Almost thirty years ago, when the internet was launched onto an unsuspecting world, even inventor Tim Berners-Lee and colleagues at CERN could not have predicted the upheaval that would follow. It has been the greatest technology revolution since the industrial original. The combination of Cloud, IoT and AI is driving opportunity and threat in equal measure. Decisions made within organizations will have an impact for years to come. The way in which IoT-Edge links to the broader cloud backend, and the way in which AI integrates across the full processing chain, will be the key to unlocking material innovation and value. After many years of rationalization and stretched infrastructure investments, the IoT represents a tipping point for telcos, the cellular networks on whose backbones the new IoT offerings will be delivered.
Fasten your harnesses, because the era of cloud computing's giant data centers is about to be rear-ended by the age of self-driving cars. Here's the problem: When a self-driving car has to make snap decisions, it needs answers fast. Even slight delays in updating road and weather conditions could mean longer travel times or dangerous errors. But those smart vehicles of the near-future don't quite have the huge computing power to process the data necessary to avoid collisions, chat with nearby vehicles about optimizing traffic flow, and find the best routes that avoid gridlocked or washed-out roads. The logical source of that power lies in the massive server farms where hundreds of thousands of processors can churn out solutions.
The advancement of artificial intelligence (AI) has been a great enabler for the Internet of things (IoT). Given the ability to think for itself, it's shrugged off its original definition as a network of tiny sensors and grown to incorporate a host of more intelligent AIoT (AI IoT) devices, from smartphones all the way up to autonomous vehicles. AI has also paved the way for new IoT device categories. Previously passive devices such as surveillance cameras are being transformed into highly valuable IoT video sensors, as cloud-based AI algorithms turn raw footage into structured data that's ripe for inference. However, the nature of systems like video sensors means they're extremely data-heavy.
Just a few years ago, many expected all the Internet of Things (IoT) to move to the cloud--and much of the consumer-connected IoT indeed lives there--but one of the key basics of designing and building enterprise-scale IoT solutions is to make a balanced use of edge and cloud computing.1 Most IoT solutions now require a mix of cloud and edge computing. Compared to cloud-only solutions, blended solutions that incorporate edge can alleviate latency, increase scalability, and enhance access to information so that better, faster decisions can be made, and enterprises can become more agile as a result. That being said, complexity introduced by edge computing should justify the objectives at hand, which include scale, speed, and resiliency. A choice that goes too far in one direction typically introduces substantial operational complexities and expenses.
Video: 3 things you should know about cloud v. data center At present, edge computing is more of a prospect than a mature market -- more of a concept than a product. It is an effort to bring quality of service (QoS) back into the data center services discussion, as enterprises decide not just who will provide their services, but also where. "The edge" is a theoretical space where a data center resource may be accessed in the minimum amount of time. You might think the obvious place for the edge to be located, for any given organization, is within its own data center ("on-premises"). Or, if you've followed the history of personal computing from the beginning, you might think it should be on your desktop, or wherever you've parked your PC.