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Uperf

Uperf is a network performance tool

Running UPerf

Given that you followed instructions to deploy operator, you can modify cr.yaml

apiVersion: ripsaw.cloudbulldozer.io/v1alpha1
kind: Benchmark
metadata:
  name: uperf-benchmark
  namespace: benchmark-operator
spec:
  elasticsearch:
    url: "http://es-instance.com:9200"
  workload:
    name: uperf
    args:
      client_resources:
        requests:
          cpu: 500m
          memory: 500Mi
        limits:
          cpu: 500m
          memory: 500Mi
      server_resources:
        requests:
          cpu: 500m
          memory: 500Mi
        limits:
          cpu: 500m
          memory: 500Mi
      serviceip: false
      runtime_class: class_name
      hostnetwork: false
      networkpolicy: false
      pin: false
      kind: pod
      pin_server: "node-0"
      pin_client: "node-1"
      pair: 1
      multus:
        enabled: false
      samples: 1
      test_types:
        - stream
      protos:
        - tcp
      sizes:
        - 16384
      nthrs:
        - 1
      runtime: 30
      colocate: false
      density_range: [low, high]
      node_range: [low, high]
      step_size: addN, log2

client_resources and server_resources will create uperf client's and server's containers with the given k8s compute resources respectively k8s resources

serviceip will place the uperf server behind a K8s Service

runtime_class If this is set, the benchmark-operator will apply the runtime_class to the podSpec runtimeClassName.

Note: runtime_class has only been tested with Kata containers. Only include runtime_class if using Kata containers.

hostnetwork will test the performance of the node the pod will run on.

networkpolicy will create a simple networkpolicy for ingress

pin will allow the benchmark runner place nodes on specific nodes, using the hostname label.

pin_server what node to pin the server pod to.

pin_client what node to pin the client pod to.

pair how many instances of uperf client-server pairs. pair is applicable for pin: true only. If pair is not specified, the operator will use the value in density_range to detemine the number of pairs. See Scale section for more info. density_range can do more than pair can, but pair support is retained for backward compatibility.

multus[1] Configure our pods to use multus.

samples how many times to run the tests. For example

[1] https://github.com/intel/multus-cni/tree/master/examples

      samples: 3
      density_range: [1,1]
      test_types:
        - stream
      protos:
        - tcp
      sizes:
        - 1024
        - 16384
      nthrs:
        - 1
      runtime: 30

Will run stream w/ tcp and message size 1024 three times and stream w/ tcp and message size 16384 three times. This will help us gain confidence in our results.

Asymmetric Request-Response

For the request-response (rr) test_type, it is possible to provide the sizes values as a list of two values where the first value is the write size and the second value is the read size.

For example:

      samples: 3
      density_range: [1,1]
      test_types:
        - rr
      protos:
        - tcp
      sizes:
        - 1024
        - [8192, 4096]
      nthrs:
        - 1
      runtime: 30

Will run the rr test with tcp, first with a symmectic size of 1024 and then with an asymmetric size of 8192 write and 4096 read.

Multus

If the user desires to test with Multus, use the below Multus NetworkAtachmentDefinition as an example:

apiVersion: "k8s.cni.cncf.io/v1"
kind: NetworkAttachmentDefinition
metadata:
  name: macvlan-range-0
spec:
  config: '{
            "cniVersion": "0.3.1",
            "type": "macvlan",
            "master": "eno1",
            "mode": "bridge",
            "ipam": {
                    "type": "host-local",
                    "ranges": [
                    [ {
                       "subnet": "11.10.0.0/16",
                       "rangeStart": "11.10.1.20",
                       "rangeEnd": "11.10.3.50"
                    } ] ]
            }
        }'
---
apiVersion: "k8s.cni.cncf.io/v1"
kind: NetworkAttachmentDefinition
metadata:
  name: macvlan-range-1
spec:
  config: '{
            "cniVersion": "0.3.1",
            "type": "macvlan",
            "master": "eno1",
            "mode": "bridge",
            "ipam": {
                    "type": "host-local",
                    "ranges": [
                    [ {
                       "subnet": "11.10.0.0/16",
                       "rangeStart": "11.10.1.60",
                       "rangeEnd": "11.10.3.90"
                    } ] ]
            }
        }'

This will use the same IP subnet across nodes, but not overlap IP addresses.

To enable Multus in Ripsaw, here is the relevant config.

      ...
      multus:
        enabled: true
        client: "macvlan-range-0"
        server: "macvlan-range-1"
      pin: true
      pin_server: "openshift-master-0.dev4.kni.lab.eng.bos.redhat.com"
      pin_client: "openshift-master-1.dev4.kni.lab.eng.bos.redhat.com"
      ...

Scale

Scale in this context refers to the ability to enumerate UPERF client-server pairs during test in a control fashion using the following knobs.

colocate: true will place each client and server pod pair on the same node.

density_range to specify the range of client-server pairs that the test will iterate.

node_range to specify the range of nodes that the test will iterate.

step_size to specify the incrementing method.

Here is one scale example:

      ...
      pin: false
      colocate: false
      density_range: [1,10]
      node_range: [1,128]
      step_size: log2
      ...

Note, the scale mode is mutually exlusive to pin mode with the pin mode having higher precedence. In other words, if pin:true the test will deploy pods on pin_server and pin_client nodes and ignore colocate, node_range, and the number of pairs to deploy is specified by the density_range.high value.

In the above sample, the scale mode will be activated since pin: false. In the first phase, the pod instantion phase, the system gathers node inventory and may reduce the node_range.high value to match the number of worker node available in the cluster.

According to node_range: [1,128], and density_range:[1,10], the system will instantiate 10 pairs on each of 128 nodes. Each pair has a node_idx and a pod_idx that are used later to control which one and when they should run the UPERF workload, After all pairs are up and ready, next comes the test execution phase.

The scale mode iterates the test as a double nested loop as follows:

   for node with node_idx less-or-equal node_range(low, high. step_size):
      for pod with pod_idx less-or-equal density_range(low, high, step_size):
          run uperf

Hence, with the above params, the first iteration runs the pair with node_idx/pod_idx of {1,1}. After the first run has completed, the second interation runs 2 pairs of {1,1} and {1,2} and so on.

The valid step_size methods are: addN and log2. N can be any integer and log2 will double the value at each iteration i.e. 1,2,4,8,16 ... By choosing the appropriate values for density_range and node_range, the user can generate most if not all combinations of UPERF data points to exercise datapath performance from many angles.

Once done creating/editing the resource file, you can run it by:

# kubectl apply -f config/samples/uperf/cr.yaml # if edited the original one
# kubectl apply -f <path_to_file> # if created a new cr file

Running Uperf in VMs through kubevirt/cnv [Preview]

Note: this is currently in preview mode.

Pre-requisites

You must have configured your k8s cluster with Kubevirt preferably v0.23.0 (last tested version).

changes to cr file

server_vm:
  dedicatedcpuplacement: false # cluster would need have the CPUManager feature enabled
  sockets: 1
  cores: 2
  threads: 1
  image: kubevirt/fedora-cloud-container-disk-demo:latest # your image must've ethtool installed if enabling multiqueue
  limits:
    memory: 4Gi
  requests:
    memory: 4Gi
  network:
    front_end: bridge # or masquerade
    multiqueue:
      enabled: false # if set to true, highly recommend to set selinux to permissive on the nodes where the vms would be scheduled
      queues: 0 # must be given if enabled is set to true and ideally should be set to vcpus ideally so sockets*threads*cores, your image must've ethtool installed
  extra_options:
    - none
    #- hostpassthrough
client_vm:
  dedicatedcpuplacement: false # cluster would need have the CPUManager feature enabled
  sockets: 1
  cores: 2
  threads: 1
  image: kubevirt/fedora-cloud-container-disk-demo:latest # your image must've ethtool installed if enabling multiqueue
  limits:
    memory: 4Gi
  requests:
    memory: 4Gi
  network:
    front_end: bridge # or masquerade
    multiqueue:
      enabled: false # if set to true, highly recommend to set selinux to permissive on the nodes where the vms would be scheduled
      queues: 0 # must be given if enabled is set to true and ideally should be set to vcpus ideally so sockets*threads*cores, your image must've ethtool installed
  extra_options:
    - none
    #- hostpassthrough

The above is the additional changes required to run uperf in vms. Currently we only support images that can be used as containerDisk.

You can easily make your own container-disk-image as follows by downloading your qcow2 image of choice. You can then make changes to your qcow2 image as needed using virt-customize.

cat << END > Dockerfile
FROM scratch
ADD <yourqcow2image>.qcow2 /disk/
END

podman build -t <imageurl> .
podman push <imageurl>

You can either access results by indexing them directly or by accessing the console. The results are stored in /tmp/ directory

Dashboard example

Using the Elasticsearch storage describe above, we can build dashboards like the below.

UPerf Dashboard

To reuse the dashboard above, use the json here

Additionally, by default we will utilize the uperf-results index for Elasticsearch.