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PerfKit Benchmarker

PerfKit Benchmarker is an open effort to define a canonical set of benchmarks to measure and compare cloud offerings. It's designed to operate via vendor provided command line tools. The benchmarks are not tuned (ie the defaults) because this is what most users will use. This should help us drive to great defaults. Only in the rare cause where there is a common practice like setting the buffer pool size of a database do we change any settings.

This README is designed to give you the information you need to get running with the benchmarker and the basics of working with the code. The [wiki] (https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/wiki) contains more detailed information:

Known Issues

Licensing

PerfKit Benchmarker provides wrappers and workload definitions around popular benchmark tools. We made it very simple to use and automate everything we can. It instantiates VMs on the Cloud provider of your choice, automatically installs benchmarks, and run the workloads without user interaction.

Due to the level of automation you will not see prompts for software installed as part of a benchmark run. Therefore you must accept the license of each benchmarks individually, and take responsibility for using them before you use the PerfKit Benchmarker.

In its current release these are the benchmarks that are executed:

Some of the benchmarks invoked require Java. You must also agree with the following license:

CoreMark setup cannot be automated. EEMBC requires users to agree with their terms and conditions, and PerfKit Benchmarker users must manually download the CoreMark tarball from their website and save it under the perfkitbenchmarker/data folder (e.g. ~/PerfKitBenchmarker/perfkitbenchmarker/data/coremark_v1.0.tgz)

SpecCPU2006 benchmark setup cannot be automated. SPEC requires users to purchase a license and agree with their terms and conditions. PerfKit Benchmarker users must manually download SpecCPU2006 tarball from their website and save it under the perfkitbenchmarker/data folder (e.g. ~/PerfKitBenchmarker/perfkitbenchmarker/data/cpu2006v1.2.tgz)

Installing PerfKit Benchmarker and Prerequisites

Before you can run the PerfKit Benchmarker, you need account(s) on the cloud provider(s) you want to benchmark:

You also need the software dependencies, which are mostly command line tools and credentials to access your accounts without a password. The following steps should help you get the CLI tool auth in place.

If you are running on Windows, you will need to install GitHub Windows since it includes tools like openssl and an ssh client. Alternatively, you can install Cygwin since it should include the same tools.

Install Python 2.7 and pip

If you are running on Windows, get the latest version of Python 2.7 here. This should have pip bundled with it. Make sure your PATH environment variable is set so that you can use both python and pip on the command line (you can have the installer do it for you if you select the correct option).

Most Linux distributions and recent Mac OS X version already have Python 2.7 installed. If Python is not installed, you can likely install it using your distribution's package manager, or see the Python Download page.

If you need to install pip, see these instructions.

(Windows Only) Install GitHub Windows

Instructions: https://windows.github.com/

Make sure that openssl/ssh/scp/ssh-keygen are on your path (you will need to update the PATH environment variable). The path to these commands should be

C:\\Users\\\<user\>\\AppData\\Local\\GitHub\\PortableGit\_\<guid\>\\bin

Install PerfKit

Download PerfKit Benchmarker from GitHub.

Install PerfKit Benchmarker dependencies

$ cd /path/to/PerfKitBenchmarker
$ sudo pip install -r requirements.txt

Cloud account setup

This section describes the setup steps needed for each cloud system.

After configuring the clouds you intend to use, skip to Running a Single Benchmark, unless you are going to use an object storage benchmark, in which case you need to configure a boto file.

Install gcloud and setup authentication

Instructions: https://developers.google.com/cloud/sdk/. If you're using OS X or Linux, you can run the command below:

$ curl https://sdk.cloud.google.com | bash

When prompted, pick the local folder, then Python project, then the defaults for all the rest.

Restart your shell window (or logout/ssh again if running on a VM)

On Windows, visit the same page and follow the Windows installation instructions on the page.

Next, create a project by visiting Google Cloud Console. After that, run:

$ gcloud init

which helps you authenticate, set your project, and set some defaults.

Alternatively, if that is already set up, and you just need to authenticate, you can use:

$ gcloud auth login

For help, see gcloud docs.

Install OpenStack Nova client and setup authentication

Make sure you have installed pip (see the section above).

Install python-novaclient via the following command:

$ sudo pip install -r requirements-openstack.txt

You must specify authentication information for test execution, including user name (``--openstack_usernameflag orOS_USERNAME` environment variable), tenant name (`--openstack_tenant` flag or `OS_TENANT_NAME` environment variable), and authentication URL (`--openstack_auth_url` flag or `OS_AUTH_URL` environment variable).

The password cannot be set through a flag. You can specify it through the OS_PASSWORD environment variable, or alternatively you can save it in a file and specify the file location with the --openstack_password_file flag or OPENSTACK_PASSWORD_FILE environment variable.

Example using environment variables:

export OS_USERNAME=admin
export OS_TENANT=myproject
export OS_AUTH_URL=http://localhost:5000
export OS_PASSWORD=<password>

Example using a password file at the default file location:

$ echo topsecretpassword > ~/.config/openstack-password.txt
$ ./pkb.py --cloud=OpenStack --benchmarks=ping

Kubernetes configuration and credentials

Perfkit uses kubectl binary in order to communicate with Kubernetes cluster - you need to pass the path to kubectl binary using --kubectl flag. It's recommended to use version 1.0.1. Authentication to Kubernetes cluster is done via a kubeconfig file. Its path is passed using the --kubeconfig flag.

Image prerequisites
Please refer to the Image prerequisites for Docker based clouds.

Kubernetes cluster configuration
If your Kubernetes cluster is running on CoreOS:

  1. Fix $PATH environment variable so that the appropriate binaries can be found:

    $ sudo mkdir /etc/systemd/system/kubelet.service.d
$ sudo vim /etc/systemd/system/kubelet.service.d/10-env.conf

    Add the following line to [Service] section:

    Environment=PATH=/opt/bin:/usr/bin:/usr/sbin:$PATH

  2. Reboot the node:

    $ sudo reboot

Note that some benchmark require to run within a privileged container. By default Kubernetes doesn't allow to schedule Dockers in privileged mode - you have to add --allow-privileged=true flag to kube-apiserver and each kubelet startup commands.

Ceph integration
When you run benchmarks with standard scratch disk type (--scratch_disk_type=standard - which is a default option), Ceph storage will be used. There are some configuration steps you need to follow before you will be able to spawn Kubernetes PODs with Ceph volume. On each of Kubernetes-Nodes and on the machine which is running Perfkit benchmarks do the following:

  1. Copy /etc/ceph directory from Ceph-host.

  2. Install ceph-common package so that rbd command is available:

  • If your Kubernetes cluster is running on CoreOS, then you need to create a bash script called rbd which will run rbd command inside a Docker container:

    #!/usr/bin/bash
/usr/bin/docker run -v /etc/ceph:/etc/ceph -v /dev:/dev -v /sys:/sys  --net=host --privileged=true --rm=true ceph/rbd $@

    Save the file as rbd and run:

    $ chmod +x rbd
$ sudo mkdir /opt/bin
$ sudo cp rbd /opt/bin

    Install rbdmap:

    $ git clone https://github.com/ceph/ceph-docker.git
$ cd ceph-docker/examples/coreos/rbdmap/
$ sudo mkdir /opt/sbin
$ sudo cp rbdmap /opt/sbin
$ sudo cp ceph-rbdnamer /opt/bin
$ sudo cp 50-rbd.rules /etc/udev/rules.d
$ sudo reboot

You have two Ceph authentication options available:

  1. Keyring - pass the path to the keyring file using --ceph_keyring flag

  2. Secret. In this case you have to create a secret first:

    Retrieve base64-encoded Ceph admin key:

    $ ceph auth get-key client.admin | base64
QVFEYnpPWlZWWnJLQVJBQXdtNDZrUDlJUFo3OXdSenBVTUdYNHc9PQ==

    Create a file called create_ceph_admin.yml and replace the key value with the output from the previous command:

    apiVersion: v1
kind: Secret
metadata:
  name: my-ceph-secret
data:
  key: QVFEYnpPWlZWWnJLQVJBQXdtNDZrUDlJUFo3OXdSenBVTUdYNHc9PQ==

    Add secret to Kubernetes:

    $ kubectl create -f create_ceph_admin.yml

    You will have to pass the Secret name (using --ceph_secret flag) when running the benchmakrs. In this case it should be: --ceph_secret=my-ceph-secret.

Mesos configuration

Mesos provider communicates with Marathon framework in order to manage Docker instances. Thus it is required to setup Marathon framework along with the Mesos cluster. In order to connect to Mesos you need to provide IP address and port to Marathon framework using --marathon_address flag.

Provider has been tested with Mesos v0.24.1 and Marathon v0.11.1.

Overlay network
Mesos on its own doesn't provide any solution for overlay networking. You need to configure your cluster so that the instances will live in the same network. For this purpose you may use Flannel, Calico, Weave, etc.

Mesos cluster configuration
Make sure your Mesos-slave nodes are reachable (by hostname) from the machine which is used to run the benchmarks. In case they are not, edit the /etc/hosts file appropriately.

Image prerequisites
Please refer to the Image prerequisites for Docker based clouds.

Cloudstack: Install dependencies and set the API keys

$ sudo pip install -r requirements-cloudstack.txt

Get the API key and SECRET from Cloudstack. Set the following environement variables.

export CS_API_URL=<insert API endpoint>
export CS_API_KEY=<insert API key>
export CS_API_SECRET=<insert API secret>

Specify the network offering when running the benchmark. If using VPC (--cs_use_vpc), also specify the VPC offering (--cs_vpc_offering).

$ ./pkb.py --cloud=CloudStack --benchmarks=ping --cs_network_offering=DefaultNetworkOffering

Install AWS CLI and setup authentication

Make sure you have installed pip (see the section above).

Follow instructions at http://aws.amazon.com/cli/ or run the following command (omit the 'sudo' on Windows)

$ sudo pip install -r requirements-aws.txt

Navigate to the AWS console to create access credentials: https://console.aws.amazon.com/ec2/

  • On the console click on your name (top left)
  • Click on "Security Credentials"
  • Click on "Access Keys", the create New Access Key. Download the file, it contains the Access key and Secret keys to access services. Note the values and delete the file.

Configure the CLI using the keys from the previous step:

$ aws configure

Windows Azure CLI and credentials

You first need to install node.js and NPM. This version of Perfkit Benchmarker is compatible with azure version 0.9.9.

Go here, and follow the setup instructions.

Next, run the following (omit the sudo on Windows):

$ sudo npm install [email protected] -g
$ azure account download

Read the output of the previous command. It will contain a webpage URL. Open that in a browser. It will download a file (.publishsettings) file. Copy to the folder you're running PerfKit Benchmarker. In my case the file was called Free Trial-7-18-2014-credentials.publishsettings.

$ azure account import [path to .publishsettings file]

Test that azure is installed correctly:

$ azure vm list

Install AliCloud CLI and setup authentication

Make sure you have installed pip (see the section above).

Run the following command to install aliyuncli (omit the sudo on Windows)

  1. Install python development tools:

    In Debian or Ubuntu:

    $ sudo apt-get install -y python-dev

    In CentOS:

    $ sudo yum install python-devel

  2. Install aliyuncli tool and python SDK for ECS:

    $ sudo pip install -r requirements-alicloud.txt

    To check if AliCloud is installed:

    $ aliyuncli --help

    Check if aliyuncli ecs command is ready:

    $ aliyuncli ecs help

    If you see the "usage" message, you should follow step 3. Otherwise, jump to step 4.

  3. Dealing with an exception when it runs on some specific version of Ubuntu. Get the python lib path: /usr/lib/python2.7/dist-packages

    $ python
> from distutils.sysconfig import get_python_lib
> get_python_lib()
'/usr/lib/python2.7/dist-packages'

    Copy to the right directory (for Python 2.7.X):

    $ sudo cp -r /usr/local/lib/python2.7/dist-packages/aliyun* /usr/lib/python2.7/dist-packages/

    Check again:

    $ aliyuncli ecs help

  4. Navigate to the AliCloud console to create access credentials:

    • Login first
    • Click on "AccessKeys" (top right)
    • Click on "Create Access Key", copy and store the "Access Key ID" and "Access Key Secret" to a safe place.
    • Configure the CLI using the Access Key ID and Access Key Secret from the previous step
    $ aliyuncli configure

DigitalOcean configuration and credentials

PerfKit Benchmarker uses the curl tool to interact with DigitalOcean's REST API. This API uses oauth for authentication. Please set this up as follows:

Log in to your DigitalOcean account and create a Personal Access Token for use by PerfKit Benchmarker with read/write access in Settings / API.

Save a copy of the authentication token it shows, this is a 64-character hex string.

Create a curl configuration file containing the needed authorization header. The double quotes are required. Example:

$ cat > ~/.config/digitalocean-oauth.curl
header = "Authorization: Bearer 9876543210fedc...ba98765432"
^D

Confirm that the authentication works:

$ curl --config ~/.config/digitalocean-oauth.curl https://api.digitalocean.com/v2/sizes
{"sizes":[{"slug":"512mb","memory":512,"vcpus":1,...

PerfKit Benchmarker uses the file location ~/.config/digitalocean-oauth.curl by default, you can use the --digitalocean_curl_config flag to override the path.

Installing CLIs and credentials for Rackspace

In order to interact with the Rackspace Public Cloud, PerfKit Benchmarker makes use of the Nova, and the Neutron CLI clients with the Rackspace extensions.

To run PerfKit Benchmarker against Rackspace is very easy. First, install the CLI clients as follows:

$ pip install -r requirements-rackspace.txt

Once these are installed, all we need to do is to set 3 environment variables:

export OS_USERNAME=<your_rackspace_username>
export OS_PASSWORD=<your_rackspace_API_key>
export OS_TENANT_NAME=<your_rackspace_account_number>

For a Rackspace UK Public Cloud account, an extra environment variable has to be set, please remember that only the LON region is available under a Rackspace UK Public Cloud account.

export OS_AUTH_URL=https://lon.identity.api.rackspacecloud.com/v2.0/

export OS_USERNAME=<your_rackspace_uk_username>
export OS_PASSWORD=<your_rackspace_uk_API_key>
export OS_TENANT_NAME=<your_rackspace_uk_account_number>

Tip: Put these variables in a file, and simple source them to your shell with source <filename>

Note: Not all flavors are supported on every region. Always check first if the flavor is supported in the region.

Image prerequisites for Docker based clouds

Docker instances by default don't allow to SSH into them. Thus it is important to configure your Docker image so that it has SSH server installed. You can use your own image or build a new one based on a Dockerfile placed in tools/docker_images directory - in this case please refer to Docker images document.

Create and configure a .boto file for object storage benchmarks

In order to run object storage benchmark tests, you need to have a properly configured ~/.boto file. The directions require that you have installed google-cloud-sdk. The directions for doing that are in the gcloud installation section.

Here is how:

  • Create the ~/.boto file (If you already have ~/.boto, you can skip this step. Consider making a backup copy of your existing .boto file.)

To create a new ~/.boto file, issue the following command and follow the instructions given by this command:

$ gsutil config

As a result, a .boto file is created under your home directory.

Open the .boto file and edit the following fields:

  1. In the [Credentials] section:

    gs_oauth2_refresh_token: set it to be the same as the refresh_token field in your gcloud credential file (~/.config/gcloud/credentials), which was setup as part of the gcloud auth login step.

    aws_access_key_id, aws_secret_access_key: set these to be the AWS access keys you intend to use for these tests, or you can use the same keys as those in your existing AWS credentials file (~/.aws/credentials).

  2. In the [GSUtil] section:

    default_project_id: if it is not already set, set it to be the google cloud storage project ID you intend to use for this test. (If you used gsutil config to generate the .boto file, you should have been prompted to supply this information at this step).

  3. In the [OAuth2] section:

    client_id, client_secret: set these to be the same as those in your gcloud credentials file (~/.config/gcloud/credentials), which was setup as part of the gcloud auth login step.

Running a Single Benchmark

PerfKit Benchmarker can run benchmarks both on Cloud Providers (GCP, AWS, Azure, DigitalOcean) as well as any "machine" you can SSH into.

Example run on GCP

$ ./pkb.py --project=<GCP project ID> --benchmarks=iperf --machine_type=f1-micro

Example run on AWS

$ cd PerfKitBenchmarker
$ ./pkb.py --cloud=AWS --benchmarks=iperf --machine_type=t1.micro

Example run on Azure

$ ./pkb.py --cloud=Azure --machine_type=ExtraSmall --benchmarks=iperf

Example run on AliCloud

$ ./pkb.py --cloud=AliCloud --machine_type=ecs.s2.large --benchmarks=iperf

Example run on DigitalOcean

$ ./pkb.py --cloud=DigitalOcean --machine_type=16gb --benchmarks=iperf

Example run on OpenStack

$ ./pkb.py --cloud=OpenStack --benchmarks=iperf --os_auth_url=http://localhost:5000/v2.0/

Example run on Kubernetes

$ ./pkb.py --cloud=Kubernetes --benchmarks=iperf --kubectl=/path/to/kubectl --kubeconfig=/path/to/kubeconfig --image=image-with-ssh-server  --ceph_monitors=10.20.30.40:6789,10.20.30.41:6789 --kubernetes_nodes=10.20.30.42,10.20.30.43

Example run on Mesos

$ ./pkb.py --cloud=Mesos --benchmarks=iperf --marathon_address=localhost:8080 --image=image-with-ssh-server

Example run on CloudStack

./pkb.py --cloud=CloudStack --benchmarks=ping --cs_network_offering=DefaultNetworkOffering

Example run on Rackspace

$ ./pkb.py --cloud=Rackspace --machine_type=general1-2 --benchmarks=iperf

How to Run Windows Benchmarks

You must be running on a Windows machine in order to run Windows benchmarks. Install all dependencies as above and set TrustedHosts to accept all hosts so that you can open PowerShell sessions with the VMs (both machines having each other in their TrustedHosts list is neccessary, but not sufficient to issue remote commands; valid credentials are still required):

set-item wsman:\localhost\Client\TrustedHosts -value *

Now you can run Windows benchmarks by running with --os_type=windows. Windows has a different set of benchmarks than Linux does. They can be found under perfkitbenchmarker/windows_benchmarks/. The target VM OS is Windows Server 2012 R2.

How to Run All Standard Benchmarks

Run without the --benchmarks parameter and every benchmark in the standard set will run serially which can take a couple of hours (alternatively, run with --benchmarks="standard_set"). Additionally, if you don't specify --cloud=..., all benchmarks will run on the Google Cloud Platform.

How to Run All Benchmarks in a Named Set

Named sets are are grouping of one or more benchmarks in the benchmarking directory. This feature allows parallel innovation of what is important to measure in the Cloud, and is defined by the set owner. For example the GoogleSet is maintained by Google, whereas the StanfordSet is managed by Stanford. Once a quarter a meeting is held to review all the sets to determine what benchmarks should be promoted to the standard_set. The Standard Set is also reviewed to see if anything should be removed. To run all benchmarks in a named set, specify the set name in the benchmarks parameter (e.g., --benchmarks="standard_set"). Sets can be combined with individual benchmarks or other named sets.

Useful Global Flags

The following are some common flags used when configuring PerfKit Benchmaker.

Flag Notes
--help see all flags
--benchmarks A comma separated list of benchmarks or benchmark sets to run such as --benchmarks=iperf,ping . To see the full list, run ./pkb.py --help
--cloud Cloud where the benchmarks are run. See the table below for choices.
--machine_type Type of machine to provision if pre-provisioned machines are not used. Most cloud providers accept the names of pre-defined provider-specific machine types (for example, GCP supports --machine_type=n1-standard-8 for a GCE n1-standard-8 VM). Some cloud providers support YAML expressions that match the corresponding VM spec machine_type property in the YAML configs (for example, GCP supports --machine_type="{cpus: 1, memory: 4.5GiB}" for a GCE custom VM with 1 vCPU and 4.5GiB memory). Note that the value provided by this flag will affect all provisioned machines; users who wish to provision different machine types for different roles within a single benchmark run should use the YAML configs for finer control.
--zone This flag allows you to override the default zone. See the table below.

The default cloud is 'GCP', override with the --cloud flag. Each cloud has a default zone which you can override with the --zone flag, the flag supports the same values that the corresponding Cloud CLIs take:

Cloud name Default zone Notes
GCP us-central1-a
AWS us-east-1a
Azure East US
AliCloud West US
DigitalOcean sfo1 You must use a zone that supports the features 'metadata' (for cloud config) and 'private_networking'.
OpenStack nova
CloudStack QC-1
Rackspace IAD OnMetal machine-types are available only in IAD zone
Kubernetes k8s

Example:

./pkb.py --cloud=GCP --zone=us-central1-a --benchmarks=iperf,ping

Proxy configuration for VM guests.

If the VM guests do not have direct Internet access in the cloud environment, you can configure proxy settings through pkb.py flags.

To do that simple setup three flags (All urls are in notation ): The flag values use the same <protocol>://<server>:<port> syntax as the corresponding environment variables, for example --http_proxy=http://proxy.example.com:8080 .

Flag Notes
--http_proxy Needed for package manager on Guest OS and for some Perfkit packages
--https_proxy Needed for package manager or Ubuntu guest and for from github downloaded packages
--ftp_proxy Needed for some Perfkit packages

Configurations and Configuration Overrides

Each benchmark now has an independent configuration which is written in YAML. Users may override this default configuration by providing a configuration. This allows for much more complex setups than previously possible, including running benchmarks across clouds.

A benchmark configuration has a somewhat simple structure. It is essentially just a series of nested dictionaries. At the top level, it contains VM groups. VM groups are logical groups of homogenous machines. The VM groups hold both a vm_spec and a disk_spec which contain the parameters needed to create members of that group. Here is an example of an expanded configuration:

hbase_ycsb:
  vm_groups:
    loaders:
      vm_count: 4
      vm_spec:
        GCP:
          machine_type: n1-standard-1
          image: ubuntu-14-04
          zone: us-central1-c
        AWS:
          machine_type: m3.medium
          image: ami-######
          zone: us-east-1a
        # Other clouds here...
      # This specifies the cloud to use for the group. This allows for
      # benchmark configurations that span clouds.
      cloud: AWS
      # No disk_spec here since these are loaders.
    master:
      vm_count: 1
      cloud: GCP
      vm_spec:
        GCP:
          machine_type:
            cpus: 2
            memory: 10.0GiB
          image: ubuntu-14-04
          zone: us-central1-c
        # Other clouds here...
      disk_count: 1
      disk_spec:
        GCP:
          disk_size: 100
          disk_type: standard
          mount_point: /scratch
        # Other clouds here...
    workers:
      vm_count: 4
      cloud: GCP
      vm_spec:
        GCP:
          machine_type: n1-standard-4
          image: ubuntu-14-04
          zone: us-central1-c
        # Other clouds here...
      disk_count: 1
      disk_spec:
        GCP:
          disk_size: 500
          disk_type: remote_ssd
          mount_point: /scratch
        # Other clouds here...

For a complete list of keys for vm_specs and disk_specs see virtual_machine.BaseVmSpec and disk.BaseDiskSpec and their derived classes.

User configs are applied on top of the existing default config and can be specified in two ways. The first is by supplying a config file via the --benchmark_config_file flag. The second is by specifying a single setting to override via the --config_override flag.

A user config file only needs to specify the settings which it is intended to override. For example if the only thing you want to do is change the number of VMs for the cluster_boot benchmark, this config is sufficient:

cluster_boot:
  vm_groups:
    default:
      vm_count: 100

You can achieve the same effect by specifying the --config_override flag. The value of the flag should be a path within the YAML (with keys delimited by periods), an equals sign, and finally the new value:

--config_override=cluster_boot.vm_groups.default.vm_count=100

See the section below for how to use static (i.e. pre-provisioned) machines in your config.

Advanced: How To Run Benchmarks Without Cloud Provisioning (e.g., local workstation)

It is possible to run PerfKit Benchmarker without running the Cloud provioning steps. This is useful if you want to run on a local machine, or have a benchmark like iperf run from an external point to a Cloud VM.

In order to do this you need to make sure:

  • The static (ie not provisioned by PerfKit Benchmarker) machine is ssh'able
  • The user PerfKitBenchmarker will login as has 'sudo' access. (*** Note we hope to remove this restriction soon ***)

Next, you will want to create a YAML user config file describing how to connect to the machine as follows:

static_vms:
  - &vm1 # Using the & character creates an anchor that we can
         # reference later by using the same name and a * character.
    ip_address: 170.200.60.23
    user_name: voellm
    ssh_private_key: /home/voellm/perfkitkeys/my_key_file.pem
    zone: Siberia
    disk_specs:
      - mount_point: /data_dir
  • The ip_address is the address where you want benchmarks to run.
  • ssh_private_key is where to find the private ssh key.
  • zone can be anything you want. It is used when publishing results.
  • disk_specs is used by all benchmarks which use disk (i.e., fio, bonnie++, many others).

In the same file, configure any number of benchmarks (in this case just iperf), and reference the static VM as follows:

iperf:
  vm_groups:
    vm_1:
      static_vms:
        - *vm1

I called my file iperf.yaml and used it to run iperf from Siberia to a GCP VM in us-central1-f as follows:

$ ./pkb.py --benchmarks=iperf --machine_type=f1-micro --benchmark_config_file=iperf.yaml --zone=us-central1-f --ip_addresses=EXTERNAL
  • ip_addresses=EXTERNAL tells PerfKit Bechmarker not to use 10.X (ie Internal) machine addresses that all Cloud VMs have. Just use the external IP address.

If a benchmark requires two machines like iperf, you can have two machines in the same YAML file as shown below. This means you can indeed run between two machines and never provision any VMs in the Cloud.

static_vms:
  - &vm1
    ip_address: <ip1>
    user_name: connormccoy
    ssh_private_key: /home/connormccoy/.ssh/google_compute_engine
    internal_ip: 10.240.223.37
    install_packages: false
  - &vm2
    ip_address: <ip2>
    user_name: connormccoy
    ssh_private_key: /home/connormccoy/.ssh/google_compute_engine
    internal_ip: 10.240.234.189
    ssh_port: 2222

iperf:
  vm_groups:
    vm_1:
      static_vms:
        - *vm2
    vm_2:
      static_vms:
        - *vm1

Specifying Flags in Configuration Files

You can now specify flags in configuration files by using the flags key at the top level in a benchmark config. The expected value is a dictionary mapping flag names to their new default values. The flags are only defaults; it's still possible to override them via the command line. It's even possible to specify conflicting values of the same flag in different benchmarks:

iperf:
  flags:
    machine_type: n1-standard-2
    zone: us-central1-b
    iperf_sending_thread_count: 2

netperf:
  flags:
    machine_type: n1-standard-8

The new defaults will only apply to the benchmark in which they are specified.

How to Extend PerfKit Benchmarker

First start with the CONTRIBUTING.md file. It has the basics on how to work with PerfKitBenchmarker, and how to submit your pull requests.

In addition to the CONTRIBUTING.md file we have added a lot of comments into the code to make it easy to:

  • Add new benchmarks (e.g.: --benchmarks=<new benchmark>)
  • Add new package/os type support (e.g.: --os_type=<new os type>)
  • Add new providers (e.g.: --cloud=<new provider>)
  • etc.

Even with lots of comments we make to support more detailed documention. You will find the documatation we have on the wiki. Missing documentation you want? Start a page and/or open an issue to get it added.

Integration Testing

In addition to regular unit tests, which are run via hooks/check-everything, PerfKit Benchmarker has integration tests, which create actual cloud resources and take time and money to run. For this reason, they will only run when the variable PERFKIT_INTEGRATION is defined in the environment. For instance, the command

$ PERFKIT_INTEGRATION=1 hooks/check-everything

will run the integration tests. The integration tests depend on having installed and configured all of the relevant cloud provider SDKs, and will fail if you have not done so.

Planned Improvements

Many... please add new requests via GitHub issues.