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Kube-burner based RAN workload for DU profile performance tests

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Kube-burner based template for a RAN DU workload

Templates for a DU workload deployed with kube-burner

Pod Number of Pods Specs Stress
Guaranteed 1 pod, 2 containers - 32 CPU, 1 GiB Memory, 16 GiB HP
- 2 configmaps and 4 secrets
1 svc
32 threads of 100% CPU stress, 512M virtual memory stress
BestEffort - web_server 4 pods, 2 containers each - 100 mc CPU, 128 Mib Memory
- 2 configmaps and 4 secrets
Exposes 8080 port for probes
BestEffort - curl_app 4 pods, 2 containers each - 100 mc CPU, 128 Mib Memory
- 2 configmaps and 4 secrets
- Liveness Probes (every 10 secs)
Kubelet stress with probes, ~250 KB per sec n/w traffic on Primary CNI
BestEffort - kubectl_pods 6 pods, 2 containers each - 100 mc CPU , 128 Mib Memory
- 2 configmaps and 4 secrets
- 2 kubectl gets (every 5 sec)
Kube-api-server stress with kubectl get, ~10% increase due to workload
  • Total pods - 15 pods / 30 containers
  • 2 config maps and 4 secrets in each pod
  • Exec Probes less than 10 in total and frequency >10 secs
  • No Exec probes on Gu pod
  • Traffic on primary CNI due to workload expected around 350 KB per sec
  • kube api-server incremental load due to workload expected to increase by 5-8%
Intended for use in internal system testing pipelines but the templates can be run on any cluster with kube-burner

Steps to run workload with cpu_utilization tests:

  • Run the MIRROR_SPOKE_OPERATOR_IMAGES stage in ocp-far-edge-vran-deployment pipeline to mirror necessary test images
  • Run the cpu_util test using ocp-far-edge-vran-tests pipeline

Steps to run locally

  • export REGISTRY=quay.io/rh_ee_apalanis
  • clone and deploy DU workload with kube-burner init --config du-intensive.yaml

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