Tutorial 4: Boosting Your IoT Device Compute for Smart Cities through Distributed Compute Technology and Secondary Application Workloads

Time: 13:00 – 14:30 (ET, New York), Monday, September 28

Through the introduction of “secondary workloads”, the distributed compute and container technologies enable the ability to unify compute for larger workloads, provide additional resilience to cyber attacks, and better utilize IoT devices. The distributed compute enables the ability to unify IoT resources in a Smart City with other intelligent devices, allowing larger compute workloads to execute across a broad compute fabric at the edge. This distributed compute capability provides resiliency against cyber-attacks for IoT devices in Smart Cities by decentralizing key compute functions across multiple nodes.

The workload placement technology enables “secondary workloads” to run on IoT devices taking advantage of underutilized compute cycles on the devices, or during idle times of the day/night – making the IoT device even more powerful. The increasing in on-device IoT processing capabilities enable real-time decision making in a Smart City and reduces the bandwidth requirement on devices.

This tutorial provides a live demonstration of the ability to distribute high compute workloads across a combination of IoT devices and other compute assets, all working on the same compute job. These applications will be deployed as “secondary workloads” on compute devices (IoT + desktop + server), scheduled to run certain hours, and utilizing a subset of a compute asset’s resources.


Dan Kepner

Dan Kepner

Kazuhm, San Diego, USA

Short bio:
Dan Kepner, Kazuhm’s Director of Solutions Engineering, has more than 17 years developing and integrating compute heavy solutions for various agencies and commercial customers. Dan is part of Kazuhm’s solutions team where he and other engineers provide technical solutions that help organizations maximize the utility of their existing computer resources. Dan holds undergraduate and master’s degrees in Systems Engineering from the University of Virginia.