Ever since the launch of “cloud computing” and its offsets, “edge computing” and “fog computing”, the differences between the three have puzzled even many professionals. But when IT comes to general consumers, IT developers, data analysts, and enterprise networks, there are clear advantages to choosing one or more of these computing platforms. These computations will provide different functions for different environments and occasions, even though they complement each other.
Here is an overview of the computing categories at each of the three levels, and how each level is actually used. As mentioned above, the terms “cloud”, “edge” and “fog” represent three layers of computing:
▲ Cloud computing layer: industrial big data, business logic and analysis database and data storage.
▲ Fog computing layer: local network assets, micro data centers.
▲ Edge computing layer: industrial PCS, process-specific applications, and real-time data processing on autonomous devices.
It is visually helpful to think of them as layers, because each layer builds on the basic functionality of the previous layer, and each layer provides intelligent analysis closer to the data source. So where does the source come from? In manufacturing, it might be a workshop and factory with networked production equipment. In an IT environment, sources of actionable data may include enterprise routers and employee terminals.
Practical application of fog calculation
So what is fog computing? Fog computing can effectively disperse computational and analytical power. It sits between local and mobile devices, in other words, they are devices with limited processing power and storage, and provide a way to filter the flow of information from iot components.
Driverless cars navigating city blocks can give people an initial impression of fog computing. If vehicles, sensors and controllers are the “edge layer” of an urban intelligent transportation system, which means edge computing — then micro data centers need to be built and operated, and the “fog computing layer” is likely to be used, along with mesh routers and servers. Fog computing is not as diffuse as edge computing, but it does further reduce the amount of data that travels over the network or up to the cloud computing layer. It facilitates communication and collaboration between “nodes” in the edge layer. In the example above, the node is a driverless car.
So, what are the industrial applications? An example of business relevance is an automated inventory system that sits between multiple warehouses and factories in the supply chain. Here, the fog computing layer can be used to “check and balance” material, equipment and supply levels at multiple locations and automatically trigger reorders.
Fog computing represents an important intermediate step in controlling the amount and type of movement of operational data through an organization’s devices and lans, as well as decision-makers (or, ultimately, industrial-scale cloud data services). In this way, fog computing can help reduce bandwidth usage and even slow down the need for costly upgrades, as well as help companies keep their IT infrastructure running smoothly. “Smart metering” is an example of an application to the power grid. “Smart metering” is when local data centers are deployed with power plants and transformers to collect and transmit information about the local power grid. “Smart grids” controlled in this way through fog computing are more resilient in limiting the impact of blackouts and make it easier for engineers to pinpoint problems when they crop up.
Practical application of edge computing
With each step from cloud computing to fog computing and eventually edge computing, “smart devices” are devices that process information closer to the data source. Thus, with edge computing, intelligent analysis can be performed on individual machines, workstations, and mobile devices on the LAN. It’s like an automation controller in a factory; Smart devices operate machines, flag maintenance projects, and stream human data “up” to cloud computing and corporate decision makers. Industrial data scientists receive data from the cloud computing layer or cloud computing as a Service layer, providing insight into the current state of operations and helping to generate better forecasts.
Here are three examples of how edge computation can be leveraged:
1. Testing large equipment requires flexible data flows, often detailing the performance of many critical components. “Edge layers” in equipment test facilities may include wireless thermometers, vibration sensors, and other meters.
2. It can be inferred from the above that intelligent traffic management will soon become the norm. Analysis and intelligence on traffic patterns will be performed locally, in autonomous vehicles, and in intersections and traffic management protocols using fixed sensors. In this context, edge computing looks like a “connected network” that allows each related device to support each other with meaningful, actionable real-time data.
3. Smarter factories are one of the most obvious applications of edge computing in industry. By combining edge nodes with fog computing, many systems within the plant can be automated, including production equipment, environmental controls, compressed air systems, coolant circulation, electricity and other power sources.
Fog computing and edge computing create a host of new tools for consumers, enterprises, data scientists, and IT architects to achieve superior results. People may have noticed that cloud computing is somewhat better than normal. Let’s go back to the top of the computing hierarchy and briefly review the latest developments in cloud computing and the opportunities and new professions it is helping to create.
What is cloud computing
When people talk about cloud computing, it tends to have a sense of mystery or confusion, but what it really means is Internet connectivity now. Think about what you’ve been through. A factory, business or consumer device was once an island of complete isolation. It may contain useful data, but until cloud connections become more accessible, extracting data from these isolated systems is a daunting task. With so many startups popping up, the Internet everywhere and connectivity at your fingertips, the younger generation may not remember much about the early days of cloud computing. However, in just a few short years, access to cloud-based connectivity tools has completely changed the game in both industry and commerce.
Even businesses with limited budgets now have access to servers and cloud-based analytics that centralize computing power and keep many parts of their business connected. Some even claim that cloud computing has taken hold in business, allowing startups to scale faster and more seamlessly without large amounts of capital, and compete more effectively, providing more time to scale and diversify their infrastructure.
A computing layer that meets various needs
The diversity of IT infrastructure technologies has led to the widespread use of cloud computing layers. The result is new opportunities for professionals and businesses across a wide range of industries, not to mention a wide range of disciplines and job security for data scientists, IT specialists and analytics specialists. Will the business build (or lease) its own common cloud infrastructure, or opt to use more specialized tools such as fog and edge computing? It depends on the needs and development of the enterprise, and enterprises can gain a competitive advantage by adopting these computing tools.
Learn more about edge computing in edge Computing community, Baidu Search: Edge computing Community