Everything to Know About Edge Computing

What’s the Deal About Edge Computing?

Edge computing is a distributed information technology (IT) architecture in which customer data is processed at the edge of the network as close to the source as possible. Data is the lifeblood of modern enterprises, providing valuable business information and supporting real-time control of critical business processes and procedures. Today’s businesses are awash in an ocean of data, and IoT sensors and devices that operate in real-time from remote locations and inhospitable operating environments can routinely capture huge amounts of data almost anywhere in the world.

This flood of virtual data is also changing the way companies deal with computing. The traditional computing paradigm, based on a centralized data center, and the everyday Internet is not suitable for moving real data streams in constant growth. Bandwidth limitations, latency issues, and unpredictable network outages can hamper these efforts. Enterprises are responding to these data challenges by using edge computing architectures.

In simple terms, edge computing moves some computing and storage resources away from the central data center and closer to the source of the data itself. Rather than submitting raw data to a central data center for processing and analysis, this work is done where the data is generated, be it in a retail store, factory, expanding utility company, or a business. Only the output of this edge computing, such as real-time business insights, device health predictions, or other actionable responses, is sent to the main data center for review and other human interactions.

Edge computing is changing computing and business computing and this article covers everything you need to know about it.

How Does Edge Computing Work?

Edge computing is a matter of location. In traditional business computing, data is generated at a customer endpoint, such as a user’s computer. This data is transmitted over a WAN, such as the Internet, through the company LAN, where the data is stored and processed by a company application. The results of this job are sent back to the client’s endpoint. This remains a tried and tested approach to client-server computing for most typical business applications.

But the number of devices connected to the Internet and the volume of data produced by these devices, and used by businesses, is growing too fast for conventional data center infrastructures to adapt. Gartner predicts that by 2025, 75% of the data generated by a company will be created outside of central data centers. The prospect of moving so much data into situations that are often sensitive to time or disruption puts incredible pressure on the global Internet, which is often exposed to congestion and disruption.

IT architects have shifted the focus from the central data center to the logical edge of the infrastructure by taking compute and storage resources out of the data center and relocating these resources to the point where data is generated. The principle is simple: If you can’t bring the data closer to the data center, move the data center closer to the data. The concept of edge computing is not new and has its roots in decades-old remote computing ideas, such as remote offices and branch offices, where it was more reliable and efficient to put computing resources in the desired location than to rely on a single central office location.

Edge computing places storage and servers where data resides and often requires little more than a partial rack of equipment to work on the remote LAN to locally collect and process data. In many cases, computer equipment is used in armored cabinets to protect the equipment from extreme temperatures, humidity, and other environmental conditions. The processing often includes normalizing and analyzing the data flow to search for business intelligence, with only the analytical results sent to the main data center.

The idea of ​​business intelligence can be very different. Some examples include retail environments where showroom video surveillance could be combined with actual sales data to determine the most desirable product configuration or consumer demand. Other examples are predictive analytics that can guide equipment maintenance and repair before actual defects or failures occur. Still, other examples are often associated with utilities such as water treatment or power generation to ensure equipment is operating properly and production quality is maintained.

Advantages of Edge Computing

Sometimes customers ask what makes edge computing different. The primary benefit of edge computing is reducing the risk of network outages or cloud delays when highly interactive and timely experiences are critical. Edge enables these experiences by incorporating intelligence and automation into the physical world. Consider optimizing factory operations in a factory, controlling robotic surgery on a patient, or automating production in a mine.

And when super speed and reliability aren’t convincing enough, here are three other unique Edge attributes:

  1. Unmatched data control: Edge is the first point where computers attack the data source and determines how much of the original fidelity is preserved when the analog signal is digitized. Here we determine what data is stored, obfuscated, summarized, and forwarded. This is also where we can add controls to account for data reliability, privacy, and regulations. For example, when unlocking a smartphone using facial recognition, it is better to keep the data out of the way. AI models are trained on each user’s face without these images leaving the device. Because data is never transmitted beyond our phones, our privacy is preserved and security breaches in the cloud are avoided.
  1. Favorable Physical Laws: Edge is always on and has low latency due to reduced network availability, round-trip times, and bandwidth restrictions. For example, a team implemented a visual analysis algorithm on a production line in a factory to find defects in car seat manufacturing. As seats moved through a production line, they implemented their low latency deep learning inference models at the edge to automate real-time fault detection. The solution kept up with production line uptime and speeds that only edge computing could enable.
  1. Lower costs: Edge processing makes uploading and cloud storage cheaper. Why pay for full loyalty data when you may only need a summary view or key information? When working on an Edge deployment, the cost-saving power of Edge was observed. It was an oil company whose oil wells could only be accessed by air, some by satellite, others by helicopter. Data storage was limited and immediate data transfer, if possible, was expensive. Edge computing was chosen to get done with tasks at hand and cut down costs drastically.

What Does the Future Hold?

Edge computing became famous with the advent of the IoT and the sudden flood of data these devices produce. However, since IoT technologies are still in their infancy, the development of IoT devices will also have an impact on the future development of Edge computing. So, does Edge computing mean the end of cloud computing? Definitely not! Not only is cloud computing a critical component for Edge management, but Edge computing will also fuel the next wave of cloud computing.