Fog computing vs Cloud computing

The decentralized data storage approaches correspond with some of the main IoT needs, such as accessibility, safety, mobility, and real-time processing. Because cloud computing is not viable for many internet of things applications, fog computing is often used. The main difference between fog and edge computing is that fog computing extends cloud services and connectivity to devices at the edge of the network. In contrast, edge computing brings computation and data storage closer to devices at the edge of the network.

What is fog computing

The practice of renting IT resources as cloud infrastructure instead of providing them in-house has been commonplace for some time now. While most solutions like IaaS or PaaS require specific user interactions for administration and scaling, a serverless architecture allows users to focus on developing and implementing their own projects. Such an architecture is pushed beyond its limits with a concept like “smart manufacturing,” because fog computing vs cloud computing it requires that data be continuously exchanged between countless end devices. Fog computing makes use of intermediate processing close to the data source in order to reduce data throughput to the data center. With fog computing, the fog layers act as a middleman between the user and the cloud. This means that the fog engine must know who is requesting the service, and the same authorization process and policies hold good here.

Design for uninterrupted fog services

Fog computing is a decentralized infrastructure that places storage and processing components at the edge of the cloud. Each vehicle has the potential to generate quite a bit of data just on speed and direction, as well as transmitting to other vehicles when it is braking, and how hard. As the data is coming from moving vehicles, it needs to be transmitted wirelessly on the 5.9 GHz frequency in the USA; if not done properly the amount of data could easily overload the finite mobile bandwidth. A key component of sharing the limited mobile bandwidth is the processing of data at the level of the vehicle via a fog computing approach through an on-board vehicle processing unit. Electrical grids these days are quite dynamic, being responsive to increased electrical consumption, and lowering production when it is not needed to be economical.

What is fog computing

Both fog and edge are placed close to endpoints to reduce latency and resources needed to process data in time-sensitive events. Edge and fog computing are less known than cloud but have a lot to offer to businesses and IoT companies in particular. These networks solve many issues that can’t be solved by IoT cloud computing services and adapt the decentralized data storage to particular needs. Let’s examine the benefits of edge, fog and cloud computing individually. Fog computing is useful when the Internet connection isn’t always stable. For instance, on connected trains, fog can pull up locally stored data in areas where the Internet connection can’t be maintained.

Head-to-Head Comparison Between Cloud Computing and Fog Computing

These devices can be used to process data before it is sent to end users. Edge-level fog computingruns on servers or appliances located at the edge of a network. These devices can be used to process data before it is sent to the cloud. Back in the day, mainframe computers with dumb terminals provided all the computing power required to handle transaction processing and other computing needs.

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  • This selected data is chosen for long-term storage and is less frequently accessed by the host.
  • However, fog networking is also flexible because it can be quickly scaled up or down depending on the needs of your organization.
  • The cloud, which is the data center, is too far away from the data source ; sending information and data to the data center for analysis results in a latency that undermines the agility of IoT technologies.
  • The main difference between cloud, fog and edge computing is defined by where data from edge devices is processed and stored.
  • In connecting fog and cloud computing networks, administrators will assess which data is most time-sensitive.

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Why Is Fog Computing Beneficial for IoT?

Intel estimates that the average automated vehicle produces approximately 40TB of data every 8 hours it is used. In this case, fog computing infrastructure is generally provisioned to use only the data relevant for specific processes or tasks. Other large data sets that are not timely for the specified task are pushed to the cloud. Fog device hosting applications can also expect to have the same concerns as current virtualized environments. Trust and privacy are issues to consider, as the fog processes user data in third-party software and hardware.

What is fog computing

It consists of a decentralized environment for computing in which the infrastructure provides storage, applications, data, and computations. Fog computing is a powerful technology used to process data, especially when used in tandem with the cloud. With the sheer amount of data being collected by IoT devices, many organizations can no longer afford to ignore the capabilities of fog computing, but it is also not wise to turn your back on the cloud either. Edge and fog computing doesn’t have the capability to expand connectivity on a global scale like the cloud. To really get the most out of your computing resources, combining cloud and fog computing applications is a great option for your IoT architecture. Fog computing is becoming more popular with industries and organizations around the world.

Components of Fog Computing

But what is fog computing, and how does it differ from cloud computing? The term fog computing was coined by one of Cisco’s product line managers, Ginny Nichols, in 2014. This computing method is called “fog” because it focuses on the network’s edge. After fog computing gained traction, IBM coined a similar term for a similar computing method, edge computing.

Massive amounts of data are constantly being collected from data sources such as connected cars, vehicles, ships, factory floors, roadways, farmlands, railways, etc., and transmitted to the cloud. You have IoT-based systems with geographically dispersed end devices generating data in the order of terabytes, and where connectivity to the cloud is irregular or not feasible. The cloud server performs further analysis on the IoT data and data from other sources to gain actionable business insights. After conversion, the data is sent to a fog node or IoT gateway—which collects, processes, and saves the data or in some cases transfers it to the cloud for further analysis.

Applications of fog computing

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. Offloading occurs when volumes of data cannot be processed remotely in a timely and efficient manner. In these circumstances, the processing of endpoint data is moved to a local node, which preprocesses and filters data, serving some requests autonomously and referring to a cloud server. This is a little like a proxy server, which operates on the local network. The cloud server collects and aggregates processed IoT data from the fog nodes.

What is fog computing

This data explosion has, however, left organizations questioning the quality and quantity of data that they store in the cloud. Cloud costs are notorious for escalating quickly, and sifting through petabytes of data makes real-time response difficult. IEEE adopted the fog computing standards proposed by OpenFog Consortium. Cloud platforms provide tools and services for easy, cost-effective maintenance. Improved User Experience – Quick responses and no downtime make users satisfied. Unfortunately, nothing is spotless, and cloud technology has some drawbacks, especially for Internet of Things services.


In edge computing, data is processed directly on the data sources such as sensors or IoT devices, or on the devices to which the sensors are connected. The OpenFog Consortium, on the other hand, defines edge computing as a component or a subset of fog computing. Consider fog computing to be how data is handled from its inception to its final storage location. Edge computing entails processing data as close to its creation as possible. Fog computing refers to everything from the network connections that bring data from the edge to its endpoint to the edge processing itself. In edge computing, intelligence and power can exist in either the endpoints or gateways.

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