What Is Fog Computing? Tech That Can Spur Government IT Modernization

Fog brings the power of the cloud closer to the network edge and improves latency for mission-critical applications.

Federal agencies are clearly moving to the cloud and are also increasingly adopting edge computing solutions. But what sits between the cloud and the edge? Fog computing. 

While fog computing and edge computing have many similarities, there are some clear differences. A key one is where fog sits and how it interacts with the cloud and edge devices. Fog brings cloud capabilities to the network edge. Both are intended to improve the latency of connections and applications and processing of data near the network edge.

Like edge, fog computing is still relatively nascent in government. The National Institutes of Standards and Technology in March 2018 produced a document that describes a conceptual model of fog computing but does not definitively define it.

The NIST document notes that “fog computing is a horizontal, physical or virtual resource paradigm that resides between smart end-devices and traditional cloud or data centers. This paradigm supports vertically-isolated, latency-sensitive applications by providing ubiquitous, scalable, layered, federated, and distributed computing, storage, and network connectivity.”

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What Is Fog Computing?

Michaela Iorga, NIST’s senior security technical lead for cloud computing, and one of the co-authors of the agency’s conceptual model of fog computing, notes that NIST perceives fog computing as a “logical layer model.” It is designed to enable ubiquitous access to shared, scalable computing resources, she notes. 

“Logically, fog computing is placed between cloud, which is a very centralized service, and the edge of the network fabric,” where smart end device and IoT devices sit, Iorga says. “Fog computing creates that extension of cloud functionality when you need it and brings that functionality closer to where latency is very important, and the geographical distribution is very important for the applications.”

Fog computing not only brings cloud computing capabilities to the edge of an agency’s Local Area Network or IoT network, but it also interoperates with agency’s cloud environments, according to Grimt Habtemariam, federal cloud strategist at Cisco Systems, and Marcus Moffett, a senior systems engineer at Cisco.

Fog, they note, enables IT teams “to create a consistent multi-cloud environment that can deliver the agility needed for application developers to innovate with speed and for government to unlock the power of the data generated at the edge.” 

Both fog and edge computing are “basically involved with the arraying of various kinds of computation, networking and storage elements in IoT networks between the bottom of the cloud and the top of the network side of intelligent sensors, actuators and end devices,” says Chuck Byers, CTO of the OpenFog Consortium. “It’s really about how we respond to the specific challenges of IoT networks that make fully cloud-based hosting of that cognition less attractive.”

MORE FROM FEDTECH: Find out what digital twin technology is and how agencies can use it. 

Fog Computing vs. Edge Computing: What’s the Difference?

Fog computing and edge computing are closely related but separate concepts. “Technologically, we have seen that fog computing is a layer that helps you get everything that you need closer to the edge where the function is necessary,” Iorga says.

The network edge refers to any location outside the data center where data is generated. Edge computing provides security, networking, compute and storage resources, Habtemariam and Moffett note. “It enables data collection and real-time data processing critical to helping organizations make decisions on time-sensitive data sets,” they add.

Michaela Iorga
Fog computing is a layer that helps you get everything that you need closer to the edge where the function is necessary."

Michaela Iorga Senior Security Technical Lead for Cloud Computing, NIST

While some of the data collected with edge computing nodes can be directly sent to the cloud for storage and further processing, Habtemariam and Moffett say, “there are many cases where this is not realistic due to time, resources and security constraints.”

For example, by the time the data makes its way to the cloud for analysis, the opportunity to act on it might be gone. Or latency and bandwidth constraints limit the ability to transport the volume of data being generated at the edge. Additionally, Habtemariam and Moffett say, “confidentiality needs to be enforced, and in some cases, data may need to be filtered before it is sent to certain cloud environments.”

There are some applications where having low latency and very fast network response time is critical, Iorga notes. She cited virtual reality as one, noting that any delay could cause the user to become nauseated. Additionally, telemedicine services in hospitals cannot afford to have high latencies. 

MORE FROM FEDTECH: Learn how embracing a cloud computing architecture can benefit your federal agency. 

What Are the Benefits and Drawbacks of Fog Computing?

Fog is useful for apps where latency is too high in the cloud, Byers notes. Many IoT applications are delay-sensitive, and if the delay gets too long, the systems become unstable. That can impact users’ safety, especially for apps like connected traffic signals in smart cities. 

“By moving the computation deeper in the networks and closer to the IoT networks and endpoints, you can have much better control over that latency,” Byers says. He notes that many cloud apps in IoT networks have up to 250-millisecond latency. Fog and edge computing can produce latency of less than a millisecond, he notes. 

Fog can also enhance data security. “If important data is running from sensors all the way through multiple layers of wireless and wireline networks, and into the cloud, there’s lots of opportunities for security breaches,” Byers says. 

By pushing computation, especially AI algorithms and analytics, deeper into the network closer to IoT devices (in cabinets on streetcars or in buildings, for example), agencies can decrease their vulnerability, he notes. 

Agencies can also bring processing capabilities needed for cybersecurity closer to the end devices. Fog “enables IT to apply security services, such as access control, threat detection and mitigation, supply chain integrity validation and data confidentiality enforcement closer to the edge,” Habtemariam and Moffett note.

Fog computing does have a drawback in that it expands an agency’s cyberattack surface. “It expands IT’s responsibility at the edge, and reinforces the need for an architectural approach to manage the organization’s multi-cloud environment,” Habtemariam and Moffett say. “As with any environment that is part of the organization’s multicloud, IT will need deep visibility into its fog computing environment and the ability to extend security capabilities to it.”

MORE FROM FEDTECH: See how the Navy uses VR to train aircraft carrier crews.

How Fog Computing Can Help Government Agencies Modernize IT

In general, fog computing enables agencies to “leverage multiple data sources such as space-based assets and signal surveillance, each feeding into a fog environment,” Habtemariam and Moffett say. “By applying analytics to the aggregated data, it enables the correlation of data across multi-domain operations, and enables time sensitive decisions to be made based on that correlation.” 

There are numerous applications for fog computing across the government. Byers notes that the Department of Homeland Security could use fog computing to verify drones and allow them to cross into restricted airspace

A drone may be trying to land in a particular area, but in order to get clearance to do so, its sensors need to be scanned and verified by a fog computing node, he says. If the drone passes the verification, it will be allowed to fly in and land. 

As agencies look to use AI and machine learning to improve training efficiency, fog computing can “provide the processing power and analytics capabilities required to aggregate, analyze and filter training related data collected at the edge,” according to Habtemariam and Moffett. “The relevant data can then be transmitted to a data lake for deep learning.”

Many defense agencies are considering fog and edge computing for their immersive, integrated live, virtual and constructive training environments, according to Habtemariam and Moffett. Such training environments are “highly distributed and sometimes link space, cyber, datalinks, radar, sensors, ranges, models and simulations across robust networks,” they say. 

Edge computing allows the training and operational software to run at the network edge, but fog computing provides the armed forces with “the resources needed to apply analytics and machine learning for real-time training adjustment. This makes it possible for personnel to conduct more realistic simulated trainings that closely replicate real-world operations,” they say. 

The armed forces can also use fog computing at forward operating bases or on Humvees and other vehicles, Byers says. Soldiers’ vests can also have computing nodes on them to communicate between soldiers and also back to their base in a fog hierarchy.

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Mar 14 2019

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