Red Hat, Inc. announced Red Hat Device Edge 4.17, with updates intended to help modernize how businesses handle time-critical workloads in their most remote and distributed locations. These new low latency and near-real-time capabilities are designed to better meet the growing demand for faster, more reliable response times. Red Hat Device Edge combines an enterprise-ready and supported distribution of the Red Hat-led open source community project MicroShift (a lightweight Kubernetes distribution derived from the edge capabilities of Red Hat OpenShift) along with Red Hat Enterprise Linux and Red Hat Ansible Automation Platform.
Red Hat Device Edge provides a more consistent platform for resource-constrained edge environments, where small form factor devices and compute resources require lower-latency operations to more effectively collect, analyze and respond to actions and data in near real-time. From industrial environments and autonomous vehicles to online gaming and smart cities, these environments are a driving force in requiring applications that are more timely, more responsive and more consistent. To support use cases demanding predictability and low latency, organizations can now implement solutions with latency requirements well below one millisecond, while also providing reliable, deterministic performance to ensure consistent outcomes.
With support for these increasingly critical workloads, Red Hat Device Edge 4. 17 provides a path to bringing an entirely new class of edge use cases into reality, even if they require near-real-time response times and data processing. Speed and agility remain constant demands for IT leaders, with artificial intelligence and machine learning (AI/ML) at the edge providing a powerful combination of capabilities to enable faster, more nimble operations. By extending AI to the edge, businesses can collect data, process images, train models, run inference and more, but need to meet the expectations of real-time responses, offline functionality, and enhanced security.
Factory floors, for example, can experience success or failure in a given operation with a delay of just 20 minutes. Low and predictable latency capabilities can help enable AI-driven controls and functions to streamline processes.