The Big “Shift Left”: An Introduction to Edge Computing

[Tom_Bradicich]

Increasing connectivity is revolutionizing the way humans and machines interact on a daily basis. Today, devices can be used to monitor home security systems, navigational programs can turn on personal computers, and assembly lines can communicate with machines in a variety of locations. All this activity out at the “edge” is driving widespread adoption of network-enabled devices and things, the interconnection of the physical world with computer-based platforms, and the Internet of Things (IoT). IT departments across all industries are looking to deploy computing technologies expressly designed for the IoT. And, this computing and analytics is required at the edge, right were large volumes of data are captured. Just as the cloud was the first off-premise, I see the edge as the “other off-premise”.

Edge computing is at the forefront of one area of HPC innovation, shifting deep processing power and high-performance data analytics towards the “left” where IoT devices generate, access, and share information at lightning speeds. According to a report by International Data Corporation (IDC), by 2019, 45% of IoT-created data will be stored, processed, analyzed, and acted upon close to or at the edge of the network.

Extracting real-time insight at the edge requires high-end compute technologies that are able to streamline access to relevant data, lower the costs and risks associated with transporting data across the network, and ultimately accelerate more profitable business decisions.

 Source: Gartner, IoT Value Add, 2015

Currently, data management and analysis is performed in the cloud or data centers. However, in an edge computing model, sensors and connected devices transmit data to a nearby edge computing device instead of transporting it back to the cloud or a remote data center. 

I like to say, “Compute at the edge, accelerate insight”. Think of it this way – what processing of edge and IoT data is currently done in the data center? And would it be better if it were processed at the edge, for these key reasons:

• Edge computing will mitigate latency issues by promoting high-velocity data management at the edge, enabling organizations to harness the full power of IoT data.
• Transporting data from IoT devices to the cloud or data center requires a tremendous amount of bandwidth. Data analysis at the edge eliminates the need for bandwidth to send information back and forth.
• The big “shift left” limits the movement of information across a network, preventing hackers from breaching vulnerable endpoints.
• Not having to move large amounts of data across geographic areas affords savings in networking and bandwidth costs.
• Data corruption. Data reliability is a major concern for all industries, as data that is moved along extended networks is more likely to become compromised or corrupted.
• Edge processing provides an alternative to the limitations of the cloud.
  
  

Hewlett Packard Enterprise (HPE) is making IoT data more accessible, actionable, and secure than ever before with their HPE Edgeline Converged Edge Systems. The Edgeline EL1000 and EL4000 systems are designed to thrive in data-rich environments and optimized to rapidly capture and extract insights at the edge. By integrating data capture, control, compute, and scalable storage in one box, HPE is helping customers capitalize on real-time decision-making, and making organizations more competitive, efficient, and profitable.

IT departments across all industries are investing in high-end computing solutions to keep pace with increasing interconnectivity. Systems that are able to quickly capture, process, analyze, and act on IoT and edge data are allowing customers to harness the growing business potential of the IoT. Organizations that adopt edge computing capabilities can enjoy some first-mover advantages in operational efficiencies and new business development.

I’ll be discussing this in more detail at the upcoming GE Minds+Machines Conference in San Francisco. I invite you to contact us to learn how Converged Edge Systems can benefit you. I also invite you to follow me on Twitter at @TomBradicichPhD.