Broadcom Software Shows Why the Move to the Edge is Accelerating…

By Andy Nallappan, Chief Technology Officer and Head of Software Business Operations, Broadcom Software

In December 2021, Broadcom Software published our blog: Predictions for 2022. We will now explore each of these predictions in more depth with this blog series. For this blog our topic is edge computing.

The future has a way of quickly becoming the present.

A couple of decades ago, when nearly all centralized computing ran in data centers, companies began talking about how to accelerate decision-making and reduce latency issues that frustrated users (commonly referred to as the “world wide wait”). This problem became more acute as the increasing use of mobile and IoT devices put new strain on existing internet infrastructure.

The technology world responded by shifting toward a new model in which the storage and processing of data would be conducted as close as possible to the end user. This decentralization of computer power, known as edge computing, progressed slowly; in 2018, just 10% of enterprise data was being processed outside of cloud-based data centers.

The speed of transition

But seemingly overnight, we’ve witnessed a surge in momentum – thanks in no small part to the massive spread of the Internet of Things and the need to close a widening gap between collecting data from equipment and using it to improve business.

Gartner now estimates the percentage of data that gets created and processed on the edge will reach 75% within the next 3 years as more enterprises understand the advantages of moving computing resources to the physical location of data creation. The speed of this transformation is extraordinary, and you can now find deployments essentially everywhere with edge computing expanding across industries and use cases. Indeed, worldwide spending on edge computing is expected to reach $176 billion in 2022, an increase of 14.8% over last year, according to IDC.

Edge, AI and the future

Edge computing solutions take many forms and a big reason for its popularity is that edge deployments help address use cases that the cloud can’t. It’s why edge is finding a role in the development of autonomous vehicles as well as in the remote monitoring of assets in the gas and oil industry. It’s also playing a key role in smart grids, where it can monitor energy use and analyze consumption as well as in predictive maintenance to proactively detect changes in production lines before problems crop up.

Think about the implications for manufacturing sites involved with what’s known as the Industrial Internet of Things (IIOT). Edge computing-driven insights will be able to help plants reduce waste and also improve overall product quality. And it will make for greater safety by finding issues, allowing maintenance teams to recognize changes in factory conditions and then respond in real time.

Consider the example offered by the luxury car maker, Audi, which requires its manufacturing facilities to be fully loaded with IoT devices with low-latency demands. Each plant can turn out 1,000 vehicles a day, with more than 5,000 welds per car. But that also means 5 million welds to inspect each day. The company adopted an inline inspection of each weld, where sensors on every welding-gun are able to analyze data surrounding each weld; that information all gets processed in real-time on the edge.

All this scratches the surface when you think about the future as enterprises combine edge computing with the power of Artificial Intelligence (AI) and machine learning (ML). This is going to happen when you consider the heavy investments enterprises are already making in AI. Indeed, a McKinsey survey last year reported that half of the respondents said they had implemented AI in at least one business function. The advent of Edge AI, as it’s being called, has a lot of people rightly excited about the prospects of an era annotated by even faster computing, improved data security, and more efficient operational controls.