Fog Computing vs. Edge Computing: Which Is Better for Your Business?

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Fog computing and edge computing both aim to bring computational power and data storage closer to where the data is generated, such as IoT devices or sensors, rather than in centralized data centers.

The choice between fog computing and edge computing for businesses revolves around where data processing should happen in relation to the edge of the network. Fog extends cloud to the edge for centralized processing, which is better for complex tasks or data aggregation. Edge computing processes data directly at the source, allowing for decentralized, low-latency processing, which is ideal for real-time applications.

The choice of edge computing vs. fog computing depends on the specific requirements of your business, the nature of the applications you’re deploying, and your available infrastructure.

Key Takeaways

  • Fog computing and edge computing both aim to bring computational power and data storage closer to where the data is generated.
  • The choice between fog computing and edge computing for businesses revolves around where data processing should happen in relation to the edge of the network.
  • Fog computing helps businesses by speeding up data processing, making systems more reliable, and keeping sensitive data safe.
  • Edge computing helps organizations by reducing delays in data processing, enabling them to make faster business decisions.

What Is Fog Computing?

Fog computing helps businesses by speeding up data processing, making systems more reliable, and keeping sensitive data safe. It also saves bandwidth, adapts easily to changes in demand, and makes edge devices smarter.

Overall, it makes business operations run more smoothly and improves customer experiences.

Fog computing examples include:

What Is Edge Computing?

Edge computing helps organizations by reducing delays in data processing, enabling them to make faster business decisions. It’s beneficial for IoT and real-time analytics, where quick responses matter. Plus, it saves money and bandwidth by handling data locally before sending it to the cloud.

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Like fog computing, edge computing also enables businesses to run more efficiently and serve their customers better.

Use cases for edge computing include:

Fog Computing vs. Edge Computing: Side-by-Side Comparison

Aspect Fog Computing Edge Computing
Location Located between the cloud and edge devices. Located at the edge devices themselves.
Processing Power Higher processing power compared to edge devices. Limited processing power compared to fog nodes.
Storage Capacity Higher storage capacity compared to edge devices. Limited storage capacity compared to fog nodes.
Latency Slightly higher latency than edge computing because it is positioned farther away from the edge. But it still reduces latency compared to traditional cloud. Lower latency due to local processing and data proximity.
Bandwidth Requires higher bandwidth for data transmission. Requires lower bandwidth for data transmission.
Scalability Highly scalable due to its hierarchical architecture, which can accommodate a larger number of nodes and devices. Limited scalability because it lacks the hierarchical architecture of fog computing.
Security With its additional layer of computing resources at the network edge, fog computing offers more comprehensive security capabilities than edge computing. May require additional security measures to ensure the protection of data and devices at the network edge.
Application Suitability Suitable for applications that require real-time processing, low latency, and high scalability at the edge of the network. Suitable for applications that require processing data as close to the data source as possible.

Fog or Edge: Which Is Better for Your Business?

Deciding whether fog computing or edge computing is better and more resilient depends on the specific requirements, strategic objectives, and constraints of the business, such as its operational scale, the nature of its data processing needs, and its security requirements.

Nigel Gibbons, director and partner at security consultancy NCC Group, told Techopedia:

“For environments where immediate, on-site data processing is crucial, edge computing is more suitable. However, for broader applications requiring resilience across a more extensive network with a need for integrated yet localized processing, fog computing may provide better solutions.”

When it comes to their operational needs, businesses that require immediate action based on real-time data, such as the predictive maintenance of manufacturing equipment, edge computing is preferable.

“For more extensive networks requiring cohesive data integration and slightly less immediate processing, such as traffic management systems in smart cities, fog computing could be more advantageous,” he says.

And fog computing might offer better resilience through its distributed node system, which can ensure continuity even if some nodes fail, Gibbons adds.

“Both models improve security by localizing data processing but in different scopes,” he says. “Edge computing might be preferable for extremely sensitive data that shouldn’t leave the device at all.”

Does Edge Have the Edge?

Theresa Lanowitz, head of evangelism at AT&T Cybersecurity, says that the question of fog computing vs edge computing is, ultimately, a choice that companies should make based on the specific networking and computing needs of their organizations and their specific use cases. 

“Both fog computing and edge computing aim to bring computing closer to the data source – reducing latency and bandwidth usage,” she says.

While they are similar, fog computing relies more heavily on cloud servers to complete certain tasks, according to Lanowitz. However, edge computing aims to perform most tasks locally without relying on the cloud or centralized resources. She says:

“[As such], edge computing can be more beneficial to businesses, especially as employees work across distributed networks while still being required to maintain the same levels of performance and security.”

With its highly scalable, flexible, and reliable qualities, edge computing addresses common business challenges surrounding technology performance. Lanowitz adds:

“It can also enhance an organization’s data security and privacy, allowing sensitive information to be processed and stored within an organization’s premises or a trusted network boundary. This is particularly beneficial for industries handling sensitive data, such as healthcare, finance, and government.”

Through the use of edge computing, businesses can identify new opportunities for innovation, efficiency, and growth across a wide range of industries and applications, Lanowitz says.

Edge computing refers to nodes that are at the edge of the network, while fog computing refers to nodes between the edge and the cloud, explains Saurabh Mishra, global director of IoT product management at data and AI provider SAS. 

“Of the two, edge computing provides organizations with better ability to react more quickly to data closest to its point of generation,” he says.

For example, edge computing solutions powered by artificial intelligence can quickly analyze data directly from IoT sensors in a factory, electric grid, smart city, or supply chain.

This analysis can deliver insights to engineers, data scientists, and leaders that help them make better, faster business decisions, Mishra says. These decisions can lower energy costs, improve worker or citizen safety, increase sustainability, enhance product quality, and improve lives.

Mishra says:

“Edge computing is more beneficial to businesses because it provides low latency, i.e., fast response with minimal delay, low cost of data movement, resilient architectures, and support for data privacy.”

Is the Term ‘Fog Computing’ Dissipating?

It’s important to note that “edge computing” has recently become more of an all-encompassing term for any processing away from the cloud, according to Mishra.

“As a result of this blurring and blending of labels, the term ‘edge computing’ is used much more actively than ‘fog computing’ today,” he says.

Jennifer Cooke, research director, cloud and edge services, worldwide infrastructure research at research firm IDC, agrees with Mishra, saying:

“I don’t think the term fog computing is really used anymore.”

Several years ago, the term fog computing had a moment in the spotlight, addressing the need for compute infrastructure, operated by cloud service providers, placed closer to the edge where data was being created, Cooke says.

“Just like core infrastructure, edge infrastructure can be deployed and operated in a number of ways, i.e., public cloud, private cloud, managed services, or traditional IT ownership,” Cooke adds. “At the edge, the logistical challenges of deploying, securing, and operating IT infrastructure and apps make cloud service providers an attractive option for many organizations.”

The complexity of deploying equipment to hundreds or thousands of locations and making sure they’re all up-to-date with patches is a daunting task, even for organizations with large IT departments. Cooke says:

“Cloud service providers have all stepped up to the plate, offering IT service anywhere that a customer needs it. But these customers have varied needs, and each industry has unique challenges regarding data sovereignty and security. Some organizations will continue to prefer to own and operate their own IT equipment in non-data center locations at the edge.

Edge resources are and will continue to be highly diverse, she says. The physical locations where edge equipment resides range from a cruise ship to a retail store to a colocation data center.

“The way this equipment is deployed could be a public or dedicated cloud, traditional IT, or via managed edge services,” Cooke says. “It’s important to understand that cloud and edge are not mutually exclusive terms. Edge is a location and cloud is an operating model.”

Industry Needs Drive Decisions

The unique industry needs drive edge infrastructure and application decisions, Cooke says.

“The retail industry, for example, often requires edge resources in places where there are no IT staff physically present,” she explains. “To meet the needs of their demanding edge workloads, the retail industry is gravitating toward cloud service providers that provide holistic, turnkey solutions.”

Some manufacturing companies may be better equipped to manage their edge resources, Cooke says. Some still opt for cloud service providers for their edge needs, but some are also reluctant to let any of their intellectual property leave the physical facility.

“Their edge needs frequently demand near-real-time response times, making on-site infrastructure a requirement to meet latency requirements,” she says.

The decision on which type of edge platform to use depends on the following:

  • How prepared the organization is to install and manage equipment at the edge location.
  • What the data control and data sovereignty regulations associated with the edge workloads are.
  • Whether the organization wants to invest capital into infrastructure.
  •  Whether the organization has staff time and expertise to commit to the edge projects.

The Bottom Line

Ultimately, you should base the answer to the question of edge vs. fog computing on your business goals, data processing needs, and the specific characteristics of the applications you’re developing.

By understanding the difference between fog and edge computing, including the strengths and limitations of each approach, you can make informed choices that optimize your computing infrastructure.

FAQs

Can you use the same hardware in both fog computing and edge computing?

What is the difference between fog nodes and edge nodes?

Is mist computing the same as edge computing?

What is the difference between cloud, fog, and edge computing paradigms?

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Linda Rosencrance
Technology journalist
Linda Rosencrance
Technology journalist

Linda Rosencrance is a freelance writer and editor based in the Boston area, with expertise ranging from AI and machine learning to cybersecurity and DevOps. She has been covering IT topics since 1999 as an investigative reporter working for several newspapers in the Boston metro area. Before joining Techopedia in 2022, her articles have appeared in TechTarget, MSDynamicsworld.com, TechBeacon, IoT World Today, Computerworld, CIO magazine, and many other publications. She also writes white papers, case studies, ebooks, and blog posts for many corporate clients, interviewing key players, including CIOs, CISOs, and other C-suite execs.