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

#News(IoTStack) [ via IoTForIndiaGroup ]

Edge computing promises to deliver IoT reliably, speedily while taking more care of security, data privacy
According to IDC, in another 4 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

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?

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.

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