What is Fog Computing?

Fog computing, as described by Cisco, is the practice of extending cloud computing to a network edge within an organization. It also goes by the names Edge Computing and Fogging. It makes it easier for end devices to communicate with computing data centers and for computing, storage, and networking services to operate.

 

In January 2014, Cisco first used the terminology "fog computing." This was due to the fact that fog, which is also referred to as clouds that are close to the ground, was associated with nodes that were present close to nodes that were present between the host and the cloud. 

 

The goal was to close the distance between the host computer and the system's processing power. After it started to acquire some traction, IBM came up with the moniker "Edge Computing" in 2015.

 

By placing certain resources and transactions at the edge of a network, this strategy takes advantage of the potential given by the data such devices create as well as the twin issue of the proliferation of computing devices.

 

Instead of creating in-cloud channels for usage and storage, users can aggregate bandwidth at access points like routers by placing these closer to the devices. 

 

As a result, fewer data must be transported from data centers across long distances and over various cloud routes, which lowers the total bandwidth needed.

 

Another significant distinction between cloud computing and fog computing is data storage. Fog computing allows users to submit data to strategic compilation and distribution rules aimed to increase efficiency and lower costs because less data requires immediate cloud storage.

 

Also Read | How does Hybrid Cloud Storage Work?

 

What is Fog Computing?

 

Fog computing and cloud computing are primarily distinguished by their decentralization and flexibility. Fog computing, also known as fogging or fog networking, refers to a decentralized computer system that is situated between the cloud and data-producing devices. 

 

Users may arrange resources, such as apps and the data they generate, in logical locations to improve efficiency thanks to this flexible framework.

 

The structure's objective is to place fundamental analytic services closer to the point of demand, at the network's edge. Users no longer have to transfer data as far across the network, which enhances performance and increases overall network efficiency.

 

It is simpler to take advantage of the processing capacity already available in such devices by transferring real-time analytics into a cloud computing fog that is situated closer to the devices. 

 

As a result, user experience is enhanced and the pressure on the cloud as a whole is lessened. IoT devices need fog computing more than any other type of device.

 

Security concerns with fog computing also help consumers. By adding more firewalls to the network, users may increase security thanks to the fog computing paradigm's ability to divide bandwidth traffic.

 

Where it originated, cloud computing, fog computing keeps some of its characteristics. Users can continue to use a fog computing paradigm while continuing to keep their apps and data in the cloud and pay for upgrades and maintenance of their data in the cloud in addition to offsite storage. For instance, their employees will still have remote access to the data.

 

Differences between Fog Computing and Edge Computing

 

The cloud is a centralized resource that offers consumers quick, cost-effective access to computing, connection, and storage options. As a result, data and devices that are remote from the centralized cloud may have performance difficulties and delays.

 

1. In order to reduce processing time and distance, edge computing aims to bring data sources and devices closer together. The performance and speed of apps and devices should thus increase as a result.

 

2. Bringing computation to the network's edge is a component of fog computing, a concept coined by Cisco. But it also alludes to the idealized model of how this procedure ought to function.

 

3. Processing latency is eliminated or significantly reduced by relocating storage and computing systems as close as feasible to the applications, parts, and devices that require them. 

 

This is crucial for Internet of Things-connected devices since they produce a tonne of data. Due to their proximity to the data source, those devices have much lower latency in fog computing.

 

4. Fog computing is the standard that provides repeatable, structured, and scalable performance inside the context of edge computing. This is another way to think about the differences between edge computing and fog computing. 

 

5. Data generation, processing, and storage are all done near to one another in edge computing, which is truly a subtype of fog computing. Fog computing incorporates edge processing as well as the required network connections and infrastructure for transferring the data.

 

6. This is due to the fact that while mobile edge computing and fog both seek to decrease latency and increase efficiency, their data processing methods are only marginally different.

 

There is a physical link between the data source and the processing site in edge computing, which often occurs right where sensors are mounted to equipment and collect data.

 

7. Fog computing does reduce the distance between the site of processing and the data source, but it does so by carrying out edge computing operations inside an IoT gateway, fog node, or fog node with LAN-connected processors, or even within the LAN hardware itself. 

 

The physical distance between the processor and the sensors increases as a result, yet there is no increase in latency.

 

Also Read | Edge Computing Vs Fog Computing 

 

Working of Fog Computing

 

Writing IoT applications for fog nodes at the network edge or porting existing IoT apps for fog nodes using fog computing software, a package fog computing programme, or other tools is how fog computing is implemented.

 

Edge nodes are those nodes that are most near the edge and receive data from other edge devices like routers or modems. They then send the data they receive to the best place for analysis.

 

Administrators will determine which data is most time-sensitive before integrating networks for fog and cloud computing. In verified control loops, the most urgently time-sensitive data should be examined as soon after its generation as is practical.

 

Data that can wait longer to be examined will then be passed to an aggregate node by the system. According to the features of fog computing, each form of data simply dictates which fog node is the best place to conduct analysis, depending on the ultimate objectives of the study, the type of data, and the current requirements of the user.

 

The following stages are involved in data transfer through a fog computing architecture in an IoT setting:

 

1. An automation controller reads signals from IoT devices.

 

2. The system programme required to automate the IoT devices is carried out by the controller.

 

3. The control system programme transmits data via different gateway protocols or a typical OPC Foundation server. The OPC interoperability standard for Internet of Things data sharing.

 

4. This information is transformed into a format that internet-based service providers can understand, like MQTT or HTTP (S).

 

5. Data is transformed before being delivered to an IoT gateway or fog node. These endpoints gather the data to be used for additional analysis or send the data sets to the cloud for wider distribution.

 

Fog computing has applications in smart electricity grids. Smart cities must adapt to changing demand, lowering output as necessary to maintain cost-effectiveness, in order to operate effectively. Thus, real-time information on electricity output and consumption is required by smart grids. 

 

These sophisticated utility systems frequently compile information from a large number of sensors or have to withstand remote installations. In any case, fog computing architecture is the best choice.

 

Another use case for fog computing is smart transportation networks. A stream of data is produced by every linked street, traffic gadget, and vehicle on this type of grid.

 

This demonstrates that massive amounts of data analysis performed in real-time are vital to prevent accidents, and a fog computing strategy is crucial to maximizing the use of the available mobile bandwidth.

 

IoT and fog computing in general is a rich field. Another excellent example of how fog computing is used is in connected industrial equipment with cameras and sensors, as well as in real-time analytics-based systems.

 

Also Read | What are Cyber-Physical Systems? 

 

Advantages of Fog Computing

 

The following are the benefits of fog computing:

 

1. Cut down on latency

 

Maintaining analysis near to the data source avoids cascade system failures, manufacturing line shutdowns, and other serious issues, especially in verticals where every second matters. Real-time data analysis enables quicker alerts, less risk to users, and less downtime.

 

2. Reduce network bandwidth use

 

Even crucial studies of large amounts of data don't always require the scale that cloud-based processing and storage can provide. While this is happening, networked devices continuously provide fresh data for study. Most of this bulky data doesn't need to be sent thanks to fog computing, freeing up bandwidth for other important operations.

 

3. Lower operational expenses

 

Lower operational expenses result from processing as much data locally as feasible and preserving network capacity.

 

4. Boost security

 

Protecting IoT data is crucial, whether it is being sent or kept. The same controls, rules, and procedures used across the whole IT environment and attack continuum may be used by users to monitor and defend fog nodes, enhancing cybersecurity.

 

5. Increased dependability

 

Circumstances can be tough since IoT devices are frequently used in emergency situations and challenging environmental conditions. Under these circumstances, fog computing can increase dependability while easing the load on data transmission.

 

6. Increase insights while maintaining the confidentiality

 

Your team may do local analysis on the devices that gather, process, and store the sensitive data instead of transmitting it to the cloud, which runs the risk of a data breach. Due to the nature of data security and privacy in fog computing, more intelligent choices are available for more sensitive data.

 

7. To increase company agility

 

Businesses can only swiftly meet customer demand if they are aware of the resources that consumers require, where those resources are needed, and when those needs are. Developers may create fog apps quickly and deploy them as required thanks to fog computing.

 

Based on current computing capabilities and infrastructure, fog computing technology also enables users to offer more specialized services and solutions to their clients and position data and data tools where they are best handled.

 

One difficulty with fog computing is its dependency on data transit. Although the development of the 5G network has made this problem better, peak congestion, slower speeds, and restricted availability are still problems. Other possible problems at fog nodes that require consideration include speed and security.

 

Fog computing aims to carry out as much processing as possible utilising computing devices that are close to data-generating equipment so that processed data rather than raw data is transferred and bandwidth requirements are reduced