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PerfKitBenchmarker

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.

KNOWN ISSUES

LICENSING

PerfKitBenchmarker 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 PerfKitBenchmarker.

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 Prerequisites

Before you can run the PerfKit Benchmaker on Cloud providers you need accounts and access:

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 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.

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

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

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.

Set your credentials up: https://developers.google.com/cloud/sdk/gcloud/#gcloud.auth. Run the command below. It will print a web page URL. Navigate there, authorize the gcloud instance you just installed to use the services it lists, copy the access token and give it to the shell prompt.

$ gcloud auth login

You will need a project ID before you can run. Please navigate to https://console.developers.google.com and create one.

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 awscli

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.

Go here, and follow the setup instructions.

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

$ sudo npm install azure-cli -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

DigitalOcean configuration and credentials

PerfKitBenchmarker 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 PerfKitBenchmarker with read/write access in Settings / API: https://cloud.digitalocean.com/settings/applications

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,...

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

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.

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).

  1. 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).

  1. 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.

Install PerfKit

Download PerfKitBenchmarker from GitHub.

Install PerfKit Benchmakrer dependencies

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

RUNNING A SINGLE BENCHMARK

PerfKitBenchmarks 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 DigitalOcean

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

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 dont 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 PerfKitBenchmaker.

Flag Notes
--help see all flags
--cloud Check where the bechmarks are run. Choices are GCP, AWS, Azure, or DigitalOcean
--zone This flag allows you to override the default zone. See below.
--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

The zone (region) as specified with the --zone flag uses the same value that the Cloud CLIs take:

Cloud Default Notes
GCP us-central1-a
AWS us-east-1a
Azure East US
DigitalOcean sfo1 You must use a zone that supports the features 'metadata' (for cloud config) and 'private_networking'.

ADVANCED: HOW TO RUN BENCHMARKS WITHOUT CLOUD PROVISIONING (eg: local workstation)

It is possible to run PerfKitBenchmarker 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 PerfKitBenchmarker) 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 JSON file describing how to connect to the machine as follows:

[
 {"ip_address": "170.200.60.23",
  "user_name": "voellm",
  "keyfile_path": "/home/voellm/perfkitkeys/my_key_file.pem",
  "scratch_disk_mountpoints": ["/scratch-disk"],
  "zone": "Siberia"}
]
  • The ip_address is the address where you want benchmarks to run.
  • keyfile_file is where to find the private ssh key.
  • zone can be anything you want. It is used when publishing results.
  • scratch_disk_mountpoints is used by all benchmarks which use disk (i.e., fio, bonnie++, many others).

I called my file Siberia.json 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 --static_vm_file=Siberia.json --zone=us-central1-f --ip_addresses=EXTERNAL
  • ip_addresses=EXTERNAL tells PerfKitBechmarker 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 both machines into the same json file as shown below. This means you can indeed run between two machines and never provision any VM's in the Cloud.

[
  {
    "ip_address": "<ip1>",
    "user_name": "connormccoy",
    "keyfile_path": "/home/connormccoy/.ssh/google_compute_engine",
    "internal_ip": "10.240.223.37",
    "install_packages", false
  },
  {
    "ip_address": "<ip2>",
    "user_name": "connormccoy",
    "keyfile_path": "/home/connormccoy/.ssh/google_compute_engine",
    "scratch_disk_mountpoints": ["/tmp/google-pkb"],
    "internal_ip": "10.240.234.189",
    "ssh_port": 2222
  }
]

HOW TO EXTEND PerfKitBenchmarker

First start with the [CONTRIBUTING.md] (https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/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] (https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/blob/master/CONTRIBUTING.md) file we have added a lot of comments into the code to make it easy to;

  • Add new benchmarks (eg: --benchmarks=)
  • Add new package/os type support (eg: --os_type=)
  • Add new providers (eg: --cloud=)
  • etc...

Even with lots of comments we make to support more detailed documention. You will find the documatation we have on the [Wiki pages] (https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/wiki). Missing documentation you want? Start a page and/or open an [issue] (https://github.com/GoogleCloudPlatform/PerfKitBenchmarker/issues) to get it added.

PLANNED IMPROVEMENTS

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