An IBM and FogHorn Partnership That Will Make Edge-Focused Hybrid Clouds

A turnkey edge, AI and hybrid cloud platform could streamline the path to smart industries and infrastructure.

Traditional smart industrial and infrastructure systems are amazing—they can implement artificial intelligence (AI) and machine learning (ML) to prevent errors, predict maintenance, optimize systems and generally improve production. The challenges are that:

  • These systems are hard to set up, costing the end user a lot of time, money and peoplepower to get it working as advertised—let alone getting everyone trained.
  • Much of the data analysis is performed on the cloud, which can create various latency, connection, bandwidth, security and aliasing issues.
Calculating smart industrial/infrastructure data on the cloud introduces bandwidth, latency and connectivity challenges. (Image courtesy of FogHorn.)

Calculating smart industrial/infrastructure data on the cloud introduces bandwidth, latency and connectivity challenges. (Image courtesy of FogHorn.)

Chris Penrose, COO at FogHorn, a developer of edge computing AI software for the Industrial Internet of Things (IIOT), said, “With computing done in so many places—on public and private clouds and the edge—the challenge that businesses face is to connect all these different elements into a cohesive, end-to-end platform.”

In response to these challenges, FogHorn’s technology processes information locally, at or near the source of streaming sensor data, and offers an easy-to-build repeatable solution. This shift in complexity and computational location delivers low latency analytics, ML and AI functionality in real time.

That is why IBM has partnered with FogHorn to produce an open and secure hybrid cloud platform that focuses on performing AI and closed-loop system controls on edge devices—where it’s needed.

Evaristus Mainsah, general manager of IBM Hybrid Cloud and Edge Ecosystem, said, “By bringing together FogHorn Lightning Edge AI offerings with IBM Edge Application Manager, which runs on Red Hat OpenShift, we can help customers automate the deployment of edge AI applications to diverse and dynamic edge computing environments, securely, and at massive scale. Together, we provide customers with the flexibility to extend their operations across any public or private cloud to the edge, enabling them to act on insights closer to where the data is collected and where actions are taken.”

How the FogHorn and IBM Hybrid Cloud Systems Will Work

The hybrid cloud solution being developed by FogHorn and IBM will be designed to run on various edge endpoints, including:

  • Devices
  • Clusters
  • Servers
  • Gateways
  • Machines

To operate on the system, industrial and infrastructure devices need to support Red Hat Enterprise Linux (RHEL), Linux operating systems, Red Hat OpenShift, Podman and various docker runtimes. This enables customers to extend current operations, from various public or private clouds, to any asset on the edge while maintaining a single system of record that keeps track of data, insights and automated actions.

An overview of how FogHorn technology, IBM Edge Manager Agent and edge devices will work together. (Image courtesy of FogHorn.)

An overview of how FogHorn technology, IBM Edge Manager Agent and edge devices will work together. (Image courtesy of FogHorn.)

Penrose said, “FogHorn will leverage IBM Edge Application Manager to deliver edge-to-cloud FogHorn solutions for our customers that can help them make more informed decisions with their data, in real time. Combining FogHorn’s vertical expertise with IBM’s cloud know-how, we will create the opportunity to address a wide range of edge use cases, which has the potential to deliver operational savings, improved up-time, reduced waste and lower energy consumption.”

Essentially, IBM Edge Application Manager will oversee a series of edge devices and decide how software is passed onto them based on policies created by the end user. The cloud then acts as the manager: it monitors the devices, makes sure everything is up to date, and ensures that everything is running. It gives edge devices the broad strokes, while all the data crunching and immediate decision making is left to the edge. Then, by integrating with IBM Maximo Application Suite, the system can optimize asset performance, maintenance, monitoring and reliability options.

“What you typically do on the cloud we do right next to the device,” Penrose said. “We get all the data in real time instead of sending portions of data to the cloud. This makes more accurate models with ultra-low latency instead of moving to the cloud and back. Not only do we identify a situation and predict a failure. We can close the loop and stop that machine. So, say a video camera sees something down the line that doesn’t look right. We can shut down that line in real time.”

Mainsah added, “Combining cloud capabilities and edge computing helps improve the performance of business-critical applications by having data analysis performed at the source of where it’s generated. The result is timelier insights and actions that also reduce the costs associated with data backhaul to the cloud or centralized locations for processing. It also provides a seamless extension of cloud applications independent of where they reside and across multi-cloud environments.”

Security and Operational Details About the FogHorn and IBM Hybrid Cloud

FogHorn’s edge AI platform is packaged with an analytics and ML engine that offers a set of no-code and low-code tools to observe and control the system. It then interfaces with current industrial and infrastructure systems to collect, store and dashboard data. 

 FogHorn’s tools offer no-code and low-code edge AI solutions. (Stock image.)

FogHorn’s tools offer no-code and low-code edge AI solutions. (Stock image.)

Sastry Malladi, CTO of FogHorn, said, “A simple software development kit (SDK) is used to develop an application, produce closed-loop actions, interface with a gambit of tools, and easily discover sensors using drag-and-drop, ML and editing tools.”

Once the connection is set up, the security protocols will come from the user and data level. The system will integrate with current identity management solutions to ensure that only the right people have access.

Malladi added, “We want to work with the security systems users have, and work with all standard protocols. Once we do that, users can define roles-based access and control to ensure people have the access they need.”

Data that moves through the hybrid cloud system will always be encrypted while in motion. And encryption tools can be added to edge devices. For added security, the edge-to-cloud communication can only be opened from one direction. An edge device can open communication with the cloud, but the cloud can only respond; it cannot open communication.

The Benefits of This Partnership for End Users

FogHorn is now a member of the IBM edge ecosystem. As a result, users will have access to open standards-based cloud-native solutions that can manage the edge at any scale. This means that in time, FogHorn’s technology will work in parallel and integrate with that of various other IBM edge ecosystem partners.

Mainsah explained, “Adopting a hybrid multi-cloud model that extends from corporate data centers to public or private clouds to edge endpoints is critical to unlocking new connected experiences for customers. IBM is working with edge ecosystem partners like FogHorn to help enterprises extend their cloud computing to the edge without the worry of vendor lock-in. This open approach can help customers integrate AI and analytics at the edge to take actions faster, run enterprise apps at the edge to reduce the impact of intermittent connectivity and minimize data transport to central hubs for cost-efficiency.”

Another added benefit to this partnership is the IBM support that FogHorn and its end users will gain access to. “IBM supports partners by helping test, integrate and bring solutions to market,” said Mainsah. “Through our collaboration with FogHorn, we help them leverage IBM Edge Application Manager and integrate with IBM Maximo Application Suite to help customers deploy, process and analyze data from edge to cloud.”

Written by

Shawn Wasserman

For over 10 years, Shawn Wasserman has informed, inspired and engaged the engineering community through online content. As a senior writer at WTWH media, he produces branded content to help engineers streamline their operations via new tools, technologies and software. While a senior editor at Engineering.com, Shawn wrote stories about CAE, simulation, PLM, CAD, IoT, AI and more. During his time as the blog manager at Ansys, Shawn produced content featuring stories, tips, tricks and interesting use cases for CAE technologies. Shawn holds a master’s degree in Bioengineering from the University of Guelph and an undergraduate degree in Chemical Engineering from the University of Waterloo.