An Experimental Study Of Fog And Cloud Computing In Cep

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 cloud vs fog computing 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 the initial data processing occurs near the data, latency is reduced, and overall responsiveness is improved.

cloud vs fog computing

In contrast, in edge computing, you’re closer to the endpoint in the end equipment/environment. This doesn’t mean that edge computing occurs on IoT devices, of course. Yet, the computation typically is only one or a few hops away, and the resources for processing, storage, etc. happen at the edge via micro data centers. Edge computing is an extension of older technologies such as peer-to-peer networking, distributed data, self-healing network technology and remote cloud services. It’s powered by small form factor hardware with flash-storage arrays that provide highly optimized performance.


In fog computing the aim is to bring the data analysis and so forth as close as possible to the data source but in this case to fog nodes, fog aggregation nodes or, when decided so by the fog application, to the cloud. It is a new computing technique which achieves options for renting of storage infrastructure and computing services, renting of business processes and overall applications. This new technique simplifies the clients computing jobs by renting resources and services.

Edge Computing vs Fog Computing: What’s the Difference? – CIO Insight

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

Posted: Tue, 28 Sep 2021 07:00:00 GMT [source]

It also protects sensitive data by analysing them within the local network. Ultimately, organisations that adopt fog computing get deeper and faster information, which increases business agility, increases service levels and improves security . Nevertheless, the design of a profitable fog architecture has to consider Quality of Service factors such as throughput, response time, energy consumption, scalability or resource utilization . Edge computing is an architecture that uses end-user clients and one or more near-user edge devices collaboratively to push computational facility towards data sources, e.g, sensors, actuators and mobile devices. It pushes the computational infrastructure to the proximity of the data source and the computing complexity will also increase correspondingly. In such architecture, any device with compute, storage and networking capabilities can serve as a near-user edge device.

Understanding Edge Computing Vs Fog Computing

It lags in providing resources where there is an extensive network involved. Traditional phones didn’t have enough built-in space to store the information and access various applications. • Public cloud is publicly accessible cloud environment owned by a third party cloud provider. • Cloud computing has few essential features service models and deployment models. The startup continues to build out its cloud platform that enables data engineers to identify and remediate data pipeline quality…

cloud vs fog computing

Moreover, that list of applications is growing day by day as the Internet of Things continues to expand and connect things we never thought were connectable, let alone worthy of a connection. Conservative estimates put the number of connected IoT devices at 55 billion by the year 2025. 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.

How Do Edge And Fog Computing Make Iot Data Processing More Secure?

The other major issue confronted with cloud computing is security and privacy. Since the cloud systems have been located with the Internet, user requests, data transmission and system responses need to traverse a large number of intermediate networks depending on the distance between the users and systems. When customer data is out there in a public cloud, there is a risk of them being compromised of their integrity and confidentiality.

  • After all, the job of these sensors is solely to record data from the environment, right?
  • WINSYSTEMS’ single-board computers can be used in a fog environment to receive real-time data such as response time , security and data volume, which can be distributed across multiple nodes in a network.
  • The primary advantage of cloud-based systems is they allow data to be collected from multiple sites and devices, which is accessible anywhere in the world.

In addition, it has been verified that low-cost devices, such as Raspberry Pi with a cost less than US$40, have enough computing resources to offer the quality of service required by IoT applications with real-time needs. Fog computing is a decentralized computing infrastructure that extends cloud computing and services to the edge of the network in order to bring computing, network and storage devices closer to the end-nodes in IoT.

Distributed Architectures: How Fog And Edge Computing Can Fit Into Your Cloud & Colo Strategy

The consortium merged with the Industrial Internet Consortium in 2019. Even though fog computing has been around for several years, there is still some ambiguity around the definition of fog computing with various vendors defining fog computing differently. Under the right circumstances, fog computing can be subject to security issues, such as Internet Protocol address spoofing or man in the middle attacks. Fog computing reduces the volume of data that is sent to the cloud, thereby reducing bandwidth consumption and related costs.

cloud vs fog computing

But some limitations have emerged as this technology’s lifecycle has matured. Perhaps even more importantly, cloud architecture supports distributed processing, meaning that mobile devices can interact with powerful algorithms and tap into vast storehouses of data. When Google Maps plots a journey, when Uber finds your driver and routes that driver, most of the processing power comes from servers in the cloud, not from your mobile device. In edge computing, physical assets like pumps, motors, and generators are again physically wired into a control system, but this system is controlled by an edge programmable industrial controller, or EPIC.

What Is Edge Computing?

As long as a device has the capacities to do what it needs to do at the edge, it can be a fog node. It could be a switch, a router, an industrial controller or even a video surveillance camera at some industrial location to name a few.

A study done by Business Insider’s research team stated that 570 million devices in 2015 used fog computing. It is expected that by the year 2020 that number will raise to include up to 5.8 billion IoT devices. Fog computing provides a better way than cloud solutions do when it comes to collecting and processing data from these devices. From businesses to cars, many industries are constantly striving to improve their efficiency in how they run their corporation. One specific route in which industries are hoping to accomplish this is by investing in newer technology, updating software, and advancing their security programs. With all the changes in the technological field, cybersecurity is a necessity that is getting harder and harder to ignore. For the technology industry, hybrid cloud computing and fog computing are two of the better known solutions protecting against cyber attacks.

Not only are the systems used for mist computing usually application specific, but sensors are often heterogeneous, making implementing a solution more complicated. In addition, the processing power available in the mist computing architecture is often limited, which adds even more constraints to any possible solution. On the other hand, regarding latency, the work highlights how a fog computing architecture considerably reduces latency with respect to cloud computing, up to 35% better. Breaking down the latency results, we can also see how the Broker is the critical element of the increase in latency. Like the core level analysis, CEP performs the event analysis and the Broker distributes the alarms from RAM. A key aspect that certifies the feasibility of using low-cost devices is that the % of memory in use is constant and independent of the number of alarms generated.

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 . Real applications can deploy more sophisticated event detection procedures, thus adding more overhead to the CEP engine. But with this simple application we can measure a performance baseline for the system.

Healthcare Provide more personalized and convenient healthcare experiences with secure, interconnected, data center solutions. Cloud computing is on-demand deliverability of hosted services over the internet. It allows users to access sql server information over the remote location rather than being restricted to a specific place. It is less expensive to operate with fog computing as data is hosted and analyzed on local devices rather than transferring it to any cloud device.


So, by having computing power on the sensor, the data can be processed, preconditioned, and optimized first before being stored. The resulting data will be much smaller, consuming less power in the transfer. However, as the network grows, and as more smart devices get connected, problems start to crop up. One such problem is often the massive amount of data that’s generated by all of those connected devices. For example, in a network of security cameras, the system can upload a huge amount of video data every second to the server. In such a system, the cameras themselves have no storage component, so all of those videos have to be stored on the main server. Fundamentally, the development of fog computing frameworks gives organizations more choices for processing data wherever it is most appropriate to do so.

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