Edge computing, the process of computing on local servers and devices at the “edge” of the network rather than in remote cloud data centers, is rapidly becoming a leading solution to power the enormous capacity and complexity of local network technologies, especially Internet of Things devices.

Although edge computing is becoming more popular and accessible, is it a durable solution for efficient and accurate data processing? This article will discuss the pros and cons associated with edge computing and consider how edge devices affect the overall structure of network data in a cloud-based world.

Edge computing advantages

1. Improve response time and latency on all devices

One of the biggest advantages of edge computing is that data processing takes place at a more local level, requiring less time and shorter latency for both the device and the user. Edge computing is especially useful for iot devices, such as smart homes, where it can respond more quickly to user queries when data doesn’t have to travel to distant cloud data centers.

Let’s consider a scenario where a reduction in latency improves the overall experience. Take robotic surgery, for example. Certain movements during surgery require not only precision, but also speed and efficiency. If the surgery is performed at a hospital in Boston, but the main data center of the hospital network is in San Francisco, the robot performing the surgery may be delayed because of the distance it takes to process the data. By performing data processing on a nearby edge server, these surgical steps can more closely simulate the response time of a trained surgeon performing these critical operations.

As a growing number of iot devices and AI software demand faster response times, Edge computing is meeting this need by fostering more computing, network access, and storage capabilities near the data in question.

2. Reduced data volume reduces enterprise security risks

Enterprise networks typically have powerful clouds and internal data centers with extensive storage and processing power. Logically, the more data is stored in these data centers, the more security infrastructure is needed to prevent network security holes. Large amounts of centralized data often mean greater risk, increased time spent sorting through unwanted data in the cloud, and greater investment in the enterprise’s security architecture.

Edge computing takes some of the security pressure off a data center by processing and storing data at the local server or device level. Only the most important data is sent to the data center, while more irrelevant data, such as hours of inert security video, remains local. As a result, edge computing results in a reduction in the total amount of data migrated to the cloud, which means less rogue data to monitor and manage.

3. Reduced bandwidth reduces transmission costs

Beyond the promise of simplifying the cloud security model, edge computing offers significant cost savings by reducing bandwidth. Edge computing requires less bandwidth at the data center level because so much data is now processed and stored in localized servers and devices without the need to transfer most of the data to the data center. Since there is less data in the cloud and more data is processed locally, data centers can save bandwidth capacity and avoid costly upgrades to their cloud storage capabilities.

4. Achieve economies of scale through edge devices

Many edge devices already exist, and more and more are entering the market for professional and personal use. Here are a few ways edge technologies can help expand enterprise and public computing possibilities:

1) Edge devices can process data locally or through edge network sensors in a LAN, even if more central networks and nodes fail. As a result, local operations can remain up and running while larger data center issues are addressed.

2) Edge devices can process less cumbersome data requests locally, freeing up bandwidth for the larger data processing needs of existing data centers.

3) Edge devices process data in almost every corner of the globe, allowing for broader access, faster results, and overall user satisfaction.

4) Since many users and local communities have actively invested in edge technologies, businesses may not need to invest in these devices to get the scalability they want.

Disadvantages of edge computing

1. Geographic differences: fewer network devices and skilled implementors

While edge computing offers more opportunities for locality-level data processing and storage, certain geographic areas may be at a disadvantage when it comes to edge implementation. In areas with fewer human, financial, or technical resources, there may be fewer active edge devices and local servers on the network. Many of the same fields will also have fewer skilled IT professionals capable of starting and managing local edge network equipment.

The history of limited network capacity can become a vicious circle. Fewer and fewer it professionals want to migrate or build complex network models in areas with little network infrastructure. As a result, historically poor, uneducated, uninhabited and/or rural areas may fall further behind in their ability to process data through marginal devices. So the growth of edge computing is another way in which structural inequality is rising, especially as it relates to the accessibility of life-changing ARTIFICIAL intelligence and Internet of Things devices.

Security vulnerabilities that are harder to prevent and monitor

While edge computing provides security advantages by minimizing the amount of data that needs to be protected in the data center, it also raises concerns about the security of each local point on the edge network. Not every edge device has the same built-in authentication and security features, which makes some data more vulnerable to corruption.

Edge devices are also often more difficult to pinpoint at an enterprise level, making it difficult to monitor localized devices that use enterprise data and determine whether they are following the security policies of the enterprise network. For organizations committed to implementing a zero-trust approach to network security, devices with limited authentication capabilities and visibility on the network can pose a challenge to overall network security.

3. Data loss with potential energy

Useless data is usually discarded after processing on edge devices and never sent to a cloud data center for storage. But what if the edge device misevaluates the usefulness of the data set? What if this data is useful for the future?

Mining all your existing data in a cloud data center can be frustrating, but its central storage also gives you the peace of mind that your data will be there whenever you need it. While edge computing processes save space and financial resources in terms of storage, critical data can be accidentally misinterpreted and lost by edge devices.

4. Cost and storage capacity requirements

Whether investing in large enterprise clouds or distributed edge devices to meet computing needs, networking technology is always a significant investment. Investing in a more robust edge network will certainly save money at the data center bandwidth level, but this approach requires its own significant expense to start and maintain edge equipment. Edge devices may require more hardware and software to achieve optimal performance and local storage requirements, and costs can rise rapidly when they are distributed across multiple local geographic locations.

About the development trend of edge computing

Edge computing has both advantages and disadvantages, but most INFORMATION technology experts don’t think it’s going away, especially with 5G access expected to grow in the near future. More users are constantly using a wider variety of devices, which means that edge computing and how it is used are changing frequently.

Are you interested in learning more about developments at the edge of technology and how they are affecting an area of technology that was once focused only on in-house deployments and cloud computing?

Read more: Cloud computing and intelligent video analysis calculation combined with the edge of the monitoring system in the age of 5 g can be used in many industries, one of the most obvious is the factory manufacturing, industry and Internet industry, video monitoring, we intensified the efforts on industry and the management of the Internet of things can reduce labor costs, improve work efficiency, and improved the security of the working environment for employees, It also promotes the development of the Internet of Things industry.

In areas such as periodic maintenance and business decision support, cloud computing can focus on non-real-time, long-term analysis of video surveillance big data; In terms of real-time intelligent processing of local business, EasyNVR edge computing can play to the advantages of real-time, short-cycle video data processing.

EasyNVR video edge computing gateway makes different devices interconnected, and finally makes the whole manufacturing process digital, flexible, flexible, to meet the personality needs of the digital age.