IoT at the Edge
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To the Cloud or Not: What’s Best for Your Application?


The proliferation of intelligence into things and devices is rapidly transforming the way enterprises collect and derive value from data. Complementing this is ubiquitous connectivity, which is producing more network-enabled things than ever before, connecting machines, systems, people, and the environment with control systems and IT on a platform known as the Internet of Things (IoT). These IoT devices and “things” generate petabytes of information every day and challenge enterprises to rethink their methods of managing, storing, and extracting insight from the Big Data of the IoT or Big Analog Data™*.

Let’s look at the increasing number of connected devices. According to Gartner, 6.4 billion IoT devices will be in use in 2016, a 30% increase from the previous year. And with 5.5 million new things connecting each day, the IoT universe is expected to reach 20.8 billion devices by 2020.

Internet of Things Units Installed Base by Category (Millions of Units).png

 Source: Gartner, November 2015

Massive volumes of data produced by the IoT contain real-time insight. I like to portion this insight into three categories: business insight, technical insight, and scientific insight. Each can help organizations adopt new business models, streamline operational processes, and create more innovative products and services. Enterprises that learn to quickly capture, process, and analyze that data can be first-movers, with the opportunity to make more informed, data-driven decisions and increase revenue. However, those operating on traditional systems lack the compute and storage capabilities necessary to support analytics for both deep and immediate insights. 

Enterprises are looking to high-performance computing (HPC) technologies placed out at the edge near the things to accelerate insight and real-time control. Many enterprises are now integrating IoT with the cloud to capitalize on the superior flexibility and scalability of a web interface. Common usage models include IoT data that is automatically synced with centralized data from other edges for analysis, housed for future processing, or archived.

But the cloud isn’t right for every application.

First, transporting data across the network to the cloud can open the door to connectivity issues, network latency, and security risks which have the potential to compromise the speed and quality of compute. Data that is moved around a lot also has a greater likelihood of being lost or corrupted. Although the cloud has the ability to derive the deepest possible insight, cloud processing may mean using a tremendous amount of bandwidth, latency, and greater vulnerability.

So, what is the alternative to the cloud?

Edge computing. In an edge system, compute power is pushed to the edge of a network to accelerate time-to-insight and improve the response times of IoT systems. The demand for immediate insight will cause more sophisticated analytics to take place at the edge, closer to the source of the data. This shift also mitigates the risky and time-consuming process of moving information back and forth across the network, thereby increasing data security and reliability.

To capitalize on these advancements, Hewlett Packard Enterprise (HPE) created a new product category called Converged Edge Systems. And, the Edgeline EL1000 and EL4000 are the industry’s first entrants into this new category. These systems help avoid cloud lock-in and enable accelerated analytics because they are built up with Intel 64 Xeon processor cores. In addition, the HPE Edgeline Converged Edge Systems are built with HPE iLO (integrated light out) technology, to allow enterprises to manage the system remotely. These systems are built on open industry standards, integrate data capture and control functions, and are environmentally hardened. This provides a choice: compute at the edge, the cloud, or both.

With the opportunity to convert IoT insights into immediate business value greater than ever, it is crucial to select a technology partner who can fuel your IoT transformation and give you choices. Please contact HPE to see firsthand how computing at the edge can accelerate data insights for your enterprise.  I also invite you to follow me on Twitter at @TomBradicichPhD.

*Big Analog Data is a trademark of National Instruments

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About the Author


Dr. Tom Bradicich is Global Head of Edge and IoT CoE & Labs,s at Hewlett Packard Enterprise. He and his HPE Labs team develop and commercialize advanced connectivity, compute, and controls software and technologies. Tom directs the HPE Edge and IoT Center of Excellence, which lead company-wide strategies, venture and M&A business and technical assessments, and the Channel-to-Edge Institute channel partner program.

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