Deterministic performance: Why Red Hat Enterprise Linux is the engine for industrial edge

In industrial environments, the drive toward smart manufacturing means blending the worlds of operational technology (OT) and information technology (IT). To this point, these worlds have existed in silos—OT managing the physical, real-time control of machinery, and IT focusing on data, security, and enterprise efficiency.

But today, the industrial edge depends on this convergence. This unification demands a reliable platform that provides  low latency as well as predictable latency—what we call deterministic performance. Determinism means  that a high-priority task, like motion control or robotic coordination, always executes within a guaranteed timeframe, regardless of system load. Without it, unpredictable delays of even milliseconds can be a recipe for disaster.

Red Hat Enterprise Linux (RHEL) delivers this tested real-time, deterministic performance required for next-generation industrial applications and control workloads. And we can prove it. In the following sections, I’ll dive into the performance data from our bare-metal and real-world "screw-to-screw" round-trip tests to clearly show how RHEL consistently maintains the sub-millisecond determinism you need.

Authors note: For the purpose of this discussion, all performance data outlined for RHEL will also apply to Red Hat Device Edge , built from RHEL and providing the same core capabilities for resource-constrained devices—especially important in the industrial space

Determinism dilemma at the industrial edge 

Before we jump into performance data, let me set some context. When we talk about performance in manufacturing, we’re not just talking about speed. We’re talking about determinism.

• Latency is the time between an event (like a sensor reading) and the system’s response. In a typical Linux environment, basic performance tuning can reduce this time.
• Determinism is the measure of how predictable that latency response time is. It means that a task with a high priority always executes within a guaranteed timeframe, regardless of system load.

For OT systems running critical applications—such as motion control, robotic coordination, or quality inspection—an unpredictable delay of even a few milliseconds can lead to machine crashes, defective products, or safety hazards. This is why many industrial control systems rely on specialized real-time operating systems (RTOS) on highly tuned hardware.

However, these specialized RTOS systems are often proprietary, hard to integrate with cloud infrastructure, and can become a bottleneck for innovation and data-driven insights, the very things industry is moving towards. This leaves a gap at the edge.

Red Hat’s approach: Predictability on an enterprise foundation

RHEL, the world’s leading enterprise Linux platform, closes that gap as it includes real-time components including a rea-time kernel (or kernel-rt).

The standard Linux kernel is designed for throughput and fair scheduling, optimizing overall system utilization—great for the data center, but not ideal at the edge where task priority is critical. In contrast, the real-time kernel is built to maintain low latency and consistent response times. It uses real-time scheduling policies such as SCHED_FIFO (first in, first out) so high-priority tasks always receive preferential CPU access.

But here’s the most important part: It’s not a brand-new, unproven operating system. It’s a standard feature made available within the RHEL environment. This means IT gets the enterprise-grade foundation they need—with security, simplified management, and a consistent operating environment from the edge to the cloud—while OT gets the sub-millisecond determinism required for control.

RHEL allows you to:

• Run mission-critical applications that demand deterministic performance right alongside applications focused on data and analytics.
• Maintain security and compliance with a consistent, expertly supported and hardened platform across your entire operational footprint.
• Reduce operational complexity by standardizing on a single platform, eliminating the need to manage diverse, proprietary RTOS environments.

What we tested and why it matters

To evaluate how well the real-time kernel feature within RHEL supports industrial control workloads, we ran 2 sets of tests. The first measured the operating system’s raw real-time performance, and the second measured full “screw-to-screw” round-trip time across the entire control loop, including network travel, data conversions, and programmable logic controller (PLC) scan cycles. Control engineers care about both, but especially the second, because it reflects how systems behave in the real world.

Test 1: Operating system real-time performance

In bare-metal tests on standard 2.6 GHz x86 hardware, RHEL with the real-time kernel delivered highly deterministic behavior:

• Max latency under 15 microseconds (µs)
• Determinism in the low tens of microseconds, even under heavy “noisy neighbor” load
• Significant reduction in CPU cycles between event and response when real-time features were enabled

This deterministic performance is what enables modern industrial workloads—control, motion, machine vision, AI inference—to respond reliably to physical events at the edge. Real-time settings clearly tightened response behavior, with far less jitter than non-real-time configurations.

The following graph shows four independent tests. The first 2 (blue and green lines) show the number of CPU cycles the system takes to respond to events with a significantly loaded CPU with no real-time optimization configurations in place. The second two lines (red and yellow) show the number of CPU cycles the system takes to respond to events with loaded CPU with the real-time capabilities enabled.   Note there is a significant decrease of CPU cycles between the event and the response when the real-time settings are in place with a lot less determinism in that response.  

Test 2: Real-world “screw-to-screw” round-trip time

While test 1 highlights the true operating system performance, test 2 provides a more real world scenario incorporating the software stacks typically used for control. A screw-to-screw test simulates the full journey of a control event, including asynchronous delays from open platform communications unified architecture (OPC UA) conversions, network travel, PLC scan time, and application processing. Based on all these asynchronous loop components, the theoretical worst-case round-trip time was 6.7 milliseconds.

While the actual real time operating system has little impact on that theoretical number, this test highlights that when the system is stressed and heavily loaded, the operating system still delivers its deterministic behavior by never letting the control loops, network packet builds, and other elements in the operating systems realm of control, exceed the calculated max.

We ran this test across 2 architectures (bare metal PLC and containerized PLC) with and without noisy-neighbor load: