Existing on-premises and centralized cloud infrastructures cannot support the huge computing needs of these powerful applications that require low latency (or data transfer latency) to smoothly transfer and access data in real time. .. To reduce latency and bandwidth usage and keep costs down, computing power and processes need to be closer to the physical location of the data. solution? Transfer computing power to local infrastructure at the “edge” of the network, rather than relying on remote data centers.
According to Frost & Sullivan, a whopping 90% of industrial enterprises will use edge computing technology by 2022, while a recent IDC report (registration required) shows that 40% of all organizations will invest in edge computing next year. I found out. “Edge computing is needed to enable the next generation of the Industrial Revolution,” said Bike Xie, vice president of engineering at AI technology vendor Kneron. He said the future of AI and other automation technologies will depend on the distributed edge, depending on whether the Internet of Things and other devices are connected to distributed network nodes or implement AI-enabled chips that can autonomously build algorithmic models. Explains.
“Edge computing complements the cloud,” says Xie. “Like the cloud, edge technology requires application manufacturers to acquire and apply data-driven knowledge that enhances smart factories and products.”
Manufacturing moves to the edge
The move to edge computing is the result of major changes in the manufacturing industry over the last two decades. Whether manufacturing industrial products, electronic equipment, or consumer goods, manufacturers slowly but steadily enhance system and process automation and self-monitoring to produce products, maintain equipment, and supply. Increases the efficiency of optimizing all links in the chain. ..
As manufacturers implement more sensor-based automation-driven devices, they generate more data than ever before. However, in many cases, datasets from sensor-based devices to centralized systems can quickly become cumbersome, slow to automate, and cause real-time applications to stop working.
Edge computing gives manufacturers flexible choices about how data is processed, eliminating time lags, reducing bandwidth usage, and choosing which data can be discarded immediately after processing, Xie says. “Manufacturers can quickly process data at the edge if sending data to the cloud is the bottleneck, and move specific data to the cloud if latency and bandwidth are not an issue.” Where to use the data Processing close together not only saves bandwidth and costs, but also makes the data more secure because it is processed immediately.
By 2023, IDC predicts that more than 50% of new enterprise IT infrastructure deployed will be at the edge of the enterprise’s data center, starting at less than 10% in 2020.
An example of the cloud-to-edge switch is by Paul Savill, Senior Vice President of Product Management and Services at Lumen, a technology company that provides edge computing platforms. Lumen recently installed in a newly built million square foot factory. Robot systems from about 50 different manufacturers rely on edge computing “because they must be within 5ms of the delay to control the robot accurately.” This deployment provides a secure connection from edge applications to robot manufacturers’ data centers to “gather information in real time.”
But for long-term data storage, machine learning and analytics applications, all of this happens in the public cloud, Savill says. Other larger workloads are processed in “huge computing power” big data centers that can quickly process huge amounts of data.
“The chain from public cloud to edge computing to on-premises is very important,” says Savill. “This allows our customers to take advantage of the latest advanced technology to save money and achieve incredible efficiency.”
Edge Computing: Moving the Future of Manufacturing
Source link Edge Computing: Moving the Future of Manufacturing