About Edge cloud
An edge cloud is a small-scale cloud data center that is distributed on the edge of the network and provides real-time data processing and analysis decisions. It is a distributed processing and storage architecture that is closer to the source of the data. For example, cameras with visual processing functions, wearable medical devices that send data to mobile phones via Bluetooth, etc., all make use of edge computing. Compared with cloud computing, edge computing is closer to the terminal and has many excellent features. Therefore, the mixed use of edge computing and cloud computing is generally considered to be the best practice for building enterprise-level IoT solutions.
Key components frequently used in edge computing
Cloud explosion and delay
With the advent of 5G communications, we have entered the era of cloud application explosion. It is true that the cloud can bring many benefits to enterprises in terms of cost, benefit, scale, automation, interoperability, and concentration. Therefore, a large number of IT companies' services are entirely on the cloud or rely heavily on the cloud.
At the same time, the number of sensor devices and the amount of data they generate are also increasing rapidly, and this trend is expected to continue in the coming years. Not only the source of data wide and the amount of data huge, the data it collects tends to change significantly within a few milliseconds, so the speed at which companies transform data into insight and then execution is particularly critical. So, how to keep the delay as small as possible in the entire process from data generation to decision then to execution? The speed of light is the speed limit of data transmission. Therefore, only by shortening the distance of data transmission can reduce the delay in a true sense. In a "cloud" -only world, data may have to be transmitted hundreds or even thousands of miles, delays are inevitable, and edge computing can effectively solve this problem.
It is estimated that in the future 95% of IoT data will be processed near the source of the data, including the device side and the edge side. Since the reduction in latency can significantly improve response time, thereby saving time and money, therefore, with the increasing demand for data, solving the latency problem will become the focus. The birth of the edge cloud is an inevitable product of the in-depth development of Internet, and a necessary condition for human society to enter the DT era (Data information age).
The delay time range from the device layer to the different layers of the public cloud
Internet of Things and Edge Cloud
The Internet of Things solution has a huge promotion effect on the agile development of enterprises. The following are several cases based on edge and cloud Internet of Things solutions:
With the continuous development of technologies such as the Internet of Things, enterprises are rapidly transforming into digital and automated business processes. At the same time, many manufacturers are located in factories around the world, and each factory usually has its own characteristics and functional requirements. Therefore, cloud computing plays an important role in enterprise transformation and intelligent manufacturing by virtue of its own advantages. Through the cloud, companies can monitor systems and processes across regions and across the globe, enabling comparative analysis of the entire operating situation to determine the optimal investment ratio. This shows that for enterprises, cloud platforms or data centers are indispensable.
Despite the obvious advantages, companies expect to centrally maintain all data through cloud platforms or data centers, but it is too large and unrealistic. Therefore, enterprises can only provide fast and almost unhindered connections to smart factories by combining edge cloud architecture.
Device layer (device layer) represents a single device component connected to the local area network or the Internet of Things to achieve instant interaction. This layer of machine learning (ML) is based on the ML model trained in the cloud. A large amount of original equipment data is also stored in this layer.
The device layer provides visibility and control of individual devices, while the plant apps layer provides visibility and control of all connected devices in the plant. The edge connectivity layer provides the necessary connection between a single device and the factory application layer.
The enterprise layer is cloud-hosted and mainly provides visibility and control across multiple factories. This level performs analysis, prediction and decision-making at the enterprise level, trains the ML algorithm model based on the data of the entire factory, and then "pushes" the results of the training and analysis to the edge level, and finally sends it to each device for intelligent operation.
Data interaction between the edge and the device layer
With the rise of intelligent networking technology, whether it is an office, retail store, factory or hospital, intelligent buildings have become more efficient, more comfortable, more convenient, bringing a unique experience to residents. It is understood that intelligent buildings combine automated operations with space management, which can effectively enhance the user experience, increase productivity,and reduce costs and network security risks..In addition, smart life can better control infrastructure and conduct business, enabling developers to save space, energy, water and human resources.
In addition, many commercial residences and office buildings now have automated control or management systems, such as heating, central air conditioning, and intelligent lighting systems embedded in sensors. They can interact with the cloud platform or the main system at the edge level. Among them, the edge server or gateway is usually used to send back the analysis results of the cloud platform to optimize the operation or scheduling of the device. In short, edge computing and cloud computing provide smarter resource management for life.