What is edge computing?

On August 15, 2019, CBInsights, a well-known venture capital research organization, wrote an article detailing the development and application prospects of edge computing. The article stated that cloud computing is no longer enough to instantly process and analyze the data generated or to be generated by IoT devices, connected cars and other digital platforms. At this time, edge computing can come in handy. This technology has the potential to be used in many industries and play a huge role.

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Sometimes faster data processing is a luxury - sometimes it is a matter of life and death.

For example, a self-driving car is essentially a high-performance computer on wheels that collects data through a large number of sensors. In order for these vehicles to operate safely and reliably, they need to react immediately to their surroundings. Any delay in processing speed can be fatal. While data processing from connected devices now largely takes place in the cloud, it can take seconds to transfer data back and forth between central servers. This time span is too long.

Edge computing makes it possible for self-driving cars to process data faster. This technology enables connected devices to process data formed at the "edge," which is within the device or much closer to the device itself.

It is estimated that by 2020, each person will generate an average of 1.5GB of data per day. As more and more devices connect to the Internet and generate data, cloud computing may not be able to fully process that data—especially in certain use cases that require very fast processing of data.

Edge computing is another optional solution besides cloud computing, and its application scope is likely to extend far beyond driverless cars in the future.

A number of tech giants, including Amazon, Microsoft and Google, are exploring "edge computing" technology, which could trigger the next massive computing race. While Amazon Web Services (AWS) still dominates the public cloud space, it remains to be seen who will become the leader in this emerging edge computing space.

In this article, we’ll take a closer look at what edge computing is, the benefits associated with the technology, and its applications across a variety of industries.

A computing field full of changes

Before understanding edge computing, we must first look at how its predecessor - cloud computing - has contributed to the global Internet of Things ( IoT) devices paving the way.

Cloud computing empowers the connected world

From wearable devices to connected kitchen appliances, connected devices are everywhere. It is estimated that the global IoT market will exceed US$1.7 trillion by 2019, more than tripling from US$486 billion in 2013.

As a result, cloud computing—the process by which many smart devices connect to the Internet to function—has become an increasingly mainstream trend.

Cloud computing enables companies to store and process data (and other computing tasks) outside of their own physical hardware through a network of remote servers (commonly known as the "cloud").

For example, you can choose to use Apple's iCloud cloud service to back up your smartphone, and then you can retrieve the smartphone data through another Internet-connected device (such as your desktop computer) by logging in Your account is connected to the cloud. Your information is no longer limited by the capacity of your smartphone or desktop's internal hard drive.

This is just one of many cloud computing use cases. Another example is accessing a variety of complete applications via a web or mobile browser. As cloud computing becomes more and more popular, it has attracted large technology companies such as Amazon, Google, Microsoft and IBM to enter the market. According to a survey conducted by private cloud management company RightScale in 2018, among the major public cloud providers, Amazon AWS and Microsoft Azure ranked first and second respectively.

Illustration: More and more enterprises are running applications on public clouds

But centralized cloud computing is not suitable for all applications and use cases. Edge computing can provide solutions in areas where traditional cloud infrastructure may struggle.

The shift to edge computing

In a future where we are saturated with data, billions of devices will be connected to the Internet, so faster and more reliable data processing will become Crucial.

The consolidated and centralized nature of cloud computing has proven to be cost-effective and flexible in recent years, but the rise of the Internet of Things and mobile computing has put considerable pressure on network bandwidth.

Ultimately, not all smart devices need to utilize cloud computing to run. In some cases, this round-trip transfer of data can—and should—be avoided.

As a result, edge computing came into being.

According to CBInsights’ market size quantification tool, the global edge computing market size is expected to reach US$6.72 billion by 2022. Although this is an emerging field, edge computing may operate more efficiently in some areas covered by cloud computing.

Edge computing enables data to be processed at the closest point (such as a motor, pump, generator or other sensor), reducing the need to transfer data back and forth between clouds.

According to market research firm IDC, edge computing is described as "a mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage library, which covers less than 100 square feet."

For example, a train might contain sensors that immediately provide information about the status of its engine. In edge computing, sensor data does not need to be transmitted to a train or a data center in the cloud to see if something is affecting the operation of the engine.

Localized data processing and storage puts less pressure on computing networks. When less data is sent to the cloud, the potential for latency—delays in data processing caused by interactions between the cloud and IoT devices—is reduced.

This also puts more tasks on the hardware based on edge computing technology, which includes sensors for collecting data and CPUs or GPUs for processing data from connected devices.

With the rise of edge computing, it is also important to understand another technology involved in edge devices, which is fog computing.

Edge computing specifically refers to the computing process performed at or near the "edge" of the network, while fog computing refers to the network connection between edge devices and the cloud.

In other words, fog computing brings the cloud closer to the edge of the network; therefore, according to OpenFog, “fog computing always uses edge computing, while edge computing always uses fog computing.”

Back to our train scenario: Sensors can collect data, but they cannot act on the data immediately. For example, if a train engineer wants to understand how a train's wheels and brakes operate, he can use historical accumulated sensor data to predict whether parts need repair.

In this case, data processing uses edge computing, but it is not always instantaneous (unlike determining engine status). With fog computing, short-term analysis can be achieved at a given point in time without the need to fully return to a central cloud.

Illustration: Cloud Computing, Fog Computing and Edge Computing

So, what is important to remember is that although edge computing complements cloud computing and is very closely related to fog computing It operates independently, but it is by no means a substitute for the two.

Advantages of edge computing

Although edge computing is an emerging field, it has some obvious advantages, including:

·Real-time or faster Data processing and analysis: Data processing is closer to the source of the data rather than in an external data center or cloud, thus reducing latency.

·Lower costs: Enterprises spend less on data management solutions on on-premises devices than on cloud and data center networks.

·Less network traffic: As the number of IoT devices increases, data generation continues to increase at a record pace. As a result, network bandwidth becomes more limited, overwhelming the cloud and creating greater data bottlenecks.

·Higher application efficiency: With reduced lag, applications can run more efficiently and at faster speeds.

Reducing the role of the cloud also reduces the likelihood of a single point of failure.

For example, if a company uses a central cloud to store its data, and if the cloud goes down, the data will be inaccessible until the problem is fixed—and the company could suffer significant business losses as a result.

In 2016, the North American 14 site of the Salesforce website (also known as NA14) was down for more than 24 hours. Customers were unable to access user data, from phone numbers to emails and more, and business operations were severely disrupted.

Salesforce has since moved its IoT cloud to Amazon's AWS, but the outage highlighted a major drawback of relying solely on the cloud.

Reducing reliance on the cloud also means that some devices can run reliably offline. This can especially come in handy in areas with limited internet connectivity – whether in specific areas that are severely underserved or in often inaccessible remote areas such as oil fields.

Another key advantage of edge computing relates to security and compliance. This is particularly important as governments increasingly focus on how businesses exploit consumers' data.

The European Union’s (EU) recently implemented General Data Protection Regulation (GDPR) is one example. The regulation aims to protect personally identifiable information from data misuse.

Because edge devices are able to collect and process data locally, data does not have to be transmitted to the cloud. Therefore, sensitive information does not need to pass through the network, so if the cloud is attacked by a network, the impact will be less severe.

Edge computing also enables interoperability between emerging networked devices and older “legacy” devices. It converts the communication protocols used by legacy systems "into a language that modern networked devices understand." This means traditional industrial equipment can be seamlessly and efficiently connected to modern IoT platforms.

The development status of edge computing

Today, the edge computing market is still in its early stages of development. But as more and more devices come online, it seems to be getting more attention.

The companies that dominate the cloud computing market (Amazon, Google, and Microsoft) are becoming leaders in edge computing.

Last year, Amazon entered the edge computing field with AWS Greengrass and was at the forefront of the industry. The service extends AWS to devices so they can "process the data they generate locally while still using the cloud for management, data analysis, and persistent storage."

Microsoft also has some big moves in this area. The company plans to invest $5 billion in the Internet of Things over the next four years, including edge computing projects.

Microsoft has released its Azure IoT Edge solution, which "extends cloud analytics to edge devices" and supports offline use. The company also wants to focus on AI applications at the edge.

Google is not to be outdone. It announced two new products earlier this month intended to help improve the development of edge-connected devices. They are the hardware chip EdgeTPU and the software stack CloudIoTEdge.

Google said, “CloudIoTEdge extends Google Cloud’s powerful data processing and machine learning capabilities to billions of edge devices, such as robotic arms, wind turbines, and oil rigs, so they can The sensor data is manipulated in real time and the results are predicted locally.”

However, these three technology giants are not the only ones interested in getting involved in this field.

As more connected devices emerge, many players in the emerging ecosystem are developing software and technology to help edge computing take off.

Over the next four years, Hewlett Packard Enterprise will invest $4 billion in edge computing. The company's Edgeline Converged Edge Systems systems are targeted at industrial partners who want data center-class computing capabilities and often operate in remote locations.

Its system promises to provide insights from connected devices to industrial operations, such as oil rigs, factories or copper mines, without relying on sending data to the cloud or data centers.

In the emerging edge computing field, other major competitors include ScaleComputing, Vertiv, Huawei, Fujitsu and Nokia.

Artificial intelligence chip manufacturer NVIDIA launched Jetson TX2 in 2017, an artificial intelligence computing platform for edge devices. Formerly known as the Jetson TX1, it claims to "redefine what is possible in extending advanced AI from the cloud to the edge."

Many well-known companies are also investing in edge computing, including General Electric, Intel, Dell, IBM, Cisco, Hewlett Packard Enterprise, Microsoft, SAPSE and ATT.

For example, in the private market, Dell and Intel have invested in Foghorn, a company that provides edge intelligence for industrial and commercial IoT applications. Dell also participated in the seed round of financing for IoT edge platform IOTech.

Many of the companies mentioned above, including Cisco, Dell and Microsoft, have also joined forces to form the OpenFog Alliance. The organization's goal is to standardize the use of this technology.

Applications of edge computing in various industries

As sensor prices and computing costs continue to decline, more “things” will be connected to the Internet.

As more connected devices become available, edge computing will be increasingly used in various industries, especially in areas where cloud computing is inefficient.

We are already starting to see the impact of this technology across a number of different industry sectors.

“When we bring the power of the cloud to devices (ie, the edge), we can bring the ability to respond, analyze and act in real time, especially in areas with limited or lack of network conditions. It’s still early days, but we’re starting to see these new capabilities being applied to solve some of the biggest challenges around the world.” - Kevin Scott, Chief Technology Officer, Microsoft

From From self-driving cars to agriculture, here are several industries set to benefit from the potential of edge computing.

Transportation

One of the most obvious potential applications for edge computing technology is transportation—more specifically, self-driving cars.

Autonomous vehicles are equipped with a variety of sensors, from cameras to radar to laser systems, to help the vehicle operate.

As mentioned earlier, these self-driving cars can use edge computing to process data closer to the vehicle through these sensors, thereby minimizing the system's response time during driving. Although self-driving cars are not yet a mainstream trend, companies are preparing for them.

Earlier this year, the Automotive Edge Computing Consortium (AECC) announced the launch of a project focused on connected car solutions.

“Connected cars are rapidly expanding from luxury models and high-end brands to high-volume mid-range models. The automotive industry will soon reach a tipping point, when the amount of data generated by cars will exceed the current Cloud, computing and communications infrastructure resources.” - AECC Chairman and President Kenichi Murata

Members of the alliance include DENSOCoration, Toyota Motor, ATT, Ericsson, Intel and other companies.

But it’s not just self-driving cars that will generate large amounts of data and need to be processed in real time. The same goes for planes, trains, and other forms of transportation—whether or not they are piloted by humans.

For example, aircraft manufacturer Bombardier's C Series aircraft are equipped with a large number of sensors to quickly detect engine performance problems. During the 12-hour flight, the aircraft generated up to 844TB of data. Edge computing enables real-time processing of data, so the company can proactively address engine issues.

Healthcare

Nowadays, people are increasingly wearing fitness trackers, blood glucose monitors, smart watches and other wearable devices that monitor their health.

But to truly benefit from the vast amounts of data being collected, real-time analysis may be essential - many wearables connect directly to the cloud, but there are also others Run offline.

Some wearable health monitors can analyze pulse data or sleep patterns locally without connecting to the cloud. Doctors can then assess the patient on the spot and provide immediate feedback on the patient's health.

But in healthcare, the potential of edge computing extends far beyond wearables.

Think of how fast data processing could benefit remote patient monitoring, inpatient care, and medical management in hospitals and clinics.

Doctors and clinicians will be able to provide faster, better care to patients, while providing an additional layer of security for the health data generated by patients. On average, there are more than 20 Internet-connected devices in hospital beds, which generates a large amount of data. The processing of this data will occur directly closer to the edge, rather than sending confidential data to the cloud, thus eliminating the risk of inappropriate access to the data.

As mentioned earlier, localized data processing means that widespread cloud or network failures will not affect business operations. Even if cloud operations are disrupted, these hospital sensors can function independently.

Manufacturing

Smart manufacturing promises insights from the vast array of sensors deployed in modern factories.

By reducing lag, edge computing may enable manufacturing processes to respond and change more quickly, enabling real-time application of insights from data analysis and real-time actions. This may include shutting down the machine before it overheats.

A factory can use two robots equipped with sensors and connected to an edge device to perform the same task. The edge device can predict whether one of the robots will fail by running a machine learning model.

If the edge device determines that the robot is likely to malfunction, it can trigger actions to stop or slow down the robot's operation. This will allow factories to assess potential failures in real time.

If robots can process data themselves, they may also become more self-sufficient and responsive.

Edge computing should support more insights from big data faster, as well as support the application of more machine learning technologies to business operations.

The ultimate goal is to tap into the immense value of the massive amounts of data generated in real time, prevent safety hazards, and reduce machine interruptions on the factory floor.

Agriculture and Smart Farms

Edge computing is well suited for agriculture because farms are often in remote locations and harsh environments where there may be bandwidth and network connectivity issues.

Today, smart farms that want to improve network connectivity need to invest in expensive fiber optics, microwave connections, or have a satellite that operates around the clock; edge computing is a suitable, cost-effective alternatives.

Smart farms can use edge computing to monitor temperature and equipment performance, and automatically slow down or shut down various equipment (such as overheating pumps).

Energy and Grid Control

Edge computing may be particularly effective across the energy industry, especially in safety monitoring of oil and gas facilities.

For example, pressure and humidity sensors should be closely monitored and there should be no room for error in connectivity, especially given that most of these sensors are located in remote locations.

If an abnormality—such as an overheating oil pipe—is not noticed in time, a catastrophic explosion could occur.

Another benefit of edge computing is the ability to detect equipment failures in real time. Through grid control, sensors can monitor the energy produced by everything from electric vehicles to wind farms, helping to make decisions to reduce costs and increase energy production efficiency.

Applications in other industry sectors

Other industries that can take advantage of edge computing technology include finance and retail. Both industries, which use large customer and back-end data sets to provide everything from stock picking information to in-store apparel placement, could benefit from reducing their reliance on cloud computing.

Retail can use edge computing applications to enhance the customer experience. With many retailers working to improve the in-store experience these days, optimizing the way data is collected and analyzed definitely makes sense for them — especially given that many are already experimenting with connected smart displays.

In addition, many people use point-of-sale data generated by in-store tablets, which is transferred to the cloud or data center. With edge computing, data can be analyzed locally, reducing the risk of sensitive data being leaked.

Summary

From wearable devices to cars to robots, IoT devices are showing increasingly strong development momentum.

As we move towards a more connected ecosystem, data generation will continue to increase rapidly, especially as 5G technology takes off, further accelerating network connectivity. While centralized clouds or data centers have traditionally been the preferred choice for data management, processing and storage, both options have limitations. Edge computing could serve as an alternative solution, but since the technology is still in its infancy, it is difficult to predict its future development.

Challenges in equipment capabilities—including the ability to develop software and hardware that can handle the computing tasks offloaded to the cloud—are likely to arise. Being able to teach machines to switch between computing tasks that can be performed at the edge and those that need to be performed in the cloud is also a challenge.

Even so, as edge computing becomes more adopted, enterprises will have more opportunities to test and deploy this technology in various fields.

Some use cases may prove the value of edge computing better than others, but overall, the technology’s potential impact on our entire connected ecosystem could be earth-shaking.

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MEC test?

MEC is an edge computing technology (MobileEdgeComputing), and MEC is a test that combines the native features of MEC's ??platform traffic routing and billing with 5G cloud functions. It is the second phase of the 5G technology test. key content.

MEC is a key technology that supports operators in 5G network transformation to meet the development needs of high-definition video, VR/AR, industrial Internet, Internet of Vehicles and other businesses. With the formation of the 5G core network SBA architecture and the rapid development of cloud computing, the current technical form of edge computing has been formed. Through the comprehensive deployment of MEC in key cities across the country, 5G can shine.

What does edge computing power mean?

Edge computing refers to algorithms bounded by the "edge" of the network, such as calculations performed inside smart gateways and cameras. However, it is not realistic to store or use all the data collected by these devices for calculation. There is too much interference or redundant information, and if not processed properly, the processing effect will be counterproductive.

Take the Haipu forest fire monitoring system as an example. Terabytes of video data are transmitted through the built-in pyrotechnic recognition processor, but the really valuable data is only a few megabytes that cause suspicion or illegal activities, and edge computing can handle the target data of interest very well.

In addition, compared with cloud computing, edge computing can also reduce congestion on network traffic and "leave room" for the execution of more critical tasks.