What does edge computing mean What is edge computing?

Edge computing refers to an open platform that integrates the core capabilities of network, computing, storage, and application on the side close to the source of things or data to provide the nearest services.

Its applications are initiated at the edge, generating faster response to network services and meeting the basic needs of the industry in terms of real-time services, application intelligence, security, and privacy protection. Edge computing sits between the physical entity and the industrial connection, or at the top of the physical entity. Cloud computing, on the other hand, still has access to historical data from edge computing.

Start with distribution

Edge computing is not a new word. Akamai, a provider of CDN and cloud services for content delivery networks, has partnered with IBM on "edge computing" since 2003. As one of the world's largest distributed computing service providers, it was responsible for 15-30% of the world's network traffic at the time.

In one of its internal research projects, the purpose and problem solving of "edge computing" was proposed, and edge edge-based services were provided on its Websphere through Akamai and IBM.

For the Internet of Things, the breakthrough in edge computing technology means that many controls will be implemented through local devices without handing over to the cloud, and the processing will be done at the local edge computing layer. This will undoubtedly greatly improve processing efficiency and reduce the load on the cloud. Being closer to the user also provides users with a faster response and resolves their needs at the edge.

Edge computing refers to an open platform that integrates the core capabilities of network, computing, storage, and application on the side close to the source of things or data. On one side of the source of these things or data, an edge computing platform that converges the core capabilities of network, computing, storage, and application provides end-users with real-time, dynamic, and intelligent service computing.

To take the simplest example: how to choose the solder joints when welding two steel parts with a welding robot? Is it a little left or a little to the right, a little above or a little down?

Although the stamped steel plate is made of a unified standard, there will inevitably be slight differences when the two parts are combined, and through visual observation and edge calculation, the robot can judge the position of the optimal solder joint by itself and weld the two parts firmly. The data of each weld is uploaded to the cloud via the network for machine learning. If there is no edge computing, the solder joint position will be judged through cloud computing, the production efficiency will be reduced, and the solder joint may also be the same, and some parts may just catch up with the unoptimal solder joint position and be welded.

Edge computing is an emerging computing architecture and technology, which is a distributed computing mode that proximates computing resources and data storage close to data sources and terminal devices.

Traditional cloud computing architectures concentrate computing and storage resources in centralized data centers, while edge computing distributes these computing and storage resources across edge nodes close to end devices. This can reduce the delay of data transmission over the network, improve the real-time and security of data processing, reduce network bandwidth consumption, and improve application performance and user experience.

Edge computing generally uses hardware devices similar to micro data centers, such as smart routers, edge servers, smart sensors, etc., which can be used for data processing, analysis, and storage locally, and can also be combined with cloud computing to achieve flexible collaboration between data locally and in the cloud.

Edge computing is widely used in the Internet of Things, intelligent manufacturing, smart cities, Internet of Vehicles, monitoring and other fields, which can effectively solve the problems of data processing and transmission delay, bandwidth, security and other aspects.

Edge computing is computing that is generated near the device.

Edge computing provides cloud services and IT environment services for application developers and service providers at the edge of the network. The goal is to provide compute, storage, and network bandwidth close to data input or users.

In layman's terms: edge computing is essentially a service, similar to cloud computing and big data services, but this service is very close to the user; Why so close? The purpose is to make users feel that they can read any content very quickly.

Internet of Things applications.

The rapid development of smartphones around the world has promoted the development of mobile terminals and "edge computing". The intelligent society of the Internet of Everything and the perception of all things is accompanied by the development of the Internet of Things, and the edge computing system has also emerged.

In fact, the concept of the Internet of Things has been around for more than 15 years, however, the Internet of Things has not become a hot application. There is a long process from a concept to a real application, and the matching technology, the cost of product equipment, acceptance, and the trial-and-error process are all long, so it often cannot quickly form a market for mass use.

According to Gartner's technology maturity curve theory, IoT has reached its peak conceptually in 2015. As a result, the large-scale adoption of the Internet of Things has also begun to accelerate. Therefore, in the next 5-10 years, IoT will enter a period of application explosion, and edge computing is expected to be more and more used.

Edge computing is the implementation of data processing, application operation, and even some functional services from the network center to the nodes at the edge of the network. Building the Internet of Things in a conventional mode, with the rapid increase in devices, the amount of data generated at the edge of the network will be enormous. If all this data is handled by a cloud-based management platform, there is a huge pressure!

By using edge computing, massive data can be processed nearby, and a large number of devices can also work efficiently and collaboratively, and many problems can be easily solved. As a result, edge computing theoretically addresses the critical needs of many industries in terms of agility, real-time, data optimization, application intelligence, and security and privacy protection.

It refers to the calculation of data edge streams.

It is also used in special industries.

Edge computing has the following six characteristics:

First, decentralization.

Edge computing is to distribute networks, computing, storage, and applications from the "center" to the edge to provide intelligent edge services nearby.

Second, non-oligarchization.

Edge computing is the "cross entrance" of the Internet, mobile Internet, Internet of Things, industrial Internet, electronics, AI, IT, cloud computing, hardware equipment, operators and many other fields.

Third, everything is marginalized.

Edge computing is as universal and universal as IT and the Internet in the early years, cloud computing and mobile Internet today, and artificial intelligence in the future.

Fourth, security.

Before the advent of edge computing, most of the user's data had to be uploaded to the data center, and in this upload process, the user's data, especially private data, such as individual tag data, bank account passwords, e-commerce platform consumption data, search records, and even smart cameras, etc., was at risk of leakage. Edge computing, on the other hand, eliminates similar risks from the source because data can be processed at the edge instead of uploading data to the data center in many cases.

Fifth, real-time.

With the increasing popularity of various scenarios such as industrial Internet, autonomous driving, smart home, intelligent transportation, and smart city, applications in these scenarios have higher and higher requirements for the speed and efficiency of computing, network transmission, and user interaction. Take autonomous driving as an example, in these aspects, it is almost necessary to speed in seconds or even milliseconds. Alocom's industrial gateways with edge computing technology allow for better data transmission.

Sixth, greening.

The data is processed at the local end, so it can save a lot of costs such as servers, bandwidth, power and even physical space in all aspects of network transmission, central computing, central storage, and backhaul, so as to achieve low cost and green.

Edge computing has the following features:

First of all, it can make the whole computer more intelligent and more efficient, not only that, edge computing can also help people access historical data, the retrieval of historical data is relatively efficient, and the achievement of intelligence is also more rapid. In addition, it also has a more ideal privacy protection function, if the personal information is stored on the system, then it can protect people's personal privacy. In fact, traditional types of computing solutions are also more common, but they are not particularly enthusiastic for people, because most of the software is relatively backward, which will affect the basic efficiency of the overall operation, but this software does not have similar problems.

Edge computing is a networking concept with the ultimate goal of bringing computing as close to the source of the data as possible to reduce latency and bandwidth usage. In short, edge computing will reduce the number of services running in the cloud and place them to run locally, such as the user's computer, IoT device, or edge server. Leveraging edge computing minimizes the amount of communication between the client and the server.

The two major disadvantages are, one is that it will bring more risks. As more "smart" devices are added, such as edge servers and IoT devices with embedded systems, hackers will have the opportunity to compromise these devices.

Another disadvantage is that it requires more local hardware. For example, while an IoT camera requires a built-in computer to send its raw data to a web server, it requires a more sophisticated computer and more processing power to run its own motion detection algorithms. But the falling cost of hardware makes it cheaper to build smarter devices.

There are three distinguishing features, namely: proximity to the data source; Low latency, fast response; Data security is high, so edge computing is used in many practical scenarios.

As a concept derived from 5G, edge computing has attracted more attention in the critical period when the country has entered the 5G "battle". Currently, this technology can be applied to many fields such as autonomous driving, Internet of Things, robotics, industry, medical, security, etc.

The benefits of edge computing include:

2.Lower costs: Edge computing can reduce costs by reducing the need for servers in the cloud.

3.Better privacy: Data processing is done at the edge, so it doesn't leak to the public internet.

4.High availability and scalability: Because data is stored locally rather than in centralized storage, high availability and scalability can be achieved.

1. Low latency: Because edge computing is close to the source of data reception, it can obtain data in real time and analyze and process the data.

2. High efficiency: Edge computing is closer to the device than cloud computing, which can realize the analysis and processing of data at the edge node, and does not need to wait for the time of data transmission, so the efficiency will be higher.

3. More secure: After the edge computing obtains the data, it can encrypt the data and then transmit it, which greatly improves the security of the data.

4. Alleviate traffic pressure: edge computing performs a part of simple data processing through edge nodes when transmitting in the cloud, and when faced with a large amount of data, it can extract useful information through compression algorithms and then transmit it, which can reduce bandwidth resource consumption.

Distributed and low-latency computing. Edge computing focuses on the analysis of real-time and short-term data, which can better support the real-time intelligent processing and execution of local services.

More efficient. Because edge computing is closer to the user, filtering and analyzing data at the edge node is more efficient. More energy efficient. The combination of cloud computing and edge computing makes it more energy-saving to use cloud computing.

Relieves flow pressure. During cloud transmission, a part of the simple data processing is carried out through the edge node, which can reduce the data traffic from the device to the cloud by reducing the response time of the device.

Hongqiao Smart "Cloud Box" has powerful computing power.

Edge computing capabilities can be built for smart light poles. Smart light poles with edge computing can be understood as special "robots" that can be found in all corners of the city. The smart light pole has the ability to execute the linkage strategy of the smart device, and the sensing device and the execution device can be linked by themselves to automatically execute the linkage strategy. Hongqiao smart "cloud box" also has the best recognition ability, and the smart light pole is an agile monitoring robot to solve the problem of unstructured data analysis efficiency and resource bottleneck.

Edge computing is a distributed computing model that moves computing and data processing from a server to an edge device closer to the data source. Edge devices can be smartphones, sensors, IoT devices, robots, etc. Edge computing can provide many values for many applications, including:

Faster response times: Edge Meter Computing moves data processing from the server to a device closer to the data source, so data can be processed and requests responded to more quickly. This is important for applications that require real-time responses, such as autonomous vehicles, industrial automation, and more.

Reduced network latency: Traditional cloud computing models require data to be sent from the device to a cloud server for processing, and then the results are returned to the device. This can lead to network latency and high data transfer costs.

The edge residual virtual bond calculation can process data locally on the device, reducing data transmission and network latency.

Better data privacy and security: Edge computing can store and process data locally on the device instead of uploading it to a server. This improves data privacy and security, and reduces the risk of data breaches and being hacked.

Higher reliability: Edge computing can process data locally on the device and continue to process data even if the network connection is interrupted. This increases the reliability of the system and ensures that there will be no failures during mission-critical tasks.

Reduce costs: Edge computing can reduce the dependence on ** servers and reduce data transmission and storage costs. In addition, it can leverage the idle resources of edge devices for computing, reducing hardware costs.

In conclusion, edge computing can provide many values to many applications, including faster response times, lower network latency, better data privacy and security, higher reliability, and lower costs.