Over the past decade, cloud computing advances have led a centralized approach to system operations and administration, while the development of mobile computing, the internet of things (IoT), SaaS have driven computing toward a distributed architecture. With the introduction of edge computing and 5G technologies, companies are now trying to avail both approaches while elevating performance for their applications.
The hype around edge and 5G tend to focus on innovation. Experts say cutting-edge applications in autonomous vehicles, virtual or augmented reality (VR/AR), and robotics go beyond these applications to provide IT professionals a vast array of opportunities.

How Edge Computing Deals with Latency

Enterprises have profited from cloud computing over the past years by centralizing resources at data centers owned by cloud providers. Internal data centers focus on avoiding capital expenditures and saving funds on management costs. But, centralization has driven to performance issues to deal with endpoints on the internet's 'edge,' including IoT sensors/devices and mobile devices.
Even though nowadays, smartphones are potentially intelligent computers that fit perfectly in your pocket, they still lack enormous processing done in the cloud. A computer science professor at Carnegie Mellon University, Mahadev Satyanarayan, asked, "Why can't you put all the intelligence at the end? In other words, why can't your smartphone just do it?"

Answering the question, he said, "The answer is to do computing that you want to be done; you need far more computing resources than you would carry with you on your smartphone." He added, "If you think about the video camera on your smartphone, it's extremely light. But, if you want to do the real-time video analytics on it, you couldn't do it with the computer on the phone today- you would ship the data to the cloud, and that's where the problem begins."

A solution was outlined in an influential 2009 IEEE Pervasive Computing article (co-authored by Satyanarayanan) is to use virtual machine-based 'cloudlets' in mobile computing. In other words, placing mini data focuses on the network's edge close to where their processing power is required.

Satyanarayanan, on average, explained that the time travel to and from a smartphone and cell tower is around 12 to 15 milliseconds on a 4G LTE network, and can be longer based on legacy systems and other factors. However, when you try to connect your smartphone with the data centre, it can take between 100 and 500 milliseconds. In some cases, it even takes up to an entire second.

What makes edge computing appealing is the reduction in the tail of distribution.

Data Transmission Speed on 5G Network

The moving intelligence concept to the edge did not catch on until four years ago. That's when telecommunication companies realised the necessity of 5G speeds and began making plans for 5G wireless.
While data travel time over 4G is between 12 to 15 milliseconds, vendors are touting latency level of 2 to 3 milliseconds with 5G. However, round-trip time from a distant data centre can still take anywhere between 100 and 500 milliseconds or so. "It makes no point if you have to return to a data centre around the country or other ends of the globe, even it is only a matter of milliseconds," Satyanarayanan said.
Agreeing with Satyanarayanan, Research Director for Edge Strategies at IDC, Dave McCarthy stated, "By itself, 5G reduces the network latency between the mobile tower and the endpoint, but it does not advocate the distance to a data centre, which can create troubles for latency-sensitive applications."

He added, "By deploying edge computing into the 5G network, it reduces this physical distance, greatly improving response time." This makes edge computing pivotal for the rollout of new mobile edge computing (MEC) services and 5G networks.

Experts say it's crucial to understand that 5G and edge computing are not connected at the hip. Wherein 5G networks require technologies of edge computing to succeed; edge computing is operational on different networks such as 4G LTE, Wi-Fi, and other network types.

How do 5G and Edge Boost Business Apps?

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

"In on-premise edge, there are many applications that already exist which could essentially be 'moved' or leverage a mobile edge computing," stated Dalib Adib, Practice Lead for edge computing at STL Partners. "

There is a sweet spot of use cases, for instance, those that use video, AI, and IoT," he added.
Experts cite a vast range of use cases for edge computing in the enterprise, including:

• Real-time process optimization in production facilities. Data generated from smart, connected equipment dynamically can not only adjust calibration settings but also increase yields and reduce defects.
• Condition-based monitoring- using IoT devices/sensors to check specific parameters on a machine to ensure it's working properly.
• Business with capital-intensive assets in industries like manufacturing, oil and gas, and energy using edge and 5G for repair and maintenance purposes. This includes AR/VR applications to guide technicians through repair and drones for visual inspections of bridges, buildings, or rail lines using advanced analytics that help identifying potential defects or products in need of maintenance.
• Video analytics for surveillance-for example, using real-time processing to determine if an individual entering a building is an employee or a visitor, and it ensures the identity of employees.
• Video analytics to serve real-time advice for law enforcement decision-makers in an emergency. (Watch this video clip from 60 Minutes elaborating wearable cognitive assistants.)
• Applications of telehealth in healthcare- using video and analytics in diagnosing a patient, or conducting remote patient monitoring.

Satyanarayanan anticipates the development of edge-native applications that are made to take advantage of edge computing's strengths, like bandwidth scalability and low latency. These apps are likely to drive demand for the growth of edge computing and 5G networks.