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Difference Between Cloud Computing And Fog Computing

The Crosser Platform enables real-time processing of streaming, event-driven or batch data for Industrial IoT and Intelligent Workflows. It is the only platform of its kind that is purpose-built for Industrial and Asset Rich organizations. He has worked with web and communication in Sweden and internationally since 1999. Since 2012, Johan has been focusing on real-time communication, and the business and operational benefits that comes with analyzing streaming data close to the data sources. The Edge Analytics software is deployed on an IoT gateway on a remote unit, or embedded, and processes the sensor data from that single unit.

fog vs cloud computing

“More infrastructure is needed and you are relying on data consistency across a large network,” he said. Because the distance that data has to travel is decreased, network bandwidth is saved. Expert in architecting and implementing cloud-based infrastructure solutions.

On the other hand, Fog computing cannot produce data, making it inoperative without Edge computing. Each of these computing methods can enable autonomous operations. Even in locations where connectivity is intermittent, or bandwidth is limited, these two technologies can still process data locally. Both technologies keep data closer to where it originated and perform computations usually done in the cloud. This means that both Edge and fog computing can rely less on cloud-based platforms for data analysis, which, in turn, minimizes latency. Simply put, Edge computing takes data storage, enterprise applications, and computing resources closer to where the user physically consumes the information.

Connecting all the endpoints directly to the cloud is often not an option. Sending raw data over the internet can have privacy, security and legal implications besides the obvious cost impact of bandwidth and cloud services. According to the OpenFog Consortium https://globalcloudteam.com/ started by Cisco, the key difference between edge and fog computing is where the intelligence and compute power are placed. One of the approaches that can satisfy the demands of an ever-increasing number of connected devices is fog computing.


Edge devices, sensors, and applications generate an enormous amount of data on a daily basis. The data-producing devices are often too simple or don’t have the resources to perform necessary analytics or machine-learning tasks. The main idea behind Fog computing is to improve efficiency and reduce the amount of data transported to the cloud for processing, analysis and storage. But it also used for security, performance and business logical reasons.

fog vs cloud computing

Networks on the edge provide near-real-time analytics that helps to optimize performance and increase uptime,” Anderson said. Edge computing and fog computing are two potential solutions, but what are these two technologies, and what are the differences between the two? 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.

What Is Fog Computing?

Workload should be categorized into monitoring, analyzing, and execution. Edge computing combined with IoT technology saves you bandwidth, thereby allowing you to choose where to best dedicate your resources. In contrast, Fog computing can’t exist without Edge computing because it can’t produce data alone.

Edge computing is a modern computing paradigm that functions at the edge of the network. It allows client data to be processed closer to the data source instead of far-off centralized locations such as huge cloud data centers. The term fog computing, originally coined by the company Cisco, refers to an alternative to cloud computing. That is, the proliferation of computing devices and the opportunity presented by the data those devices generate .

fog vs cloud computing

In 2015, Cisco partnered with Microsoft, Dell, Intel, Arm and Princeton University to form the OpenFog Consortium. Other organizations, including General Electric , Foxconn and Hitachi, also contributed to this consortium. The consortium’s primary goals were to both promote and standardize fog computing. The consortium merged with the Industrial Internet Consortium in 2019. The front end is the user side, which allows accessing data present in the cloud over the browser or the computing software.

Fog Computing Vs Cloud Computing: Difference Between The Two Explained

A cloud-based application then analyzes the data that has been received from the various nodes with the goal of providing actionable insight. Popular fog computing applications include smart grids, smart cities, smart buildings, vehicle networks and software-defined networks. Fogging, also known as fog computing, is an extension of cloud computing that imitates an instant connection on data centers with its multiple edge nodes over the physical devices. In cloud computing, data processing takes place in remote data centers. Fog processing and storage are done on the edge of the network close to the source of information, which is crucial for real-time control.

Connect to existing PLCs/PACs and legacy systems, as well as directly to sensors and actuators. Data privacy and security is more straightforward to implement locally. Reduced latency, so your apps usually function smoothly when working with real-time data. Remote data access that allows workers to collaborate from any country or device.

fog vs cloud computing

Fog also allows you to create more optimized low-latency network connections. Going from device to endpoints, when using fog computing architecture, can have a level of bandwidth compared to using cloud. Then the data is sent to another system, such as a fog node or IoT gateway on the LAN, which collects the data and performs higher-level processing and analysis. This system filters, analyzes, processes, and may even store the data for transmission to the cloud or WAN at a later date.

Application Of Fog Computing

From smart voice assistants to in-store beacons, brands are experimenting with touch points in a bid to improve the customer experience and collect data in new and inventive ways. Avoid the cost and maintenance hassles; save space and complexity. Handle all edge computing and data communication needs in the same compact, industrially hardened controller that runs your system. Improve processes and reduce costs by analyzing the data you’ve acquired. See patterns and predict future needs through advanced analytics. Based on the data and application, there are three types of cloud computing.

  • It means in future, immense volume of data will transfer between the cloud and data sources and essentially, current network infrastructures cannot cope with that amount of data.
  • The potential for Software as a Service pricing structures, which makes expensive software scalable and remarkably affordable.
  • The Crosser Platform enables real-time processing of streaming, event-driven or batch data for Industrial IoT and Intelligent Workflows.
  • With Edge computing, data is analyzed on the sensor itself or the actual device.
  • Typically on a factory shop floor or building with multiple machines.
  • Data is distributed so the local data might remain safe if the data center gets compromised.

Remember, the goal is to be able to process data in a matter of milliseconds. An IoT sensor on a factory floor, for example, can likely use a wired connection. However, a mobile resource, such as an autonomous vehicle, or an isolated resource, such as a wind turbine in the middle of a field, will require an alternate form of connectivity. 5G is an especially compelling option because it provides the high-speed connectivity that is required for data to be analyzed in near-real time. Because an autonomous vehicle is designed to function without the need for cloud connectivity, it’s tempting to think of autonomous vehicles as not being connected devices.

What Is The Difference Between Edge Computing And Fog Computing?

The main advantages of both these computing methods are improved user experience, systematic data transfer, and minimal latency. Start-up costs for fog computing mean additional expenses on the hardware front since fog computing needs to utilize both the Edge and the cloud. One of the significant differences between Edge and fog computing is where computation and data analysis occur. Additionally, Edge and fog computing offer increased security and privacy by encrypting data. They can also identify potential cyber-attacks and put security measures into place quickly. This assessment determines whether or not the data is important enough to send to the cloud.

In traditional IoT cloud architecture, all data from physical assets or things is transported to the cloud for storage and advanced analysis. The Edge Analytics software is typically deployed on an IoT gateway and processes the sensor data from multiple field fog vs cloud computing units. The metaphorfogoriginates from the idea of a cloud closer to the ground. During 2015 Microsoft, Cisco, Intel and a couple of other enterprises were gathered in a joint consortium to push for the idea of Fog Computing, called Open Fog Consortium.

It works by cutting down the work of both the Edge and the cloud, taking on specific processing tasks from the two. In essence, when Edge computing is employed, data is not transferred anywhere. This cuts costs and allows data to be analyzed in real-time, optimizing performance.

The data is processed at the end of the nodes on the smart devices to segregate information from different sources at each user’s gateways or routers. It establishes a missing link between cloud computing as to what data needs to be sent to the cloud and the internet of things and what data can be processed locally over different nodes. The relationship between edge computing and Industry 4.0 is fascinating to me. Now I understand the actual difference between standard cloud computing and fog computing. I understood cloud computing, but fog was something I was not familiar with. The section talking about how fog is a mediator between hardware and remote servers was helpful.

Fog computing enables real-time analytics in healthcare sector in order to provide the fast and accurate treatment to patients. Still the cloud services can be used to manage and analyse large volume of data. In fact, fog computing can be consider as an extension to the cloud computing. Many research firms have predicted that, in future, billions of internet-enabled devices will be connected to the internet. Gartner, a research company forecast that, by 2020, more than 20 billion of IoT devices will be connected.

Customer Experience

Cloud has different parts like front end platform (e.g. mobile device), back end platforms , cloud delivery, and network . The fog has some additional features other than the ones provided by the cloud’s components which enhance its storage and performance at the end gateways. In today’s age of ransomware and widespread cyber attacks, your cloud data needs to be protected. Increased traffic may cause congestion between the host and the fog node . Edge computing simplifies this communication chain and reduces potential points of failure. A hybrid cloud gives more flexibility by allowing data and application sharability between private and public cloud.

As edge computing moves the computing services like storage and servers closer to end-user or source of data, data processing becomes much faster with lower latency and also saves bandwidth. Because cloud computing is not viable for many internet of things applications, fog computing is often used. Fog computing reduces the bandwidth needed and reduces the back-and-forth communication between sensors and the cloud, which can negatively affect IoT performance. Fog networking complements — doesn’t replace — cloud computing; fogging enables short-term analytics at the edge, while the cloud performs resource-intensive, longer-term analytics. Although edge devices and sensors are where data is generated and collected, they sometimes don’t have the compute and storage resources to perform advanced analytics and machine learning tasks.

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