With the rollout of 5G and edge computing technologies, companies are now looking to take advantage of both approaches while boosting performance for their applications.

How Edge Computing Tackles Latency

Enterprises have benefited from cloud computing during the past decade by centralizing resources at data centers owned by cloud providers — saving money on management costs and avoiding capital expenditures needed for internal data centers. But centralization has led to performance issues when dealing with endpoints on the internet’s “edge,” such as sensors and mobile devices.



Note: Reducing the tail of distribution down to the edge is what makes edge computing appealing.

the round-trip time between a smartphone and cell tower is about 12 to 15 milliseconds over a 4G LTE network, and can be longer depending on legacy systems and other factors. However, when you ping the data center from your smartphone, this could take anywhere between 100 milliseconds to 500 milliseconds, even up to a full second in some cases.

How Edge and 5G can Boost Business Apps

When you combine the speed of 5G with edge computing’s processing capabilities, it’s only natural to focus on applications that require low latency. This is why early use cases tend to involve AR/VR, artificial intelligence, and robotics, which require split-second decisions from computing resources. But there’s potential for a variety business apps to benefit from both edge and 5G.

“In on-premises edge, there are many applications that already exist which could potentially be ‘moved’ or leverage a mobile edge compute,” said Dalia Adib, principal consultant and practice lead for edge computing at STL Partners. “There is a sweet spot of use cases — for example, those that use video, and AI.”

Use cases for edge computing in the enterprise

  • Businesses with capital-intensive assets in industries such as manufacturing, oil and gas, and energy using 5G and edge for maintenance and repair activities. This includes AR/VR apps to guide technicians through repair, as well as drones for visual inspections of rail lines, bridges, or buildings using advanced analytics to identify potential defects or items in need of maintenance.
  • Real-time process optimization in manufacturing facilities. Data generated from smart, connected equipment can dynamically adjust calibration settings, increasing yield and reducing defects.
  • Condition-based monitoring — using sensors to check certain parameters on an asset or machine to ensure it’s working properly.
  • Video analytics for surveillance, such as using real-time processing to determine whether a person entering a building is an employee or a visitor and to confirm the identity of employees.
  • Video analytics to provide real-time advice for law enforcement decision-makers in emergency situations.
  • Telehealth applications in healthcare — using video and analytics to diagnose a patient, or to conduct remote patient monitoring.

The development of edge-native applications that are built to take advantage of edge computing’s strengths, such as low latency and bandwidth scalability. These apps will likely drive demand for 5G networks and edge computing growth, he said.

By leveraging edge computing, the computing resources that can be brought to bear in this task can be far larger, heavier, more energy-hungry and more heat-dissipative than could ever be carried or worn by a human user.”