group has identified numerous IoT use cases that require edge computing including smart buildings, drone-based delivery services, real-time subsurface imaging, traffic congestion management and video surveillance. The group released a fog computing reference architecture in February 2017. Because cloud computing is not viable for many internet-of-things applications, fog computing is often used. Its distributed approach addresses the needs of IoT and industrial IoT, as well as the immense amount of data smart sensors and IoT devices generate, which would be costly and time-consuming to send to the cloud for processing and analysis. Fog computing reduces the bandwidth needed and reduces the back-and-forth communication between sensors and the cloud, which can negatively affect IoT performance.

Conclusion:

Fog Computing aims to reduce processing burden of cloud computing. Fog computing is bringing data processing, networking, storage and analytics closer to devices and applications that are working at the network’s edge. that’s why Fog Computing today’s trending technology mostly for IoT Devices.


Cisco invented the phrase "Fog Computing," which refers to extending cloud computing to an enterprise's network's edge. As a result, it's also known as Fogging or Edge Computing. It makes computation, storage, and networking services more accessible between end devices and computing data centers.

Fog computing is the computing, storage, and communication architecture that employs EDGE devices to perform a significant portion of computation, storage, and communication locally before routing it over the Internet backbone.

Fog computing is a type of distributed computing that connects a cloud to a number of "peripheral" devices. (The term "fog" refers to the edge or perimeter of a cloud.) Rather than sending all of this data to cloud-based servers to be processed, many of these devices will create large amounts of raw data (for example, via sensors).

The goal of fog computing is to conduct as much processing as possible using computing units that are co-located with data-generating devices so that processed data rather than raw data is sent and bandwidth needs are decreased.

Another advantage of processing locally rather than remotely is that the processed data is more needed by the same devices that created the data, and the latency between input and response is minimized.

Application of Fog Computing

  • It's utilized when only a small amount of data has to be sent to the cloud. This data is chosen for long-term storage and is accessed by the host less frequently.

  • It's utilized when a large number of services must be delivered over a broad region and at various places.

  • Fog computing is required for devices that are subjected to demanding calculations and processing.

  • Fog computing is utilized in IoT devices (for example, the Car-to-Car Consortium in Europe), Devices with Sensors and Cameras (IIoT-Industrial Internet of Things), and other applications.

Advantages of Fog Computing

  • The quantity of data that has to be transmitted to the cloud is reduced using this method.

  • Because the distance that data has to travel is decreased, network bandwidth is saved.

  • Reduces the system's reaction time.

  • Because the data is kept near to the host, it increases the system's overall security.

Disadvantages of Fog Computing

  • Increased traffic may cause congestion between the host and the fog node (heavy data flow).

  • When a layer is added between the host and the cloud, power usage rises.

  • It's challenging to coordinate duties between the host and fog nodes, as well as the fog nodes and the cloud.

  • Data management becomes laborious because, in addition to storing and computing data, data transfer requires encryption and decryption, which releases data.

What is the goal of fog computing?

Updated on 17-Aug-2021 14:27:54

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What is the goal of fog computing Cisco?

The goal of fog computing is to improve the efficacy of local and cloud data storage. It reduces the amount of data needed to be sent to the cloud. This boosts data analysis efficiency and the security of IoT.

What is the objective of fog computing platform?

Fog computing is a decentralized computing infrastructure in which data, compute, storage and applications are located somewhere between the data source and the cloud. Like edge computing, fog computing brings the advantages and power of the cloud closer to where data is created and acted upon.

Where is fog computing used?

Fog computing is a decentralized infrastructure that places storage and processing components at the edge of the cloud. Fog computing is defined as a decentralized infrastructure that places storage and processing components at the edge of the cloud, where data sources such as application users and sensors exist.

What is fog computing and why is it important?

Fog computing, also called fog networking or fogging, describes a decentralized computing structure located between the cloud and devices that produce data. This flexible structure enables users to place resources, including applications and the data they produce, in logical locations to enhance performance.