Difference Between Cloud Computing And Fog Computing

So, cloud, fog, and mist computing all have their own strengths and weaknesses. As more and more devices are connected to the Internet, using all of these paradigms correctly will be key to ensuring that our systems and applications are able to scale alongside our growing network of devices. While Bernhardy acknowledges fog computing’s advantage of being able to connect with more devices and hence process more data than edge computing, he also identified that this dimension of fog computing is also a potential drawback. “Edge computing technology saves time and resources in the maintenance of operations by collecting and analyzing data in real-time.

  • This work exposes the use cases in which it is of great importance, and necessity, to decentralize resources with a fog computing architecture.
  • Internet of Things has been poised as the next big evolution after the Internet promising to change our lives by connecting the physical entities to the Internet in a ubiquitous way leading to a smart world.
  • In most cases, the ideal approach is to decide what data to process in the cloud and what’s better suited for edge and fog computing.
  • Different software as a service, infrastructure as a service, and developing the platform is known as cloud computing.
  • Their teams will still be able to access data remotely, for example.
  • Another problem that cloud computing often doesn’t handle well is time delay.

His current research interests include optical networks and network survivability. Jian Kong is currently working in Ciena, Austin, USA. He earned his Ph.D. in Telecommunications Engineering from the University of Texas at Dallas, in 2018. His research interests are in the areas of network virtualization, optical network and fog/cloud computing.

Fog Computing Vs Cloud Computing For Iot Projects

Finally, a spine-leaf fog computing network to reduce network latency and congestion problems in a multilayer and distributed virtualized IoT data center environment is presented in Okafor et al. . This approach is cost effective as it maximizes bandwidth while maintaining redundancy and resistance to failures in mission critical applications. These results, in latency and QoS metrics, are obtained for datacenters by comparing these two methods for a typical fog computing architecture with respect to cloud computing. Edge and fog computing are often used interchangeably as both move computational processes and data storage closer to end users.

Fog Computing vs Cloud Computing

Most edge and fog computing use cases relate to the Internet of Things. That’s probably because most research on the matter has so far centered on IoT possibilities. However, that could change as people get more curious about exploring past perceived limits. For example, research from Wayne State University suggests it could improve firefighting. It’s also possible to rely on fog computing to simultaneously update IoT devices without requiring they first connect to the cloud. Another way to protect fog computing is to install virtual firewalls around it. Here’s an explanation of how edge and fog computing differ, and how they complement each other.

Cloud Vs Fog Computing

The fog computing architecture considered in this work integrates the core level and the edge level (see Fig.2). It should be noted at this point that the main idea of the described architecture is that fog applications are not involved in performing batch processing, but have to interact with the devices to provide real-time streaming.

Fog Computing vs Cloud Computing

Will be interesting to see how the advancements in 5G technology will impact fog computing. Because as 5G continues to roll out, more and more devices will have the power and speed levels to become interconnected. Fog acts as a mediator between data centers and hardware, and hence it is closer to end-users. If there is no fog layer, the cloud communicates with devices directly, which is time-consuming. Fog can also include cloudlets — small-scale and rather powerful data centers located at the edge of the network. Their purpose is to support resource-intensive IoT apps that require low latency.

“More infrastructure is needed and you are relying on data consistency across a large network,” he said. It can become a complex issue for brands to handle, as data sets that require more sophisticated algorithms are better handled in the cloud, whereas simpler analytical processes are best kept at the edge. Incorporating trusted, high-performance rugged servers closer to your IoT smart devices can help you do both, no matter the conditions of the environment on land, in space, in air, or at sea. Blanca Caminero is an Associate Professor in Computer Architecture and Technology at the Computing Systems Department at UCLM. She teaches networking related subjects at the School of Computer Science and Engineering in Albacete since 2000.

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Taking into account this evaluation set out in the literature, the actual load of this architecture has been evaluated in our work, but specifically in real-time IoT applications. For these types of applications in IoT, two important and critical architecture components emerge, to be integrated into both the edge nodes and the cloud, these are, the CEP technology and the MQTT protocol.

For example, commercial jets generate 10 TB for every 30 minutes of flight. Fog computing sends selected data to the cloud for historical analysis and long-term storage. Fog computing cascades system failure by reducing latency in the operations. It analyzes data close to the device and helps in averting any disaster. The emergence of cloud computing is because of the evolution of IoT devices, and the cloud is not able to keep up with the pace. Fog computing allows us to locate data over each node on local resources and thus making the analysis of data more accessible. The fog has a decentralized architecture where information is located over different nodes at the user’s closest source.

Fog Computing vs Cloud Computing

At the same time, though, fog computing is network-agnostic in the sense that the network can be wired, Wi-Fi or even 5G. According to the OpenFog Consortium started by Cisco, the key difference between edge and fog computing is where the intelligence Agile software development and compute power are placed. In a strictly foggy environment, intelligence is at the local area network , and data is transmitted from endpoints to a fog gateway, where it's then transmitted to sources for processing and return transmission.

Security

Networks on the edge provide near-real-time analytics that helps to optimize performance and increase uptime,” Anderson said. Here at Trenton Systems, when we use the term edge computing, we mean both. Our definition of edge computing is any data processing that’s done on, in, at, or near the source of data generation. As for average CPU consumption (in %), see Fig.11a, we can see that it has not been excessive in both architectures since the events sent do not perform complex mathematical operations that stress the CPU, but are simple comparison events.

The system will then pass data that can wait longer to be analyzed to an aggregation node. The characteristics of fog computing simply dictate that each type of data determines which fog node is the ideal location for analysis, depending on the ultimate goals for the analysis, the type of data, and the immediate needs of the user. In connecting fog and cloud computing networks, administrators will assess which data is most time-sensitive. The most critically time-sensitive data should be analyzed as close as possible to where it is generated, within verified control loops. The main difference – at least as it is being defined these days – comes from the fact that the cloud exists via a centralized system. Whereas in a fog computing environment, everything is decentralized, and everything connects and reports via a distributed infrastructure model.

Thus, in this particular case, and by which the subsequent performance study will be carried out, we will compare the latency in both architectures for a controlled number of alarms generated, specifically 200, 400, 600 and 800 alarms/min. Equation 1 has been used to calculate total latency (see “Latency analysis” section). Hence, Fig.8 shows the results of making this comparison between the different connections to the Broker for a load with the pattern described in the previous subsection and a total of 800 alarms/min. As expected, a user who is on the same LAN of the Fog Node will receive the alert in less time than one connected by 3G and 4G, although 4G is very close to WiFi. One of the strengths of 4G is the speed and stability of the signal with respect to 3G which, as can be seen, has a more pronounced variance than 4G . For this work a maximum limit of 800 alarms/min has been established since when generating more alarms, a bottleneck was created in the Fog Node and events were beginning to be lost. To do this, 20 end-points are emulated and a total of 1600 data per minute is sent, that is, 80 data per end-point.

While Fog networking is famous for its access speed, cloud computing speed is much dependent on the VM connectivity. So, in terms of large users, fogging is widely recommended as compared to Cloud computing due to the safety and security it provides. Connected manufacturing devices with cameras and sensors provide another great example of fog computing implementation, as do systems that make use of real-time analytics.

In this case, we have a structure of intermediate devices, called a gateway, that sort out which data will be processed on the edge and which will be taken for processing on the cloud, in an intelligent way. As we have seen, there are still challenges when it comes to Edge Computing, especially when we consider the processing capacity of these devices at the edge. At the same time, we need to reduce some latency or bandwidth problems Offshore outsourcing that can happen when using only Cloud Computing. Edge Computing is a change in perspective in relation to Cloud Computing, since in this type of solution all data processing takes place at the edge, that is, on the devices used by users. Consider the fog network as the point where information from all these localized devices will be converged, processed and necessary actions will be communicated to the relevant devices.

Fog Computing vs Cloud Computing

Cloud architecture is centralized and consists of large data centers that can be located around the globe, a thousand miles away from client devices. Fog architecture is distributed and consists of millions of small nodes located as close to client devices as possible. One should note that fog networking is not a separate architecture and it doesn’t replace cloud computing but rather complements Fog Computing vs Cloud Computing it, getting as close to the source of information as possible. While cloud computing still remains the first preference for storing, analyzing, and processing data, companies are gradually moving towards Edge and Fog computing to reduce costs. The fundamental idea of adapting these two architectures is not to replace the Cloud completely but to segregate crucial information from the generic one.

Cisco estimates that IoT will generate more than 500 zettabytes per year in data by the end of 2019. Instead of sending extensive IoT data to the cloud, fog computing in this way analyzes the most time-sensitive data at the network edge, making it act in milliseconds. Fog computing enables quick responses and reduces network latency and traffic. It is because of cloud computing technology that these phones got “smart” as it transmits the data and gives on-demand availability of the resources and services. And to cope with this, services like fog computing, and cloud computing are utilized to manage and transmit data quickly to the users’ end. Another good blog would be talking about the differences between edge computing and fog computing. They sound very similar to me, but I want to understand the difference in use cases between the two.

Pros Of Fog Computing

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. Autonomous vehicles essentially function as edge devices because of their vast onboard computing power. These vehicles must be able to ingest data from a huge number of sensors, perform real-time data analytics and then respond accordingly. Because the initial data processing occurs near the data, latency is reduced, and overall responsiveness is improved.

Author: Rebecca M. Mahnke

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