Recent News

Edge Computing vs. Fog Computing: 10 Key Comparisons – Toolbox

Edge computing and fog computing can both be defined as technological platforms that bring computing processes closer to where data is generated and collected from. This article explains the two concepts in detail and lists the similarities and differences between them.

Table of Contents

Edge computing and fog computing can both be defined as technological platforms that bring computing processes closer to where data is generated and collected from. This article explains the two concepts in detail and lists the similarities and differences between them.

Edge vs. Fog Computing

As the name implies, edge computing occurs exactly at ‘the edge’ of the application network. In terms of topology, this means that an ‘edge computer’ is right next to or even on top of the endpoints (such as controllers and sensors) connected to the network. The data is then either partially or entirely processed and sent to the cloud for further processing or storage.

However, edge computing can lead to large volumes of data being transferred directly to the cloud. This can affect system capacity, efficiency, and security. Fog computing addresses this problem by inserting a processing layer between the edge and the cloud. This way, the ‘fog computer’ receives the data gathered at the edge and processes it before it reaches the cloud.

Fog computing also differentiates between relevant and irrelevant data. While relevant data is sent to the cloud for storage, irrelevant data is either deleted or transmitted to the appropriate local platform. As such, edge computing and fog computing work in unison to minimize latency and maximize the efficiency associated with cloud-enabled enterprise systems.

IT personnel commonly view the terms edge computing and fog computing as interchangeable. This is because both processes bring processing and intelligence closer to the data source.

Cloud Computing vs. Fog Computing vs. Edge Computing

Edge computing defined

Edge computing brings processing and storage systems as close as possible to the application, device, or component that generates and collects data. This helps minimize processing time by removing the need for transferring data to a central processing system and back to the endpoint. As a result, data is processed more efficiently, and the need for internet bandwidth is reduced. This keeps operating costs low and enables the use of applications in remote locations that have unreliable connectivity. Security is also enhanced as the need for interaction with public cloud platforms and networks is minimized. Examples of edge devices are sensors, laptops, and smartphones.

Edge computing is useful for environments that require real-time data processing and minimal latency. This includes applications such as autonomous vehicles, the internet of things (IoT), software as a service (SaaS), rich web content delivery, voice assistants, predictive maintenance, and traffic management.

Naturally, edge computing is not a replacement for the cloud. In fact, these two technologies work with each other to add value through data. In edge networks, cloud computing is often dedicated to completing tasks that require more computing power, such as large-scale artificial intelligence (AI) and machine learning (ML) operations.

In traditional business applications, endpoints such as employees’ computers are used to collect or produce data. The data is then transmitted to an enterprise application using some combination of local area networks (LAN) and wide area networks (WAN) such as the internet. Once the data is processed, the output is transmitted back to the endpoint.

However, the number of devices connected to enterprise networks and the volume of data being generated by them are scaling at a pace that is too rapid for traditional data centers to keep up with. In fact, Gartner projects that 75% of enterprise data will be generated outside of centralized systems by 2025. Such a situation could lead to tremendous strain on both local networks and the internet at large.

To address this threat of congestion and help enhance the reliability of big data processing systems, IT infrastructure has evolved to bring computing resources to the point of data generation. Edge computing removes the reliance on a single, centralized data processing center. Instead, it makes computing more efficient by bringing data centers closer to where they are actually needed.

Fog computing defined

Fog computing places a decentralized enterprise computing layer between the source of data and a central cloud platform. Like edge computing, fog computing also brings the processing power closer to where the data is extracted from. While fog computing enhances efficiency, it can also be leveraged for cybersecurity and regulatory compliance. The term ‘fog computing’ was coined by Cisco — just like fog is formed close to the ground, fog computing takes place close to the network edge.

Wearable smart devices such as fitness trackers are an excellent example of fog computing. Such devices rely on linked smartphones to process the data they collect and instantly show the output to the user. This removes the need for these devices to transmit data to a remote cloud platform that the manufacturer would probably need to create and maintain.

Like edge computing, fog computers are not meant to replace cloud computing. Instead, ‘fogging’ complements the cloud by performing less intensive analytics and processing tasks at the edge. This reduces the pressure on the cloud and allows it to focus on more long-term, resource-intensive tasks. Numerous fog computers process data in real time and create analytical summaries. This metadata is then shared with a central cloud platform, where it is analyzed to generate actionable insights.

Edge devices that generate or extract data sometimes lack the storage and computing power required to carry out advanced processing tasks, such as those related to machine learning and analytics. The cloud has the necessary computing power to accomplish these tasks; however, it may be placed too far away to do so efficiently enough to meet the needs of certain applications. This is where fog computing bridges this gap.

Additionally, transmitting raw data to a remote cloud server using the internet may go against the regulations of certain jurisdictions. Fog computing addresses such concerns by ensuring a more private, secure, and compliant computing environment for processing sensitive information. Fog computing has applications in smart cities, smart grids, smart homes, and software-defined networking (SDN).

See More: IT Services & Remote Monitoring Solutions for the Edge: An Unmissable Business Opportunity for MSPs

10 Key Comparisons: Similarities and Differences Between Edge & Fog Computing

As established above, edge computing happens at the edge of a network, in physical proximity to the endpoints collecting or generating data. On the other hand, fog computing acts as an intermediary between the edge and the cloud. While there is considerable overlap between the two concepts, certain important distinctions also exist.

Similarities between edge & fog computing

Below are the top five similarities between edge computing and fog computing.

Edge and Fog Computing Similarities

Edge and Fog Computing Similarities

1.Improved bandwidth

The bandwidth of a network is defined as the amount of data that it can carry over a specific period of time. A common unit for measuring bandwidth is bits per second (bps). Every network has a limit on bandwidth; however, wired networks boast stronger bandwidth than wireless ones.

Limited bandwidth poses a challenge for networks with numerous devices connected to them. Such a network can easily become congested in case of a spike in concurrent data transactions by multiple endpoints. While it is possible for organizations to enhance network bandwidth to allow for more data throughput and connected devices, such enhancements may increase costs significantly.

Both edge computing and fog computing help companies overcome the challenges arising from restrictions on data throughput and the number of devices that can connect to enterprise networks. These computing platforms enable data to be processed at the edge instead of on the cloud, minimizing bandwidth requirements and associated costs. Such bandwidth savings are especially beneficial in IoT environments where devices are numerous and every second counts regarding response time.

2.Minimized latency & congestion

Latency is defined as the time taken by a network to transmit data between two points. Even though communication takes place extremely swiftly nowadays, the speed of data transfers can be impeded due to the great distances between servers and clients. Network congestion and service outages can further increase latency. Such delays can affect time-sensitive business processes such as device health monitoring, network analytics, and decision making. Real-time responses are critical in many technological applications, especially in use cases such as autonomous vehicles and healthcare.

Both edge and fog computing minimize latency by processing data locally in near-real-time. This allows enterprises to enjoy instantaneous response times, especially for time-sensitive applications.

3.Enabling autonomous operations

While high latency and congestion are problems faced by many organizations, some organizations face a related but far more severe problem–total lack of connectivity. For instance, ships at sea, remote farms, oil rigs, and other remote locations are all less likely to be within the range of a serviceable internet connection. However, they still have use cases for cutting-edge technology such as IoT, AI, and ML.

This is where edge and fog computing come in. These platforms work together to process data locally, even in environments where bandwidth is severely restricted or connectivity is unreliable. Once the data is processed, it can be saved locally until the necessary connection is established and the data can be transferred to a central platform. An example of edge and fog computing working together to enable autonomous operations is the water quality in remote villages being gauged using sensors on water purifiers.

4.Bolstered security & privacy

The cloud is ideal for complex data analytics and modeling applications. However, concerns around the security of the data, once it is in motion between the endpoint and the data center, are not completely unwarranted. Both edge and fog computing address data security and privacy concerns by encrypting data before it leaves the edge.

Further, these computing methods help strengthen the otherwise inadequate security posture of IoT environments by keeping data off locations or streams that can be compromised. Edge and fog systems also leverage their complex distributed computing environments to identify potential cyber threats and take adequate countermeasures before affecting the entire network.

Finally, these computing architectures can be used to implement data privacy measures, such as processing sensitive data on the edge without sending anything to a centralized cloud platform. Any subset of this data can be encrypted and transmitted to the cloud as and when required.

5.Compliance with regulatory requirements

Transmitting large volumes of data over long distances is not just a technical challenge. Many jurisdictions have implemented regulations that restrict the transfer and storage of data across national and regional boundaries. Such regulations dictate how organizations store, process, and use data and can impose debilitating penalties for non-compliance.

Edge and fog computing can help enterprises comply with existing data processing and storage regulations, such as the European Union’s General Data Protection Regulation (EU GDPR). These computing platforms enable raw data to be processed and encrypted within the mandated jurisdiction. Thus, data can either be obscured from global networks or secured before being sent over such a network to a data center located outside the jurisdiction.

See More: Why Kubernetes Is Vital for Moving Cloud Native Technologies To the Edge

Key differences: Edge vs. fog computing

Five key differences between edge computing and fog computing have been listed below.

1. Concept
Edge Computing Fog Computing
Edge computing is defined as a computing architecture that brings data processing as close to the source of data as physically possible.

In many cases, data collection and processing occur on the same device, such as on an endpoint computer or IoT device. This minimizes bandwidth use and latency.

Simply put, edge computing leads to fewer processes being run in the cloud. Instead, computing processes take place locally, thus reducing the need for long-distance data transfers to cloud servers, which can be expensive and slow.

Fog computing, a term coined by Cisco, is an alternative to in-cloud processing and data storage. Like edge computing, fog computing reduces bandwidth requirements by transmitting lesser data to and from remote, cloud-based data centers. Instead, data is processed as close to the edge as physically possible.

However, unlike edge computing, fog computing often does not take place on the same device on which data is extracted or produced. In simpler terms, while ‘edge computers’ are normally the same devices that generate or collect data, ‘fog computers’ are nodes that are physically close to but distinct from these edge computers.

However, it must be noted that experts use the terms edge computing and fog computing interchangeably. Some even consider fog computing to simply be a Cisco brand name for a form of edge computing.

2. Scope
Edge Computing Fog Computing
Edge computing normally takes place on employee endpoints (laptops or smartphones) or IoT devices (sensors).

In some cases, the device that collects or generates data is not the same as the ‘edge computer’. Rather, the edge computer is a device that stores and computes data and is connected to the data-generating device over a local area network.

Such a setup may see a small-scale rack of the technology required to process data locally. Depending on the nature of the data being collected, this setup can be protected from wear and tear by using air conditioning, hardened enclosures, or other forms of security infrastructure.

An edge computer is capable of data processing for business applications. It can also transmit the results of its processes directly to the cloud. As such, edge computing is possible without fog computing.

Fog computing reduces the load on both edge and cloud computers by undertaking processing tasks from both sides.

A fog computer is physically close to the edge computer, and they can both be connected using a LAN.

Fog computing is adopted in environments where the cloud platform is located too far away to allow efficient response times, and the edge devices are either resource-limited or physically distributed.

A fog computer, by definition, is not capable of data collection or generation. As such, fog computing would not exist without edge computing.

3. Applications
Edge Computing Fog Computing
Edge computing is normally used in less resource-intensive applications due to the limited capabilities of the devices that collect data for processing.

Predictive maintenance is one such application. Here, edge computers in the form of sensors help manufacturers analyze plant equipment and detect changes before a failure occurs. IIoT sensors constantly monitor equipment health and use analytics to warn of impending maintenance needs.

Healthcare applications such as patient monitoring are also a popular use for edge devices. Devices such as smart glucose monitors and heart monitors connect directly to patients’ smartphones and relay relevant information to their healthcare provider in real-time.

Finally, massive-scale multiplayer gaming continues to stay popular across the globe. This is a prime example of edge computing, as all inputs and processing takes place on the edge device, which can be a gaming console, personal computer, or smartphone. As this form of gaming is highly sensitive to latency, only the metadata from the game session is transmitted to the cloud for processing. Provided the connections between the edge devices and the cloud server are stable, the outcomes of the actions of all players are displayed in real-time.

Fog computing is often deployed in time-sensitive applications that require high volume, resource-intensive processing of data collected from a dispersed network of devices.

Autonomous vehicles, especially cars and drones, are fast gaining popularity in the US and around the world. These vehicles, used for civil as well as military applications, produce high volumes of data. This information needs to be processed in real-time, or lives can be put in danger. This is why many autonomous vehicles rely on fog computing to operate efficiently.

Smart grids also require the processing of large volumes of real-time data to allow for efficient management. The sensors and other edge devices used in these applications are numerous and greatly dispersed. Therefore, fog computing is used to process data concurrently without compromising response time.

Finally, real-time analytics that leverage artificial intelligence and machine learning to generate actionable business insights rely on the data collected from numerous edge computers. While long-term analytics can rely directly on a centralized cloud computer, rapidly changing short-term analytics may take place over fog computers. This helps meet the requirements of time-sensitive data analytics applications, such as those seen in the banking and finance industry.

4. Processing and Storage
Edge Computing Fog Computing
In the case of edge computing, data is processed and stored either within the edge computer itself or very close to it.

As edge devices have limited processing and storage capabilities, data can be transmitted to the cloud for further operations.

A smartphone connected to a cloud network is an example of an edge computer.

Fog computing is more like a ‘gateway’ of intelligence and processing power. A fog computer connects to a batch of edge computers simultaneously, thus creating a localized network of devices for more efficient data processing and storage.

Fog computing enhances the efficiency of enterprise networks by providing a ‘bigger picture’ of the operations through the use of data from multiple endpoints. However, it does so without the latency and congestion associated with a direct edge to cloud connection.

An IIoT environment in a manufacturing plant is an example of fog computing.

5. Economic Considerations
Edge Computing Fog Computing
Edge computing services are provided by leading vendors such as Microsoft, Amazon, and Dell. These providers have a fixed, recurring fee based on configuration and usage.

Companies can also set up their own edge infrastructure. However, depending on the scale of operations and the quality of the components used, it is usually more economical for edge computing requirements to be outsourced.

Naturally, customized edge setups are bound to be more expensive regardless of whether they are built from scratch or subscribed to.

Fog computing services are a slightly more customized version of edge computing and may need to be set up either from scratch or using a combination of ‘as a service’ deployments.

While this is likely to mean a higher price tag than edge computing, the benefits of fog computing are manifold. For instance, fog computing creates an economic opportunity through massive savings in terms of bandwidth, latency, computing, and storage.

Depending on the use case, fog computing provides an economically viable alternative to large data centers.

See More: How Edge and 5G Can Unlock the True Potential of AR and VR


Edge and fog computing bring computing power closer to the data source, allowing information to be processed without the immediate need for a central cloud platform. Both computing methods are emerging technological ecosystems with futuristic applications.

These IT models are expected to enable new and exciting use cases and open up opportunities for service providers across industry verticals to develop new solutions for businesses and consumers. Key advantages of both these computing architectures include efficient data transfer, real-time computing capabilities, enhanced user experience, and minimized latency and costs.

Do you think edge and fog computing have lucrative applications in your industry? Let’s discuss on LinkedIn, Twitter, or Facebook! 


This UrIoTNews article is syndicated fromGoogle News

About Post Author