Shifting clouds:Artificial intelligence and IoT make edge computing new buzzword

Bengaluru:Elon Musk’s Tesla cars make timely and autonomous driving decisions. The reason: The vehicles are embedded with powerful on-board computers that allow for near real-time, low-latency data processing which is collected by the vehicle’s numerous sensors.

Intel estimates that autonomous cars will generate 40 terabytes of data for every eight hours of driving. This implies that it is unsafe and impractical to send such humongous amounts of data to the cloud.

But what if some of the computing can be done in the cars itself, making the vehicle a mini data centre? Would that make autonomous cars much more reliable and secure while keeping the consumer’s data private? Further, what if wearables and wireless medical devices need to process complex data in real time? Would cloud computing, with its bandwidth and related latency issues, suffice?

Also, regulatory and compliance issues may dictate that not all data can be sent to the cloud. Similarly, in the consumer segment, would online multiplayer games—where milliseconds can mean the difference between winning and losing—not work better if their latency issues are solved?

For decades, computing was done solely on servers in the backyard of companies. Over the last two decades, however, businesses gradually began shifting their workloads to the cloud. This trend, better known as cloud computing, has helped companies reduce capital expenditure and increase return on investment. As a result, private cloud (on-premises), public cloud (on a network—typically the internet) and hybrid cloud (a combination of both public and private) are terms that are well understood by companies today, even if not fully implemented.

However, even as companies are talking about a “multi-cloud” approach—one that envisages the use of multiple cloud vendors—the term edge computing (Cisco Inc.’s “fog computing” has a similar goal) is gaining ground with good reason.

At VMWorld 2018 in Las Vegas, for instance, VMware Inc.’s chief executive Pat Gelsinger, was insistent that as billions of devices get connected as part of the Internet of Things (IoT) trend, computing will increasingly be done at the so-called “edge”—at, or near, the source of the data. Technology vendors like VMware—a listed unit of Dell Technologies Inc.—believe this trend will prompt companies to process and analyse data using artificial intelligence (AI) and machine learning (ML) in a hybrid cloud operating model.

VMWare is making a good bet. According to market research firm International Data Corporation (IDC), in another four years, more than 30% of organizations’ cloud deployments in India alone will include edge computing to address bandwidth bottlenecks, reduce latency and process data for decision support in real time.

There are a number of reasons for this. For one, the number of phones in the global market will lead to an explosion of data, giving the ability to derive more personalized services from a B2B (business to business) and B2C (business to consumer) perspective, leading to an increased need for analytics tools and more ML models, etc.

A lot of computing is already shifting to the edge such as phones themselves and even microchips embedded in light bulbs (read light fidelity). And, “with 5G, you could do very interesting things. Edge computing has tremendous number of use cases in cities, shipping and logistics, etc., because of 5G,” Rick Harshman, managing director (Asia-Pacific) at Google Cloud, said in a recent interview.

However, while cloud computing has traditionally served as a reliable and cost-effective way to manage these data streams, the total growth of data will put increasing strain on network bandwidth, which is where edge computing comes into play. This data will need to be processed, which is why Nvidia Corp.