As the trend of the Internet of Things continues to deepen, the number of end devices such as smart phones and smart glasses continues to increase, making the growth rate of data far exceed the growth rate of network bandwidth; at the same time, many new applications such as augmented reality and driverless driving There is a higher demand for delays. Edge computing provides a unified platform for computing, networking, and storage resources on the edge of the network to provide services to users, enabling data to be processed in a timely and efficient manner near the source. This mode is different from cloud computing in that all data is transmitted to the data center, bypassing the bottleneck of network bandwidth and delay, and has attracted widespread attention.
How to understand edge calculation
In recent years, the rapid development of big data, cloud computing, and intelligent technology has brought profound changes to the Internet industry, and also put forward new requirements for computing models. The amount of data generated every day in the era of big data is increasing rapidly, while the data in the context of applications such as the Internet of Things is geographically dispersed, and higher requirements are placed on response time and security. Although cloud computing provides an efficient computing platform for big data processing, the current network bandwidth growth rate is far behind the growth rate of data. The speed of network bandwidth cost is slower than the cost of hardware resources such as CPU and memory. At the same time, complex network environments make network delays difficult to achieve breakthroughs. Therefore, the traditional cloud computing model needs to solve the two bottlenecks of bandwidth and delay. In this application context, edge computing came into being, and it has received wide attention from researchers in the past two years.
The edge in edge computing refers to the computing and storage resources on the edge of the network. The edge of the network here is opposite to the data center, which is closer to the user in terms of geographical distance and network distance. Edge computing is a technology that uses these resources to serve users at the edge of the network, enabling applications to process data near data sources. If we understand the edge calculation from the perspective of bionics, we can make an analogy: cloud computing is equivalent to the human brain, and edge computing is equivalent to the human nerve ending. When the needle is stabbed, it is always subconsciously closed, and then the brain realizes that the needle has reached the hand because the process of retracting the hand is an unconditioned reflex directly processed by the nerve ending. This unconditioned reflex accelerates people's reaction speed, avoids more damage, and allows the brain to focus on advanced intelligence. The future is the era of the Internet of Things. Cisco expects to have 50 billion devices connected to the Internet by 2020. We can't make cloud computing the "brain" of every device, and edge computing is to let devices have their own "brains."
For everyone's more convenient understanding, we can think of a very magical creature in the world - octopus, as an animal with high IQ in invertebrates, octopus has a huge number of neurons, but 60% are distributed in octopus On the eight legs, the wrists are only 40% of the brain. Escape and hunting are extremely fast, and the eight legs are clear and plain, never entangled, thanks to the octopus similar to distributed computing "multiple cerebellum + one brain."
Advantages of edge computing
When it comes to edge computing, we have to mention cloud computing. The cloud computing service is a centralized service, and all data is transmitted to the cloud computing center through the network for processing. The high concentration and integration of resources makes cloud computing highly versatile. However, in the face of the explosive growth of IoT devices and data, the aggregation service based on cloud computing model gradually reveals its real-time, network constraints, Insufficient resource overhead and privacy protection.
Compared to cloud computing, edge computing can better support mobile computing and IoT applications, with the following obvious advantages:
1. Mitigating network bandwidth and data center pressure. Cisco pointed out in the 2015-2020 Global Cloud Index that with the development of the Internet of Things, global devices will generate 600ZB of data in 2020, but only 10% of them are critical data, and the remaining 90% are temporary data without long-term storage. Edge computing can take advantage of this feature to process large amounts of temporary data at the edge of the network, thereby reducing network bandwidth and data center pressure.
2. Enhance the real-time response. The application of the Internet of Everything scene has extremely high requirements for real-time performance. Under the traditional cloud computing model, the application transmits data to the cloud computing center, and then requests data processing results, which increases the system delay. In the case of driverless car applications, high-speed cars require milliseconds of reaction time. Once the system delay is increased due to network problems, serious consequences will result. Edge computing, in the vicinity of data producers, does not require the network to request the response of the cloud computing center, which greatly reduces the system delay. The popularity of Gigabit wireless technology guarantees the network transmission speed, which makes the edge service ratio Cloud services are more responsive.
3. Protect private data and improve data security. The security of data in IoT applications has been a key issue, and surveys show that about 78% of users are concerned that their IoT data is being used by third parties without authorization. In the cloud computing mode, all data and applications are in the data center, and it is difficult for users to perform fine-grained control over the access and use of data. With the popularity of smart homes, many homes install web cameras in their homes. If video data is directly uploaded to the cloud data center, the transmission of video data not only occupies bandwidth resources, but also increases the risk of revealing user privacy data. To this end, for the data security problem of the existing cloud computing model, the edge computing model provides a better privacy protection mechanism for such sensitive data. On the one hand, the user's source data first uses near data before uploading to the cloud data center. The edge node directly processes the data source to protect and isolate some sensitive data. On the other hand, the edge node establishes a functional interface with the cloud data, that is, the edge node only receives the request from the cloud computing center, and The results of the processing are fed back to the cloud computing center. This approach can significantly reduce the risk of privacy breaches.
However, edge computing is not a substitute for cloud computing. It is a supplement to cloud computing. Many services that require global data support are still inseparable from cloud computing. For example, in an e-commerce application, users can perform operations on their own shopping carts on edge nodes to achieve fast response times, while services such as product recommendation are more suitable for the cloud because it requires global data support.
Edge computing application
At present, the edge computing application is very extensive, and it is especially suitable for application scenarios with special service requirements such as low latency, high bandwidth, high reliability, massive connection, heterogeneous aggregation, and local security privacy protection.
1. Smart city
Smart cities use advanced information technology to realize the intelligent management and operation of cities. In 2016, Alibaba Cloud proposed the concept of “urban brainâ€, which essentially uses the city's data resources to better manage the city. However, the data that smart city construction relies on has the characteristics of diversification and heterogeneity of sources, and involves the privacy and security of urban residents. Therefore, applying edge computing model to process data at the edge of the network is a good solution. .
Edge computing has a rich application scenario in the construction of smart cities. In the urban road surface inspection, sensors are installed on the street lamps on both sides of the road to collect urban road surface information, and to detect environmental data such as air quality, light intensity, and noise level, and timely feedback to maintenance personnel when the street lamp fails. In intelligent transportation, the edge server manages and analyzes data in real time by running an intelligent traffic control system, and controls traffic information lights according to real-time road conditions to alleviate road traffic congestion. In driverless driving, if uploading sensor data to the cloud computing center will increase the real-time processing difficulty and be restricted by the network, the driverless mainly relies on the in-car computing unit to identify traffic signals and obstacles, and to plan the path. EdgeOSc is a system-level operating system for smart cities based on edge computing. It is divided into three parts, the underlying data sensing layer, the middle network interconnection layer and the top-level data application management layer. The operating system can effectively manage multi-source data in smart cities, and increase the scope and depth of data sharing to realize the value of data in smart cities.
2, smart manufacturing
Intelligent manufacturing is a very typical application area of ​​edge computing in the Internet of Things. Edge computing will promote the deep integration of IT and OT systems. Industrial robots are the basis for intelligent manufacturing. In recent years, industrial robots have shown a booming trend in the Chinese market. According to statistics, in 2016, the total consumption of industrial robots in the Chinese market reached 87,000 units, nearly one-third of the world's sales. The application fields of industrial robots are mainly concentrated in the automotive manufacturing, 3C industry, logistics, metal processing, plastics and chemical industries. The robots complete the handling and loading and unloading, assembly and disassembly, welding and other working environments, automation / execution accuracy and safety. A very high work scenario is required. Industrial robots need the ability to cope with complex on-site environments and combine them with current workflows for comprehensive analysis and judgment, as well as the ability to collaborate with other robots to perform complex work tasks. All of these require robots equipped with intelligent controllers to perform complex computational tasks. For applications that use dozens of robots in a factory environment, if each robot is equipped with a sophisticated intelligent controller, this will increase the cost of the robot. However, if the edge technology is adopted, the intelligent controller function of the industrial robot is centrally deployed in the edge node of the production workshop, and centralized control can be realized while ensuring the delay, and the linkage cooperation between the robots can be completed, which can greatly reduce the industrial robot. Development, deployment, and maintenance costs.
3, smart home
In the current smart home, smart home appliances are basically composed of smart items, such as password lock, smart lighting, smart air conditioner, security monitoring, smart bathroom, indoor environment monitoring, home theater multimedia system, etc. The device needs to rely on the cloud platform to realize remote control of the mobile terminal on the external network. This smart home based on the cloud platform will not be able to control when the network is faulty. In particular, the scenario where multiple smart items are linked will not be able to coordinate multiple devices. Smart home appliances are connected to the cloud/data center through Wi-Fi modules. Users also have concerns about leakage of home data stored in the cloud/data center. In addition, a large amount of surveillance video data also consumes smart home devices to cloud/data. Communication bandwidth between centers. Using edge computing technology, home video data can be stored on the local edge computing gateway device to ensure that the user's privacy is not leaked; the linkage between multiple smart items can also be coordinated in near real time through local edge calculation; edge calculation Nodes can also periodically update control and device status information in sync with cloud computing.
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