Benchmarking Red Hat OpenShift Virtualization for latency-sensitive workloads

In a previous introductory article, The software defined programmable logic controller (PLC), our colleague Daniel Fröhlich introduces the concept of "predictable low latency" and how it compares to strict real time.

This article explains how we extended the benchmarking of the Red Hat OpenShift Performance Addon Operator, and underlying Red Hat Enterprise Linux (RHEL), to OpenShift Virtualization to assess whether OpenShift can accommodate latency-sensitive VM workloads.

Investigating network latency

Our focus is on latency and jitter, but our tests also include the network's impact on latency. After all, the workload types we are investigating not only talk to other applications but may also control physical machinery, which makes a "predictable low latency"—with an emphasis on predictable—paramount.

Network latencies are as critical for manufacturing as the jitter introduced by an operating system. We run our test workloads in virtual machines (VMs) on OpenShift, leveraging OpenShift Virtualization, to show that this benchmarking is not limited to cloud-native applications running in containers and that it can also accommodate lift-and-shift use cases.

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For the sake of cross-validation and to ensure consistency of results with the benchmarking demonstrated in the software-defined PLC article (linked at the top of this article), we started with a jitter test of the system. We deployed two jitterdebugger instances in VMs on a real-time capable worker node within an OpenShift cluster (shown in the figure above). A real-time capable worker node in OpenShift runs RHEL with an optimized kernel designed to maintain low latency, consistent response time, and determinism. We applied performance tuning using the OpenShift Performance Addon Operator.

We deployed an additional stress workload on our OpenShift real-time worker node as noise to make the results more relevant for real-world scenarios. The jitterdebugger measurement was executed for three days to prove that we could achieve and maintain this low latency.

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Both jitterdebugger instances demonstrated similar values resulting in maximal latencies of 54 and 55µs, which is consistent with the previous observations when running those workloads directly in containers. That consistency allows us proceed with the goal of this demo: investigating network latencies.

We modified the test setup to assess the networking capabilities. Measuring instances requires access to two independent networking interfaces (NICs). To achieve the maximal performance, both NICs were mounted to guest VMs using PCI passthrough (shown in the figure below).

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For the measurement, we used the l2reflect test. The l2reflect test is a DPDK application that implements a ping-pong benchmark to estimate latency on the L2 level between two networking devices. The benchmark leader sends a packet to the second instance, and a timestamp is taken. The second instance returns the packet, and a second timestamp is taken. The difference between the first and second timestamps is recorded as the latency.

We executed the l2reflect test for five days to replicate a realistic scenario of a 24/7 deployment, resulting in a maximum latency of 65µs. Compared to 55µs latency measured with the jitterdebugger test, communication over the network caused only 10µs overhead, which is still an acceptable level.

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Wrap up

This benchmarking demonstrates that OpenShift can accommodate latency-sensitive workloads using OpenShift Virtualization in conjunction with the OpenShift Performance Addon Operator. Organizations can expect their latency-sensitive applications to be free from interruptions and suitable for various manufacturing use cases. At the same time, they gain access to all advantages of a modern cloud-native deployment, scaling, and management system such as OpenShift.