On the surface, the value of edge computing is relatively simple; by placing computing power closer to users, they experience better latency and performance. However, as you get past this seemingly simple goal of improving latency, you’re left with a task that’s more complex to implement and that has profound impacts on how we interact with data.

One example of how edge computing impacts data is in machine learning. To make accurate predictions, machine learning algorithms need to process large data sets. However, the storage and processing of these data sets are hindered if devices need to transmit back and forth to the cloud to access processing power. Edge computing allows organizations to process data on-premises and then later upload it to the cloud for wider access.

The demand for real-time digital experiences and lower latency computing are more than centralized cloud solutions can handle. This reality and the growth in compute power portability have taken computing at the edge from a good idea to mass adoption. But, how does edge computing work?

Digging Deeper Into How Edge Computing Works

To understand edge computing, we need to know what the edge refers to. The edge is where user devices come in contact with the network. For businesses, this contact point could be a manufacturing plant, office, medical facility, or anywhere a business has large compute and data transfer needs. The goal of edge computing is to place servers closer to where users are accessing the network.

If we think of edge computing in layers, it can help us visualize how everything works together to improve latency. The first layer is built on the high-powered servers in a centralized data center that handles extreme amounts of data processing. The next layer is fog computing which covers the network connections and computing power required to bring the data from the edge to its destination. The third layer pushes computing power even farther out to the edge, where customers interact with the network. This could be at the city, organization, or even building level.

Edge computing helps improve the metric that organizations care about — response time. To illustrate, imagine a machine learning algorithm is working on the preliminary diagnosis of cancer patients. Since the speed of diagnosis is crucial to better patient outcomes, medical facilities must have the on-premise edge infrastructure to analyze the data quickly. Edge computing helps speed up computations when compared to working in the cloud and helps ensure critical functions are performed as fast as possible.

Edge computing has changed the network strategy of the modern organization. Today, a comprehensive network strategy must not only factor in hardware and software but also the server node locations that will best serve its customers.

Two team members working through a strategy together.

It’s exciting for organizations to consider the benefits of edge computing, but we shouldn’t overlook its challenges. Leaders who make these challenges part of their planning are more likely to have a positive experience from their edge implementation. Let’s look at three of these challenges:

  1. Logistical challenges. Edge computing infrastructure is widely distributed, and organizations may have to manage hundreds of smaller locations instead of a few large data centers. They must take into account the security, cost, and maintenance of machines located around the globe.
  2. Minimal technical support. Edge computing sites are typically operated without on-site technical support. This can present challenges if your infrastructure doesn’t have robust self-repairing capabilities or when local support isn’t readily available.
  3. Strategic design decisions. When choosing the design of edge nodes, architects must consider how variations will impact training, troubleshooting, and management. A repeatable design is often better since it means that issues can be documented and resolved more quickly at scale.

Despite the challenges, edge computing is often more cost-effective and energy-efficient than centralized data centers since the power required to transfer data is reduced, and it requires less energy to control the temperature of these distributed edge nodes.

Simplify Your Edge Computing Supply Chain

The need for edge computing is too powerful to ignore. As technology becomes more powerful, applications demand real-time performance and lower latency data transmission. Centralized computing isn’t fast enough to handle machine learning applications in the manufacturing, industrial, and medical industries. To fuel future innovation, organizations must embrace the power of edge computing and solve the challenges that come with it.

The new reality of edge computing means organizations must find ways to scale quickly and cost-effectively. At Intequus, we help simplify scaling your edge computing infrastructure by custom engineering edge node servers to client needs, managing relationships between vendors and stakeholders, and ensuring your hardware is maintained over its entire life cycle. If you want to talk about your edge computing strategy, our team would be happy to chat. Contact us to learn more.

Call Now Button