diff --git a/.bumpversion.cfg b/.bumpversion.cfg index 75cd2b2..4ccfe4f 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.2.0 +current_version = 0.3.0 commit = True tag = True diff --git a/.ssh.cfg b/.ssh.cfg new file mode 100644 index 0000000..468d8bf --- /dev/null +++ b/.ssh.cfg @@ -0,0 +1,20 @@ + +Host api.twist.tu-berlin.de + User proxyuser + HostName api.twist.tu-berlin.de + Port 2222 + ProxyCommand none + BatchMode yes + PasswordAuthentication no + +Host tplink* + User root + +Host tplink* nuc* + ProxyCommand ssh -q -p 2222 -W %h:%p proxyuser@api.twist.tu-berlin.de + ControlMaster auto + ControlPath ~/.ssh/twist-%r@%h:%p + ControlPersist 1h + ServerAliveInterval 60 + TCPKeepAlive yes + compression no diff --git a/Pipfile b/Pipfile index 90c2f42..aa94483 100644 --- a/Pipfile +++ b/Pipfile @@ -19,6 +19,8 @@ invoke = "*" tqdm = "*" seaborn = "*" matplotlib = "*" +bokeh = "*" +networkx = "*" [dev-packages] diff --git a/Pipfile.lock b/Pipfile.lock index ac1a1d5..28651fe 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -1,7 +1,7 @@ { "_meta": { "hash": { - "sha256": "9d3c1ff396d642999521b18703dd6ae8741a82be8a73235475e8b0cf22117ccf" + "sha256": "4424804d46b480cac6aae3d6c331d031858fa012a56f3921854f53e2a485d19d" }, "host-environment-markers": { "implementation_name": "cpython", @@ -9,9 +9,9 @@ "os_name": "posix", "platform_machine": "x86_64", "platform_python_implementation": "CPython", - "platform_release": "4.14.4-1-ARCH", + "platform_release": "4.14.7-1-ARCH", "platform_system": "Linux", - "platform_version": "#1 SMP PREEMPT Tue Dec 5 19:10:06 UTC 2017", + "platform_version": "#1 SMP PREEMPT Sun Dec 17 19:13:39 UTC 2017", "python_full_version": "3.6.3", "python_version": "3.6", "sys_platform": "linux" @@ -37,10 +37,17 @@ }, "asn1crypto": { "hashes": [ - "sha256:654b7db3b120e23474e9a1e5e38d268c77e58a9e17d2cb595456c37309846494", - "sha256:0874981329cfebb366d6584c3d16e913f2a0eb026c9463efcc4aaf42a9d94d70" + "sha256:2f1adbb7546ed199e3c90ef23ec95c5cf3585bac7d11fb7eb562a3fe89c64e87", + "sha256:9d5c20441baf0cb60a4ac34cc447c6c189024b6b4c6cd7877034f4965c464e49" ], - "version": "==0.23.0" + "version": "==0.24.0" + }, + "attrs": { + "hashes": [ + "sha256:e7d51b70f19a4da5fe6b3c9938983e0af3b91e230edc504bd73c443d98037063", + "sha256:c78f53e32d7cf36d8597c8a2c7e3c0ad210f97b9509e152e4c37fa80869f823c" + ], + "version": "==17.3.0" }, "babel": { "hashes": [ @@ -90,19 +97,25 @@ ], "version": "==2.1.2" }, + "bokeh": { + "hashes": [ + "sha256:6e28cbfd88de0c459435278b75f9982591ec0aaa3d37a195052645e1c62e89e3" + ], + "version": "==0.12.13" + }, "boto3": { "hashes": [ - "sha256:edd35eb7cd8d56f08e65b112206936cf9f76bb406fb15f63075ff72331ef6f5f", - "sha256:332c6a17fd695581dd6f9ed825ce13c2d5ee3a6f5e1b079bed0ff7293809faf0" + "sha256:df129fdde1e24b20e565f27be1235cab4bde9364f1fa6775744aa9b377a763c8", + "sha256:18bc8affbb575db705a6262cc09a9a1a003a2f3c5cc1c64c9d55e637cd68a870" ], - "version": "==1.4.8" + "version": "==1.5.8" }, "botocore": { "hashes": [ - "sha256:415f561d58ecfd18b06c158ce00e91400b3475c51af8cd12fad670bd55f581e9", - "sha256:740f77e855b06a6061e2a7c5513b943b61758f3ebd483160935473922739c37a" + "sha256:8763f1d1ab9b56e2cde6d63af92e8e7df942c24921888e3d25476c98e98e0e05", + "sha256:40ab4b36df9c33a7c8a715b5d36d2c41e1798a66068394a579e376fbb4121ce3" ], - "version": "==1.8.13" + "version": "==1.8.22" }, "cffi": { "hashes": [ @@ -138,7 +151,6 @@ "sha256:75e1de9ba7c155d89bcf67d149b1c741df553c8158536e8d27e63167403159af", "sha256:ab87dd91c0c4073758d07334c1e5f712ce8fe48f007b86f8238773963ee700a6" ], - "markers": "platform_python_implementation != 'PyPy'", "version": "==1.11.2" }, "click": { @@ -257,7 +269,6 @@ "sha256:fcc6d46f08c3c4de7b15ae1c426e15be1b7932bcda9d83ce1a4304e8c1129df3", "sha256:51c158a6c8b899898d1c91c6b51a34110196815cc905f9be0fa5878e19355608" ], - "markers": "python_version >= '3.3'", "version": "==6.2.1" }, "ipython-genutils": { @@ -269,17 +280,17 @@ }, "ipywidgets": { "hashes": [ - "sha256:f7640c6582bfc340bb426e5bb16c4d7fc0ac76aa808f7d1b9092db4c9c6d5bec", - "sha256:321be3dc48193130ba16e8080172bb5cd052eb65e3ad0ea7b5f80ff73e24bc54" + "sha256:05e10c4eccd8505cda16c6bb3cf78d3ddee913004c557f8da7ea9c81162d78bd", + "sha256:3e2be7dea4f97c9a4df71ef065cad9f2e420dd901127bf7cb690fb56d2b34ea3" ], - "version": "==7.0.5" + "version": "==7.1.0" }, "jedi": { "hashes": [ - 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"sha256:b573acb69f66a970197b5fdbbdfad3b8a417a520e383133b2b4e708f104bfc9a", - "sha256:c5279916bb417aa2bf634648ff895cf35dce371d7319744884827bfad06f8d7b" + "sha256:a7bb86fe0844304869d1c08e8bd0e52be931228483025c422917411ab82d628a", + "sha256:5815f3fe254e5665f3c5d6f54f086c2502035cb631a91341591b5a564203cffb" ], - "version": "==0.1.0" + "version": "==0.1.1" }, "pbr": { "hashes": [ @@ -502,6 +513,12 @@ ], "version": "==0.7.4" }, + "pluggy": { + "hashes": [ + "sha256:7f8ae7f5bdf75671a718d2daf0a64b7885f74510bcd98b1a0bb420eb9a9d0cff" + ], + "version": "==0.6.0" + }, "prompt-toolkit": { "hashes": [ "sha256:3f473ae040ddaa52b52f97f6b4a493cfa9f5920c255a12dc56a7d34397a398a4", @@ -515,8 +532,16 @@ "sha256:e8c43b5eee76b2083a9badde89fd1bbce6c8942d1045146e100b7b5e014f4f1a", "sha256:e64193f0047ad603b71f202332ab5527c5e52aa7c8b609704fc28c0dc20c4365" ], + "markers": "os_name != 'nt'", "version": "==0.5.2" }, + "py": { + "hashes": [ + "sha256:8cca5c229d225f8c1e3085be4fcf306090b00850fefad892f9d96c7b6e2f310f", + "sha256:ca18943e28235417756316bfada6cd96b23ce60dd532642690dcfdaba988a76d" + ], + "version": "==1.5.2" + }, "pyasn1": { "hashes": [ "sha256:f81c96761fca60d64b1c9b79ec2e40cf9495a745cf570613079ef324aeb9672b", @@ -594,6 +619,13 @@ ], "version": "==2.2.0" }, + "pytest": { + "hashes": [ + "sha256:ae4a2d0bae1098bbe938ecd6c20a526d5d47a94dc42ad7331c9ad06d0efe4962", + "sha256:cf8436dc59d8695346fcd3ab296de46425ecab00d64096cebe79fb51ecb2eb93" + ], + "version": "==3.3.1" + }, "python-dateutil": { "hashes": [ "sha256:95511bae634d69bc7329ba55e646499a842bc4ec342ad54a8cdb65645a0aad3c", @@ -757,18 +789,20 @@ }, "widgetsnbextension": { "hashes": [ - "sha256:1c53272068456501f75a99dc73bf854dba853afc528d335b83107ffe9b102e06", - "sha256:a57e29e733b989e68fdd0f3d6927a3691763b39792591d573b95a89a5a12ec15" + "sha256:d38e6873ea18f22c2a2b32025cdde35b635efa46b234e8738c81eec993f0bf1e", + "sha256:67fc28c3b9fede955d69bccbd92784e3f0c6d0dee3a71532cd3367c257feb178" ], - "version": "==3.0.8" + "version": "==3.1.0" } }, "develop": { "blessings": { "hashes": [ - "sha256:edc5713061f10966048bf6b40d9a514b381e0ba849c64e034c4ef6c1847d3007" + "sha256:26dbaf2f89c3e6dee11c10f7c0b85756ed75cf602b1bb7935b4efd8ed67a000f", + "sha256:466e43ff45723b272311de0437649df464b33b4daba7a54c69493212958e19c7", + "sha256:74919575885552e14bc24a68f8b539690bd1b5629180faa830b1a25b8c7fb6ea" ], - "version": "==1.6" + "version": "==1.6.1" }, "bpython": { "hashes": [ diff --git a/README.md b/README.md index bcd9643..d6f3ab8 100644 --- a/README.md +++ b/README.md @@ -1,25 +1,20 @@ # WaLKER - Wifi Linux Kernel ExpeRiment -Perform a full mesh measurements of the interference and throughput between all WiFi nodes deployed in TWIST testbed in TKN building. - -It is helpful to know the channel quality between each pair of nodes in a testbed. -There can, of course, be interference on the channel and this can change over time. -We have to keep in mind that the measurement of the channel quality is only one of the approaches. -But even this approach can be helpful to understand happenings on the channel. -It is a good idea to measure the throughput between a pair of nodes to get an indication of the channel quality. +We compare the Wi-Fi performance across recent Linux kernel releases. +For each experiment run we use 6 nodes loaded with the same kernel version. +Sequentially, for each possible (ordered) pair of nodes, we perform a following measurement. +We setup one node as \ac{AP} and one as \ac{STA} and measure UDP throughput between them, with traffic flow from \ac{STA} to \ac{AP}. +Use WiFi nodes deployed in [TWIST testbed](http://www.twist.tu-berlin.de/) in TKN building. +Nodes are spread over different distances to eliminate the impact of this parameter and increase the validity of the results. All nodes of the testbed operate in the 2.4 GHz and in the 5 GHz band. -The goal of the experiment is to measure the channel quality between all pair of nodes by its throughput. -This measurement should be done twice: once in the 2.4GHz and in the 5GHz band. - -Other experimenters can modify this script for their own experiment. -To enable them to do this a documentation of the script is needed. -## Usage and installation +Feel free to clone this repository and modify for own experiments. +This is a showcase for *DevOps based Toolchain for Wireless Network Experimentation Support* paper. -### Preparation +## Installation -We leverage Python 3 tools in the whole experiment. +We leverage Python 3.6 tools in the whole experiment. We use [pipenv](https://docs.pipenv.org/index.html) as a packaging tool. Please install it first with: @@ -33,24 +28,125 @@ To bootstrap new virtual environment enter the project directory and run: pipenv ``` -### Data analysis +## Usage -To view results start jupyter notebook: +We assume all commands are executed within `pipenv shell`. + +### Node selection + +The nodes that are used in the experiment are listed in one [YAML](https://www.yaml.org) file under `node_selection/hosts`. The file is a valid [ansible inventory](http://docs.ansible.com/ansible/latest/intro_inventory.html) hosts file and is used in multiple stages throughout the experiment. +In the same directory, you'll find a [Jinja2](http://jinja.pocoo.org/docs/2.10/) template for a request [RSpec](http://groups.geni.net/geni/wiki/GENIExperimenter/RSpecs), that will be used to request node reservation later on. There are also two pre-rendered RSpec files `rendered.rspec` and `rendered_small.rspec`, which can be used as-is to reserve the corresponding nodes. + +### OS image preparation + +This stage consists of three essential steps: +1. Building the disk image +2. Uploading the image +3. Rendering the RSpec with the corresponding link to the image + +The three steps are conveniently wrapped in one script `images/preparation/prepare.py`. To display available commands and options use +```bash +./prepare.py --help ``` -pipenv run jupyter notebook + +You can build a xenial image with additional linux kernels `v3.18.87` and `v4.14.5`, upload it to an [Amazon S3](https://aws.amazon.com/s3/?nc1=h_ls) bucket and render the corresponding RSpec using above command. + +Note that you will need to setup an AWS bucket and provide your credentials. You will find the resulting RSpec under `node_selection/rendered.rspec`. Use this file to request reservation at the testbed (see TODO). + +The following three paragraphs give a more detailed description of the process + +#### Building the disk image + +We use openstack's [diskimage-builder](https://docs.openstack.org/diskimage-builder/latest/) to build a ready-to-use ubuntu disk image, which can be flashed and booted on the nodes. We add two elements to customize the image according to our needs: `images/add_elements/twist` installs python3 into the image and allows to use a customized `sources.list` file, which is rendered from the template under `images/sources.list.jn2` and can be modified to include additional repositories for aptitude. The `images/add_elements/mainline-kernel` element allows to install user-defined mainline kernels into the image. The corresponding `.deb` packages are automatically downloaded and installed during image creation. + +This functionality is provided by the `build` command of the `prepare.py` script: + +```bash +./prepare.py --release xenial --diskimage myimage.tgz --kernel 4.14.5 --kernel 3.18.87 build ``` -and open one of the notebooks in `analysis` folder. +#### Uploading the image + +In order to deploy the image to the nodes, the testbed management software of `TWIST` must be able to fetch the image from an http server. As an example, we use an [Amazon S3](https://aws.amazon.com/s3/?nc1=h_ls) bucket to upload the file and generate a publicly available URL, from which the image can be fetched. The corresponding code can be found in `images/awss3.py`. Note that you need to setup the `twistimages` bucket and provide your `AWS` credentials in order to use this script. You are encouraged to use your own cloud storage or self-hosted web server to provide the image. + +Use the following command to upload your exisiting image `myimage.tgz` to the cloud. It will output the resulting URL, which you can use to render the RSpec + +```bash +./prepare.py --diskimage myimage.tgz upload +``` + +#### Rendering the RSpec + +Now that we have uploaded the image and can provide a URL, the RSpec can be rendered using the hosts file under `node_selection/hosts` and the previously mentioned request template under `node_selection/rspec.jn2`. + +Use the following command to render the RSpec for the experiment, using the URL to your uploaded image: + +```bash +./prepare.py render_rspec --url http://myserver/myimage.tgz +``` + +### OS image deployment -## Useful commands +#### Using SFA testbed + +At this point, we can request reservation of the corresponding nodes at the testbed using the previously generated RSpec file. The reservation consists of three consecutive steps: + +1. Allocation - Claim exclusive access to the nodes for the time of the experiment +2. Provisioning - Prepare the nodes for the experiment by flashing the disk image and allowing remote access +3. Starting - Boot the disk image, allowing you to login to the nodes by ssh + +[jFed](http://jfed.iminds.be/) is a Java-based framework for node management in federated testbeds. It offers a GUI for defining the resources used in an experiment, but also allows to use existing RSpec files to reserve nodes from a range of federated testbeds. Refer to the official instructions for [getting started](http://jfed.iminds.be/get_started/) and [creating a first experiment](http://doc.ilabt.iminds.be/jfed-documentation-5.7/firstexperiment.html). + +We recommend [omni](https://github.com/GENI-NSF/geni-tools/wiki/Omni) as a python-based CLI alternative to jFed. Follow the official [instructions](https://github.com/GENI-NSF/geni-tools/wiki/Omni) for getting started and usage. + +#### Using Ad-hoc setup + +We also provide scripts to download and boot the image on an ad-hoc setup of hardware. The mechanism is based on the `kexec` feature of the Linux kernel. +Please refer to the scripts located in `images/deployment`. The playbook `deploy.yml` downloads the image to the target nodes and handles basic configuration. The key task is execution of the `bootos` module. This is an Ansible action plugin, provided by us. It consists of two parts. The first one `images/deployment/action_plugins/bootos.py` is executed locally on the machine, that is running ansible. The actual module (`images/deployment/library/bootos` is executed remotely on the target host and uses kexec to boot a specified kernel with user-defined parameters. + +Assuming all nodes are prepared with a basic OS with kexec support and the experimenter has ssh access, the experimental OS can be downloaded and booted by executing the corresponding playbook: + +```bash +ansible-playbook deployment.yml +``` + +### Software deployment + +We use this step to deploy software, which is common to all experiments that we are going to perform. We use [ansible](https://www.ansible.com/) to concisely define the desired state of the nodes (which packets should be installed, regulatory settings, etc.). The corresponding playbook can be found under `deployment/main.yml`. To apply the configuration to each node in the experiment we call ansible: ```bash -fab config.reserve set_hosts config.install +ansible-playbook main.yml +``` + +### Experiment run -fab set_hosts full_scan +Now that we have an experimental OS with all the required software up and running, we can start the actual experiments. In the `experiment` directory there is a convenient script `experiment.py` that can be used to orchestrate all experiments. -fab config wifi.scan -fab -H nuc4 wifi.create_ap:iface=wlan0 -fab -H nuc11 wiwifi.connect:iface=wlan1 +The first step is to activate the kernel, for which we want to measure Wifi throughput, in a two step procedure. + +```bash +./experiment.py select_kernel ``` + +This will prompt the user to select one of the installed kernels. Note that this just sets a symlink to the specified kernel, but does not yet load it. Therefore the nodes have to be rebooted in order to activate the new kernel. This can be achieved using the testbed API (or using Ansible/Fabric in an Ad-Hoc setting). + +When the nodes have rebooted, it is time to start the actual measurements. This consists of configuration, i.e. starting an Access Point on a node and connecting a Station and starting iperf to measure throughput between the pair, saving results to the local filesystem. This procedure is automatically repeated for every constellation when calling: + +```bash +./experiment.py run +``` + +After the measurements have finished, repeat the whole procedure, starting by selecting another kernel. + +### Data analysis + +To view results start jupyter notebook: + +```bash +jupyter notebook +# or more directly +jupyter notebook analysis/Connectivity\ Analysis.ipynb +``` + +and open one of the notebooks in `analysis` folder. diff --git a/analysis/Connectivity Analysis.ipynb b/analysis/Connectivity Analysis.ipynb index 1d0f746..e56a797 100644 --- a/analysis/Connectivity Analysis.ipynb +++ b/analysis/Connectivity Analysis.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# Global imports\n", "%matplotlib inline\n", @@ -23,68 +14,942 @@ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", - "import analysis as an" + "import numpy as np\n", + "import analysis as an\n", + "from pathlib import Path\n", + "\n", + "project_folder = Path(an.__file__).parent.parent.resolve()\n", + "\n", + "# plt.style.use('seaborn')\n", + "sns.set_context('paper', font_scale=2)\n", + "sns.set_style('whitegrid')\n", + "sns.set_color_codes('deep')\n", + "rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})\n", + "rc('text', usetex=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Connectivity Analysis\n" + "# Connectivity Analysis\n", + "\n", + "## Data loading" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Storing results in: /home/chwalisz/Code/twist/experiments/walker/results\n", + "Available data paths:\n" + ] + }, { "data": { "text/plain": [ - "[PosixPath('/home/chwalisz/Code/twist/experiments/wifi-channel/data/2017-12-07-141330'),\n", - " PosixPath('/home/chwalisz/Code/twist/experiments/wifi-channel/data/2017-12-11-122231'),\n", - " PosixPath('/home/chwalisz/Code/twist/experiments/wifi-channel/data/2017-12-11-155541'),\n", - " PosixPath('/home/chwalisz/Code/twist/experiments/wifi-channel/data/2017-12-12-110727'),\n", - " PosixPath('/home/chwalisz/Code/twist/experiments/wifi-channel/data/2017-12-12-111518'),\n", - " PosixPath('/home/chwalisz/Code/twist/experiments/wifi-channel/data/2017-12-12-111553'),\n", - " PosixPath('/home/chwalisz/Code/twist/experiments/wifi-channel/data/2017-12-12-120803'),\n", - " PosixPath('/home/chwalisz/Code/twist/experiments/wifi-channel/data/2017-12-13-122225'),\n", - " PosixPath('/home/chwalisz/Code/twist/experiments/wifi-channel/data/short')]" + "[PosixPath('/home/chwalisz/Code/twist/experiments/walker/data/2018-01-17-144113'),\n", + " PosixPath('/home/chwalisz/Code/twist/experiments/walker/data/2018-01-17-192523'),\n", + " PosixPath('/home/chwalisz/Code/twist/experiments/walker/data/outdated')]" ] }, - "execution_count": 18, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from pathlib import Path\n", + "data_folder = project_folder / 'data'\n", + "results_folder = project_folder / 'results'\n", + "results_folder.mkdir(exist_ok=True)\n", + "print(f'Storing results in: {results_folder}')\n", + "print('Available data paths:')\n", + "[x for x in sorted(data_folder.iterdir()) if x.is_dir()]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Throughput [Mbps]bytesendomittedpacketssecondssocketstartClientAccess PointKernelTimestampSystem InfoProtocolConnectionfile
022673100.028344321.00011False3461.00011040.00000nuc6nuc44.14.5-041405-genericWed, 17 Jan 2018 14:01:42 GMTLinux nuc6 4.14.5-041405-generic #201712101332...UDPnuc4\\nnuc6nuc4-nuc6
126671700.033341442.00016False4071.00005041.00011nuc6nuc44.14.5-041405-genericWed, 17 Jan 2018 14:01:42 GMTLinux nuc6 4.14.5-041405-generic #201712101332...UDPnuc4\\nnuc6nuc4-nuc6
227394300.034242563.00015False4180.99998942.00016nuc6nuc44.14.5-041405-genericWed, 17 Jan 2018 14:01:42 GMTLinux nuc6 4.14.5-041405-generic #201712101332...UDPnuc4\\nnuc6nuc4-nuc6
328180500.035225604.00015False4301.00000043.00015nuc6nuc44.14.5-041405-genericWed, 17 Jan 2018 14:01:42 GMTLinux nuc6 4.14.5-041405-generic #201712101332...UDPnuc4\\nnuc6nuc4-nuc6
428377000.035471365.00015False4331.00000044.00015nuc6nuc44.14.5-041405-genericWed, 17 Jan 2018 14:01:42 GMTLinux nuc6 4.14.5-041405-generic #201712101332...UDPnuc4\\nnuc6nuc4-nuc6
\n", + "
" + ], + "text/plain": [ + " Throughput [Mbps] bytes end omitted packets seconds socket \\\n", + "0 22673100.0 2834432 1.00011 False 346 1.000110 4 \n", + "1 26671700.0 3334144 2.00016 False 407 1.000050 4 \n", + "2 27394300.0 3424256 3.00015 False 418 0.999989 4 \n", + "3 28180500.0 3522560 4.00015 False 430 1.000000 4 \n", + "4 28377000.0 3547136 5.00015 False 433 1.000000 4 \n", + "\n", + " start Client Access Point Kernel \\\n", + "0 0.00000 nuc6 nuc4 4.14.5-041405-generic \n", + "1 1.00011 nuc6 nuc4 4.14.5-041405-generic \n", + "2 2.00016 nuc6 nuc4 4.14.5-041405-generic \n", + "3 3.00015 nuc6 nuc4 4.14.5-041405-generic \n", + "4 4.00015 nuc6 nuc4 4.14.5-041405-generic \n", + "\n", + " Timestamp \\\n", + "0 Wed, 17 Jan 2018 14:01:42 GMT \n", + "1 Wed, 17 Jan 2018 14:01:42 GMT \n", + "2 Wed, 17 Jan 2018 14:01:42 GMT \n", + "3 Wed, 17 Jan 2018 14:01:42 GMT \n", + "4 Wed, 17 Jan 2018 14:01:42 GMT \n", + "\n", + " System Info Protocol Connection \\\n", + "0 Linux nuc6 4.14.5-041405-generic #201712101332... UDP nuc4\\nnuc6 \n", + "1 Linux nuc6 4.14.5-041405-generic #201712101332... UDP nuc4\\nnuc6 \n", + "2 Linux nuc6 4.14.5-041405-generic #201712101332... UDP nuc4\\nnuc6 \n", + "3 Linux nuc6 4.14.5-041405-generic #201712101332... UDP nuc4\\nnuc6 \n", + "4 Linux nuc6 4.14.5-041405-generic #201712101332... UDP nuc4\\nnuc6 \n", + "\n", + " file \n", + "0 nuc4-nuc6 \n", + "1 nuc4-nuc6 \n", + "2 nuc4-nuc6 \n", + "3 nuc4-nuc6 \n", + "4 nuc4-nuc6 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_list = [\n", + " an.get_iperf_folder(data_folder / '2018-01-17-144113', recursive=True),\n", + " an.get_iperf_folder(data_folder / '2018-01-17-192523', recursive=True),\n", + "]\n", + "df = pd.concat(df_list)\n", "\n", - "data_folder = Path.cwd() / '..' / 'data'\n", - "data_folder = data_folder.resolve()\n", - "[x for x in sorted(data_folder.iterdir()) if x.is_dir()]" + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Throughput analysis" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
Access PointClientKernel
nuc10nuc123.18.87-031887-generic600.01.982681e+061.107750e+06262140.0851926.52457535.03080190.03670010.0
4.14.5-041405-generic600.02.531438e+061.204261e+06196587.01441850.02555805.03473187.56029320.0
nuc133.18.87-031887-generic600.05.511620e+071.023968e+0644164800.054639625.055313250.055772100.057021600.0
4.14.5-041405-generic600.05.507240e+071.530037e+0634137000.054525700.055379850.055966525.057411200.0
nuc43.18.87-031887-generic600.02.065760e+071.105270e+0617891400.019791900.020775100.021561400.023134200.0
4.14.5-041405-generic600.02.901202e+075.950485e+0614221200.025540875.027525150.034762475.039263400.0
nuc63.18.87-031887-generic600.04.771828e+071.001825e+0639446700.047253800.047939300.048366925.049613400.0
4.14.5-041405-generic600.08.020296e+071.683148e+0668542400.079607175.080666350.081297525.082691300.0
nuc11nuc133.18.87-031887-generic600.08.500891e+064.330391e+056291410.08257407.58519500.08781850.09568570.0
4.14.5-041405-generic600.09.240247e+064.518421e+061376080.05243020.08060740.013762400.017302900.0
nuc43.18.87-031887-generic600.01.873739e+076.947005e+0515990900.018284544.018808900.019218950.020316200.0
4.14.5-041405-generic600.02.109330e+073.064439e+0612646600.018611525.019529850.023986350.028049400.0
nuc12nuc103.18.87-031887-generic600.03.695138e+063.183239e+05393238.03604500.03735535.03801182.55046220.0
nuc43.18.87-031887-generic600.03.696525e+071.087711e+0630797100.036241075.037026450.037743750.040173700.0
4.14.5-041405-generic600.03.711183e+072.728012e+0623003100.036438175.037486600.038535300.041748400.0
nuc63.18.87-031887-generic600.01.572306e+079.192109e+0512255300.015073300.015859800.016449536.017695100.0
4.14.5-041405-generic600.01.898730e+072.895118e+068715770.017760050.018808700.019660600.027787200.0
nuc13nuc104.14.5-041405-generic600.05.575996e+071.158453e+0647901000.055178650.055967722.056557425.058265200.0
nuc114.14.5-041405-generic600.07.166688e+068.440859e+052949110.06684295.07274540.07798725.08651010.0
nuc43.18.87-031887-generic600.01.695798e+075.590556e+059632570.016711675.017038600.017301500.018154100.0
4.14.5-041405-generic600.01.393983e+071.244893e+069173430.013107875.014025700.014876600.016255000.0
nuc64.14.5-041405-generic600.03.825696e+078.938981e+0532112500.037814293.038467500.038868500.039847800.0
nuc4nuc103.18.87-031887-generic600.01.994752e+071.632116e+0616383900.019119875.019593350.020250700.026215200.0
4.14.5-041405-generic600.02.860831e+079.132692e+0522544400.028312900.028966350.029164400.029622600.0
nuc113.18.87-031887-generic600.01.454954e+073.596013e+0512646600.014419300.014615050.014762675.015270500.0
4.14.5-041405-generic600.01.744841e+071.755698e+069433910.015466500.018282900.018810075.019923100.0
nuc123.18.87-031887-generic600.03.282807e+071.482703e+0625100100.031916225.032900400.033884175.036175800.0
4.14.5-041405-generic600.02.815765e+071.071149e+0622607100.027459475.028114450.028770200.032831700.0
nuc133.18.87-031887-generic600.09.420252e+063.392312e+056683600.09240615.09437330.09634375.010027100.0
4.14.5-041405-generic600.01.421497e+071.102339e+0610287900.013500475.014287350.015007900.016842600.0
nuc63.18.87-031887-generic600.02.832870e+071.073818e+0621689100.027656575.028572900.029097988.030935000.0
4.14.5-041405-generic600.02.761621e+078.714244e+0522673100.027199800.027790250.028245650.029228200.0
nuc6nuc103.18.87-031887-generic600.05.555932e+079.214495e+0547244300.055245575.055704250.056166150.057804100.0
4.14.5-041405-generic600.05.590821e+071.311118e+0645875000.055506650.056164750.056750800.057799700.0
nuc123.18.87-031887-generic600.01.142139e+079.682329e+058650740.010747900.011534550.012140550.013500400.0
4.14.5-041405-generic600.01.520533e+077.952546e+0511861800.014680875.015269250.015794275.017170100.0
nuc133.18.87-031887-generic600.03.836400e+076.788698e+0534007900.038015400.038471000.038861725.039650700.0
4.14.5-041405-generic600.03.801797e+071.072993e+0631194300.037617175.038335450.038731000.039389800.0
nuc43.18.87-031887-generic600.03.865563e+077.449842e+0532959800.038338375.038731450.039126225.040239500.0
4.14.5-041405-generic600.04.564352e+078.018753e+0631391500.036421675.050790350.052708200.055377700.0
\n", + "
" + ], + "text/plain": [ + " count mean std \\\n", + "Access Point Client Kernel \n", + "nuc10 nuc12 3.18.87-031887-generic 600.0 1.982681e+06 1.107750e+06 \n", + " 4.14.5-041405-generic 600.0 2.531438e+06 1.204261e+06 \n", + " nuc13 3.18.87-031887-generic 600.0 5.511620e+07 1.023968e+06 \n", + " 4.14.5-041405-generic 600.0 5.507240e+07 1.530037e+06 \n", + " nuc4 3.18.87-031887-generic 600.0 2.065760e+07 1.105270e+06 \n", + " 4.14.5-041405-generic 600.0 2.901202e+07 5.950485e+06 \n", + " nuc6 3.18.87-031887-generic 600.0 4.771828e+07 1.001825e+06 \n", + " 4.14.5-041405-generic 600.0 8.020296e+07 1.683148e+06 \n", + "nuc11 nuc13 3.18.87-031887-generic 600.0 8.500891e+06 4.330391e+05 \n", + " 4.14.5-041405-generic 600.0 9.240247e+06 4.518421e+06 \n", + " nuc4 3.18.87-031887-generic 600.0 1.873739e+07 6.947005e+05 \n", + " 4.14.5-041405-generic 600.0 2.109330e+07 3.064439e+06 \n", + "nuc12 nuc10 3.18.87-031887-generic 600.0 3.695138e+06 3.183239e+05 \n", + " nuc4 3.18.87-031887-generic 600.0 3.696525e+07 1.087711e+06 \n", + " 4.14.5-041405-generic 600.0 3.711183e+07 2.728012e+06 \n", + " nuc6 3.18.87-031887-generic 600.0 1.572306e+07 9.192109e+05 \n", + " 4.14.5-041405-generic 600.0 1.898730e+07 2.895118e+06 \n", + "nuc13 nuc10 4.14.5-041405-generic 600.0 5.575996e+07 1.158453e+06 \n", + " nuc11 4.14.5-041405-generic 600.0 7.166688e+06 8.440859e+05 \n", + " nuc4 3.18.87-031887-generic 600.0 1.695798e+07 5.590556e+05 \n", + " 4.14.5-041405-generic 600.0 1.393983e+07 1.244893e+06 \n", + " nuc6 4.14.5-041405-generic 600.0 3.825696e+07 8.938981e+05 \n", + "nuc4 nuc10 3.18.87-031887-generic 600.0 1.994752e+07 1.632116e+06 \n", + " 4.14.5-041405-generic 600.0 2.860831e+07 9.132692e+05 \n", + " nuc11 3.18.87-031887-generic 600.0 1.454954e+07 3.596013e+05 \n", + " 4.14.5-041405-generic 600.0 1.744841e+07 1.755698e+06 \n", + " nuc12 3.18.87-031887-generic 600.0 3.282807e+07 1.482703e+06 \n", + " 4.14.5-041405-generic 600.0 2.815765e+07 1.071149e+06 \n", + " nuc13 3.18.87-031887-generic 600.0 9.420252e+06 3.392312e+05 \n", + " 4.14.5-041405-generic 600.0 1.421497e+07 1.102339e+06 \n", + " nuc6 3.18.87-031887-generic 600.0 2.832870e+07 1.073818e+06 \n", + " 4.14.5-041405-generic 600.0 2.761621e+07 8.714244e+05 \n", + "nuc6 nuc10 3.18.87-031887-generic 600.0 5.555932e+07 9.214495e+05 \n", + " 4.14.5-041405-generic 600.0 5.590821e+07 1.311118e+06 \n", + " nuc12 3.18.87-031887-generic 600.0 1.142139e+07 9.682329e+05 \n", + " 4.14.5-041405-generic 600.0 1.520533e+07 7.952546e+05 \n", + " nuc13 3.18.87-031887-generic 600.0 3.836400e+07 6.788698e+05 \n", + " 4.14.5-041405-generic 600.0 3.801797e+07 1.072993e+06 \n", + " nuc4 3.18.87-031887-generic 600.0 3.865563e+07 7.449842e+05 \n", + " 4.14.5-041405-generic 600.0 4.564352e+07 8.018753e+06 \n", + "\n", + " min 25% \\\n", + "Access Point Client Kernel \n", + "nuc10 nuc12 3.18.87-031887-generic 262140.0 851926.5 \n", + " 4.14.5-041405-generic 196587.0 1441850.0 \n", + " nuc13 3.18.87-031887-generic 44164800.0 54639625.0 \n", + " 4.14.5-041405-generic 34137000.0 54525700.0 \n", + " nuc4 3.18.87-031887-generic 17891400.0 19791900.0 \n", + " 4.14.5-041405-generic 14221200.0 25540875.0 \n", + " nuc6 3.18.87-031887-generic 39446700.0 47253800.0 \n", + " 4.14.5-041405-generic 68542400.0 79607175.0 \n", + "nuc11 nuc13 3.18.87-031887-generic 6291410.0 8257407.5 \n", + " 4.14.5-041405-generic 1376080.0 5243020.0 \n", + " nuc4 3.18.87-031887-generic 15990900.0 18284544.0 \n", + " 4.14.5-041405-generic 12646600.0 18611525.0 \n", + "nuc12 nuc10 3.18.87-031887-generic 393238.0 3604500.0 \n", + " nuc4 3.18.87-031887-generic 30797100.0 36241075.0 \n", + " 4.14.5-041405-generic 23003100.0 36438175.0 \n", + " nuc6 3.18.87-031887-generic 12255300.0 15073300.0 \n", + " 4.14.5-041405-generic 8715770.0 17760050.0 \n", + "nuc13 nuc10 4.14.5-041405-generic 47901000.0 55178650.0 \n", + " nuc11 4.14.5-041405-generic 2949110.0 6684295.0 \n", + " nuc4 3.18.87-031887-generic 9632570.0 16711675.0 \n", + " 4.14.5-041405-generic 9173430.0 13107875.0 \n", + " nuc6 4.14.5-041405-generic 32112500.0 37814293.0 \n", + "nuc4 nuc10 3.18.87-031887-generic 16383900.0 19119875.0 \n", + " 4.14.5-041405-generic 22544400.0 28312900.0 \n", + " nuc11 3.18.87-031887-generic 12646600.0 14419300.0 \n", + " 4.14.5-041405-generic 9433910.0 15466500.0 \n", + " nuc12 3.18.87-031887-generic 25100100.0 31916225.0 \n", + " 4.14.5-041405-generic 22607100.0 27459475.0 \n", + " nuc13 3.18.87-031887-generic 6683600.0 9240615.0 \n", + " 4.14.5-041405-generic 10287900.0 13500475.0 \n", + " nuc6 3.18.87-031887-generic 21689100.0 27656575.0 \n", + " 4.14.5-041405-generic 22673100.0 27199800.0 \n", + "nuc6 nuc10 3.18.87-031887-generic 47244300.0 55245575.0 \n", + " 4.14.5-041405-generic 45875000.0 55506650.0 \n", + " nuc12 3.18.87-031887-generic 8650740.0 10747900.0 \n", + " 4.14.5-041405-generic 11861800.0 14680875.0 \n", + " nuc13 3.18.87-031887-generic 34007900.0 38015400.0 \n", + " 4.14.5-041405-generic 31194300.0 37617175.0 \n", + " nuc4 3.18.87-031887-generic 32959800.0 38338375.0 \n", + " 4.14.5-041405-generic 31391500.0 36421675.0 \n", + "\n", + " 50% 75% max \n", + "Access Point Client Kernel \n", + "nuc10 nuc12 3.18.87-031887-generic 2457535.0 3080190.0 3670010.0 \n", + " 4.14.5-041405-generic 2555805.0 3473187.5 6029320.0 \n", + " nuc13 3.18.87-031887-generic 55313250.0 55772100.0 57021600.0 \n", + " 4.14.5-041405-generic 55379850.0 55966525.0 57411200.0 \n", + " nuc4 3.18.87-031887-generic 20775100.0 21561400.0 23134200.0 \n", + " 4.14.5-041405-generic 27525150.0 34762475.0 39263400.0 \n", + " nuc6 3.18.87-031887-generic 47939300.0 48366925.0 49613400.0 \n", + " 4.14.5-041405-generic 80666350.0 81297525.0 82691300.0 \n", + "nuc11 nuc13 3.18.87-031887-generic 8519500.0 8781850.0 9568570.0 \n", + " 4.14.5-041405-generic 8060740.0 13762400.0 17302900.0 \n", + " nuc4 3.18.87-031887-generic 18808900.0 19218950.0 20316200.0 \n", + " 4.14.5-041405-generic 19529850.0 23986350.0 28049400.0 \n", + "nuc12 nuc10 3.18.87-031887-generic 3735535.0 3801182.5 5046220.0 \n", + " nuc4 3.18.87-031887-generic 37026450.0 37743750.0 40173700.0 \n", + " 4.14.5-041405-generic 37486600.0 38535300.0 41748400.0 \n", + " nuc6 3.18.87-031887-generic 15859800.0 16449536.0 17695100.0 \n", + " 4.14.5-041405-generic 18808700.0 19660600.0 27787200.0 \n", + "nuc13 nuc10 4.14.5-041405-generic 55967722.0 56557425.0 58265200.0 \n", + " nuc11 4.14.5-041405-generic 7274540.0 7798725.0 8651010.0 \n", + " nuc4 3.18.87-031887-generic 17038600.0 17301500.0 18154100.0 \n", + " 4.14.5-041405-generic 14025700.0 14876600.0 16255000.0 \n", + " nuc6 4.14.5-041405-generic 38467500.0 38868500.0 39847800.0 \n", + "nuc4 nuc10 3.18.87-031887-generic 19593350.0 20250700.0 26215200.0 \n", + " 4.14.5-041405-generic 28966350.0 29164400.0 29622600.0 \n", + " nuc11 3.18.87-031887-generic 14615050.0 14762675.0 15270500.0 \n", + " 4.14.5-041405-generic 18282900.0 18810075.0 19923100.0 \n", + " nuc12 3.18.87-031887-generic 32900400.0 33884175.0 36175800.0 \n", + " 4.14.5-041405-generic 28114450.0 28770200.0 32831700.0 \n", + " nuc13 3.18.87-031887-generic 9437330.0 9634375.0 10027100.0 \n", + " 4.14.5-041405-generic 14287350.0 15007900.0 16842600.0 \n", + " nuc6 3.18.87-031887-generic 28572900.0 29097988.0 30935000.0 \n", + " 4.14.5-041405-generic 27790250.0 28245650.0 29228200.0 \n", + "nuc6 nuc10 3.18.87-031887-generic 55704250.0 56166150.0 57804100.0 \n", + " 4.14.5-041405-generic 56164750.0 56750800.0 57799700.0 \n", + " nuc12 3.18.87-031887-generic 11534550.0 12140550.0 13500400.0 \n", + " 4.14.5-041405-generic 15269250.0 15794275.0 17170100.0 \n", + " nuc13 3.18.87-031887-generic 38471000.0 38861725.0 39650700.0 \n", + " 4.14.5-041405-generic 38335450.0 38731000.0 39389800.0 \n", + " nuc4 3.18.87-031887-generic 38731450.0 39126225.0 40239500.0 \n", + " 4.14.5-041405-generic 50790350.0 52708200.0 55377700.0 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df = an.get_iperf_folder(data_folder / '2017-12-12-120803', recursive=True)\n", - "# df = an.get_iperf_folder(data_folder / '2017-12-12-111553', recursive=True)" + "throughput = df.groupby(['Access Point', 'Client', 'Kernel'])['Throughput [Mbps]'].describe()\n", + "throughput" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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GXfGqtQQrljfsM1K7T/sSEemoktVjMhw4x90vMbMngbOAR3fTfgJQCBzi7hEz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\npKHERERERJLG/weZtedPtNk0dwAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -92,112 +957,399 @@ } ], "source": [ - "fig, ax = plt.subplots(figsize=(8,8))\n", + "fig, ax = plt.subplots(figsize=(9,9))\n", + "\n", + "sns.boxplot(\n", + " x='Connection',\n", + " y='Throughput [Mbps]',\n", + " hue='Kernel',\n", + " data=df,\n", + " showfliers=True,\n", + " showmeans=True,\n", + " meanline=True,\n", + " order=list(df.groupby('Connection')['Throughput [Mbps]'].describe().sort_values('mean').index),\n", + " hue_order=sorted(df['Kernel'].unique()),\n", + "# split=True,\n", + "# cut=0,\n", + ");\n", + "ax.set_xlim([0, None])\n", + "ax.yaxis.set_major_formatter(an.bitrate_formatter)\n", + "plt.xticks(rotation=55)\n", + "fig.savefig(str(results_folder / 'throughput_kernels.pdf'), transparent=False, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(9,9))\n", + "\n", "sns.violinplot(\n", - " x='bits per second',\n", - " y='connection',\n", + " y='Throughput [Mbps]',\n", + " x='Connection',\n", + " hue='Kernel',\n", " data=df,\n", - " hue='mode',\n", - " split=True,\n", - " cut=0\n", + " showfliers=False,\n", + "# split=True,\n", + " cut=0,\n", + " order=list(df.groupby('Connection')['Throughput [Mbps]'].describe().sort_values('mean').index),\n", + " hue_order=sorted(df['Kernel'].unique()),\n", ");\n", - "ax.xaxis.set_major_formatter(an.bitrate_formatter)" + "ax.set_xlim([0, None])\n", + "plt.xticks(rotation=55)\n", + "ax.yaxis.set_major_formatter(an.bitrate_formatter)\n", + "# ax.xaxis.set_major_formatter(an.bitrate_formatter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Kernel comarison\n", + "\n", + "Looking at mean throughput" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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nuc43.865563e+074.564352e+07
\n", + "
" + ], "text/plain": [ - "array(['nuc6.1513076895.188685.6a968ecc3c0eb',\n", - " 'nuc4.1513077282.853218.12b7e5a027bda',\n", - " 'nuc4.1513077200.660557.2e6fd69a5cca8',\n", - " 'nuc4.1513076944.632141.5e2b8823278e3',\n", - " 'nuc4.1513077359.564923.794aff6504fa7',\n", - " 'nuc6.1513076974.564635.75324d281a7d4',\n", - " 'nuc6.1513077406.039662.20c528101ea8c',\n", - " 'nuc13.1513077007.614688.13707f2f2526',\n", - " 'nuc10.1513077217.064754.11879a3b6917',\n", - " 'nuc12.1513076878.760077.71f973397eb0',\n", - " 'nuc10.1513076862.348214.5a0aa4e82f79',\n", - " 'nuc10.1513077376.272670.4ffe78281429',\n", - " 'nuc6.1513077057.135969.4a3bcdde63eae',\n", - " 'nuc12.1513077233.463325.1952b95759c7',\n", - " 'nuc4.1513077027.160565.4b0e4c7e282dd'], dtype=object)" + "Kernel 3.18.87-031887-generic 4.14.5-041405-generic\n", + "Access Point Client \n", + "nuc10 nuc12 1.982681e+06 2.531438e+06\n", + " nuc13 5.511620e+07 5.507240e+07\n", + " nuc4 2.065760e+07 2.901202e+07\n", + " nuc6 4.771828e+07 8.020296e+07\n", + "nuc11 nuc13 8.500891e+06 9.240247e+06\n", + " nuc4 1.873739e+07 2.109330e+07\n", + "nuc12 nuc10 3.695138e+06 NaN\n", + " nuc4 3.696525e+07 3.711183e+07\n", + " nuc6 1.572306e+07 1.898730e+07\n", + "nuc13 nuc10 NaN 5.575996e+07\n", + " nuc11 NaN 7.166688e+06\n", + " nuc4 1.695798e+07 1.393983e+07\n", + " nuc6 NaN 3.825696e+07\n", + "nuc4 nuc10 1.994752e+07 2.860831e+07\n", + " nuc11 1.454954e+07 1.744841e+07\n", + " nuc12 3.282807e+07 2.815765e+07\n", + " nuc13 9.420252e+06 1.421497e+07\n", + " nuc6 2.832870e+07 2.761621e+07\n", + "nuc6 nuc10 5.555932e+07 5.590821e+07\n", + " nuc12 1.142139e+07 1.520533e+07\n", + " nuc13 3.836400e+07 3.801797e+07\n", + " nuc4 3.865563e+07 4.564352e+07" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.cookie.unique()" + "kernels = pd.pivot_table(\n", + " throughput,\n", + " values='mean',\n", + " index=['Access Point', 'Client'],\n", + " columns='Kernel',\n", + "# aggfunc=np.mean,\n", + ")\n", + "kernel_comparison = kernels['4.14.5-041405-generic'] / kernels['3.18.87-031887-generic']\n", + "kernels" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 18.000000\n", + "mean 1.171766\n", + "std 0.232810\n", + "min 0.822022\n", + "25% 1.000395\n", + "50% 1.153253\n", + "75% 1.317671\n", + "max 1.680760\n", + "dtype: float64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(kernel_comparison)\n", + "kernel_comparison.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Short measurements" + "Looking and standard deviations" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['ap', 'bits per second', 'bytes', 'channel', 'client', 'connection',\n", - " 'cookie', 'end', 'file', 'mode', 'omitted', 'packets', 'phy',\n", - " 'retransmits', 'seconds', 'server', 'server bits per second',\n", - " 'server bytes', 'server end', 'server jitter ms', 'server lost packets',\n", - " 'server lost percent', 'server omitted', 'server packets',\n", - " 'server seconds', 'server socket', 'server start', 'snd cwnd', 'socket',\n", - " 'sta', 'start', 'timestamp', 'title', 'traffic'],\n", - " dtype='object')\n", - "['nuc5.1513155851.815381.08418dff58f49'\n", - " 'nuc5.1513156027.391810.7fc7305300fa2'\n", - " 'nuc5.1513160810.607668.2060c76b68b34'\n", - " 'nuc5.1513162237.499119.170c57c617380'\n", - " 'nuc5.1513162836.439874.0f7bced95fda2'\n", - " 'nuc5.1513169120.328561.118631b97ff2a'\n", - " 'nuc4.1513169244.076307.017c99e3111ff'\n", - " 'nuc4.1513169320.311426.5e3b8f2920080'\n", - " 'nuc4.1513169464.047935.7ea5bc7a01384'\n", - " 'nuc4.1513170629.356701.63e62a8f3e86e'\n", - " 'nuc4.1513171139.495362.0a67395248e7e'\n", - " 'nuc5.1513242952.213256.27d352892e315'\n", - " 'nuc4.1513243489.278448.72eb1fe80e5ed'\n", - " 'nuc4.1513243600.651589.434c195c58816'\n", - " 'nuc5.1513243564.451288.2316a0ca3c6ef']\n" - ] + "data": { + "text/plain": [ + "Access Point Client\n", + "nuc10 nuc12 1.087123\n", + " nuc13 1.494223\n", + " nuc4 5.383737\n", + " nuc6 1.680081\n", + "nuc11 nuc13 10.434210\n", + " nuc4 4.411166\n", + "nuc12 nuc10 NaN\n", + " nuc4 2.508030\n", + " nuc6 3.149568\n", + "nuc13 nuc10 NaN\n", + " nuc11 NaN\n", + " nuc4 2.226779\n", + " nuc6 NaN\n", + "nuc4 nuc10 0.559561\n", + " nuc11 4.882347\n", + " nuc12 0.722430\n", + " nuc13 3.249520\n", + " nuc6 0.811520\n", + "nuc6 nuc10 1.422887\n", + " nuc12 0.821346\n", + " nuc13 1.580558\n", + " nuc4 10.763654\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "shorts_folder = data_folder / 'short'\n", - "short = an.get_iperf_folder(shorts_folder, recursive=False)\n", - "short.sort_values(by=['file', 'start'], inplace=True)\n", - "print(short.columns)\n", - "print(short.cookie.unique())" + "kernel_std = pd.pivot_table(\n", + " throughput,\n", + " values='std',\n", + " index=['Access Point', 'Client'],\n", + " columns='Kernel',\n", + "# aggfunc=np.mean,\n", + ")\n", + "kernel_std_comp = kernel_std['4.14.5-041405-generic'] / kernel_std['3.18.87-031887-generic']\n", + "kernel_std_comp" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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FvjPwB0nHA9+m5X1t63jkBUvTJV0vaXC+DK2zVeBTp05l06ZNAGzcuLHot+t03YSNGzdy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\n", "text/plain": [ - "" + "count 18.000000\n", + "mean 3.177152\n", + "std 3.067554\n", + "min 0.559561\n", + "25% 1.171064\n", + "50% 1.953430\n", + "75% 4.120755\n", + "max 10.763654\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" ] }, "metadata": {}, @@ -205,27 +1357,167 @@ } ], "source": [ - "fig, ax = plt.subplots()\n", - "sns.boxplot(\n", - " x='server bits per second',\n", - " y='file',\n", - " hue='traffic',\n", - " data=short,\n", - "# cut=0,\n", - ");\n", - "ax.xaxis.set_major_formatter(an.bitrate_formatter)" + "sns.boxplot(kernel_std_comp)\n", + "kernel_std_comp.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connection investigation" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Rr0UYGtTx+512ki4VlXQJnA4uXUAQEK8kE5d5mqktHJl8YDJj240FIEhb0z4/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h4v48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\nCAKL1Cpmn7nIE2m5DLS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Clientnuc10nuc11nuc12nuc13nuc4nuc6
Access Point
nuc10NaNNaN2.53143855.07240529.01202080.202956
nuc11NaNNaNNaN9.24024721.093300NaN
nuc12NaNNaNNaNNaN37.11182818.987303
nuc1355.7599647.166688NaNNaN13.93983138.256959
nuc428.60831417.44841228.15764614.214971NaN27.616207
nuc655.908207NaN15.20532938.01797345.643524NaN
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" + ], + "text/plain": [ + "Client nuc10 nuc11 nuc12 nuc13 nuc4 nuc6\n", + "Access Point \n", + "nuc10 NaN NaN 2.531438 55.072405 29.012020 80.202956\n", + "nuc11 NaN NaN NaN 9.240247 21.093300 NaN\n", + "nuc12 NaN NaN NaN NaN 37.111828 18.987303\n", + "nuc13 55.759964 7.166688 NaN NaN 13.939831 38.256959\n", + "nuc4 28.608314 17.448412 28.157646 14.214971 NaN 27.616207\n", + "nuc6 55.908207 NaN 15.205329 38.017973 45.643524 NaN" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "connections = pd.pivot_table(\n", + " df[df['Kernel'] == '4.14.5-041405-generic'],\n", + " values='Throughput [Mbps]',\n", + " index='Access Point',\n", + " columns='Client',\n", + " aggfunc=np.mean,\n", + ")\n", + "connections = connections * 1e-6\n", + "connections" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -233,10 +1525,8 @@ } ], "source": [ - "fig, ax = plt.subplots()\n", - "short.groupby('file')['server bits per second'].plot(legend=True)\n", - "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n", - "ax.yaxis.set_major_formatter(an.bitrate_formatter)" + "f, ax = plt.subplots(figsize=(9, 6))\n", + "sns.heatmap(connections, annot=True, linewidths=.5, ax=ax)" ] }, { @@ -263,7 +1553,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/analysis/Network Map.ipynb b/analysis/Network Map.ipynb new file mode 100644 index 0000000..a337e15 --- /dev/null +++ b/analysis/Network Map.ipynb @@ -0,0 +1,466 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
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\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"ed6bee97-af5a-4825-8ded-8ee3ecec263e\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"ed6bee97-af5a-4825-8ded-8ee3ecec263e\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'ed6bee97-af5a-4825-8ded-8ee3ecec263e' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"ed6bee97-af5a-4825-8ded-8ee3ecec263e\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Global imports\n", + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "from matplotlib import rc\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import analysis as an\n", + "from pathlib import Path\n", + "\n", + "project_folder = Path(an.__file__).parent.parent.resolve()\n", + "\n", + "from bokeh.io import push_notebook, show, output_notebook\n", + "output_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available data paths:\n" + ] + }, + { + "data": { + "text/plain": [ + "[PosixPath('/home/chwalisz/Code/twist/experiments/walker/experiment/data/2018-01-11-182755'),\n", + " PosixPath('/home/chwalisz/Code/twist/experiments/walker/experiment/data/2018-01-11-225043')]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_folder = project_folder / 'experiment' / 'data'\n", + "print('Available data paths:')\n", + "[x for x in sorted(data_folder.iterdir()) if x.is_dir()]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df_list = [\n", + " an.get_iperf_folder(data_folder / '2018-01-11-182755', recursive=True),\n", + " an.get_iperf_folder(data_folder / '2018-01-11-225043', recursive=True),\n", + "]\n", + "df = pd.concat(df_list)\n", + "# df.groupby(['Kernel', 'Access Point', 'Client'])['Throughput [Mbps]'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + "import twistmap\n", + "\n", + "G = nx.DiGraph()\n", + "G.add_nodes_from(twistmap.node_positions.keys())\n", + "means = df.groupby(['Access Point', 'Client'])['Throughput [Mbps]'].mean().to_dict()\n", + "for key in means:\n", + " G.add_edge(key[0], key[1], {'weight': means[key]})" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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"application/vnd.bokehjs_exec.v0+json": { + "id": "f08f8dda-1a23-4307-a558-8be063533532" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plot = twistmap.create_map()\n", + "twistmap.draw_graph(plot, G)\n", + "show(plot, notebook_handle=True);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/analysis/analysis.py b/analysis/analysis.py index 890fde4..afc11e2 100644 --- a/analysis/analysis.py +++ b/analysis/analysis.py @@ -9,44 +9,30 @@ def get_iperf(source: Path) -> pd.DataFrame: with source.open('r') as f: raw_data = json.load(f) - raw_data_frame = [x['streams'][0] for x in raw_data['intervals']] - if not raw_data_frame: - raise ValueError('Missing data') - df = pd.DataFrame(raw_data_frame) - try: - raw_data_server = [x['streams'][0] - for x in raw_data['server_output_json']['intervals']] - except KeyError: + intervals = [x['streams'][0] for x in raw_data['intervals']] + if not intervals: raise ValueError('Missing data') - df_server = pd.DataFrame(raw_data_server) - - df_server.columns = ['server_' + x for x in df_server.columns] - result = pd.concat([df, df_server], axis=1) - # df_server = df_server.set_index('start') - - server = (raw_data['server_output_json'] - ['start']['system_info']).split(' ')[1] - client = raw_data['start']['system_info'].split(' ')[1] - conn = sorted([client, server]) - result['cookie'] = raw_data['start']['cookie'] - result['timestamp'] = raw_data['start']['timestamp']['time'] - result['server'] = server - result['client'] = client - result['connection'] = ' '.join(conn) - result['traffic'] = raw_data['start']['test_start']['protocol'] - try: - result['title'] = raw_data['title'] - key_values = raw_data['title'].split(',') - if len(key_values) > 2: - for kv in key_values: - kv = kv.split(':') - result[kv[0]] = kv[1] - except KeyError: - pass - result['mode'] = 'Access Point' if conn[0] == server else 'Client' + + result = pd.DataFrame(intervals) + + result['Client'] = raw_data['title'].split(' ')[3] + result['Access Point'] = raw_data['title'].split(' ')[1] + result['Kernel'] = raw_data['start']['system_info'].split(' ')[2] + + result['Timestamp'] = raw_data['start']['timestamp']['time'] + result['System Info'] = raw_data['start']['system_info'] + result['Protocol'] = raw_data['start']['test_start']['protocol'] + + result['Connection'] = ['{0[0]}\n{0[1]}'.format(sorted(elem)) + for elem in zip(result['Access Point'], result['Client'])] + result['file'] = source.stem + result.columns = [x.replace('_', ' ') for x in result.columns] + result = result.rename(columns={ + 'bits per second': 'Throughput [Mbps]', + }) return result @@ -71,9 +57,7 @@ def get_iperf_folder(source: Path, recursive: bool = False) -> pd.DataFrame: def bitrate(x, pos): 'The two args are the value and tick position' - if x >= 1e6: - return '{:1.0f}M'.format(x * 1e-6) - return '{:1.0f}K'.format(x * 1e-3) + return '{:1.0f}'.format(x * 1e-6) bitrate_formatter = FuncFormatter(bitrate) diff --git a/analysis/twistmap/__init__.py b/analysis/twistmap/__init__.py new file mode 100644 index 0000000..a9e9b10 --- /dev/null +++ b/analysis/twistmap/__init__.py @@ -0,0 +1,94 @@ +# import numpy as np + +import networkx as nx +import pandas as pd + +from bokeh.models import BoxSelectTool +from bokeh.models import Circle +from bokeh.models import HoverTool +from bokeh.models import MultiLine +from bokeh.models import TapTool +from bokeh.models.graphs import EdgesAndLinkedNodes +from bokeh.models.graphs import NodesAndLinkedEdges +from bokeh.models.graphs import from_networkx +from bokeh.palettes import Spectral4 +from bokeh.plotting import figure +from pathlib import Path + +CURR_PATH = Path(__file__).parent + +background = { + 'name': 'twistmap/tub-hft-3.og.png', + 'origin_x': -6.59, + 'origin_y': -2.97, + 'x': 3862, + 'y': 2243, + 'resolution': 0.01, +} + +nodes = pd.read_csv(CURR_PATH / 'wifi_nodes.csv') +nodes['floor'] = nodes['room'].str[2].astype(int) +nodes = nodes[(nodes['floor'] == 3) & (nodes['status'] == 'on')] +nodes = nodes[nodes['platform'] == 'nuc'] + +node_positions = nodes.set_index('node_id')[['x', 'y']].to_dict('index') +for key in node_positions: + pos = node_positions[key] + node_positions[key] = (pos['x'], pos['y']) + + +def create_map(): + p = figure( + x_range=(-1, 32), y_range=(-1, 16), + plot_width=320, plot_height=160, + toolbar_location='above', + title='TWIST 3rd floor', + sizing_mode='scale_width', + # output_backend="webgl", + ) + p.xaxis.axis_label = 'X [m]' + p.yaxis.axis_label = 'Y [m]' + + p.image_url( + url=[background['name']], + x=background['origin_x'], + y=background['origin_y'], + w=background['x'] * background['resolution'], + h=background['y'] * background['resolution'], + anchor='bottom_left', + ) + return p + + +def draw_graph(plot, G): + plot.add_tools( + HoverTool(tooltips=None), + TapTool(), + BoxSelectTool()) + graph = from_networkx(G, + nx.spring_layout, + pos=node_positions, + fixed=node_positions.keys()) + + graph.node_renderer.glyph = Circle( + size=15, + fill_color=Spectral4[0]) + graph.node_renderer.selection_glyph = Circle( + size=15, + fill_color=Spectral4[2]) + graph.node_renderer.hover_glyph = Circle( + size=15, + fill_color=Spectral4[1]) + + graph.edge_renderer.glyph = MultiLine( + line_color="#CCCCCC", line_alpha=0.8, line_width=5) + graph.edge_renderer.selection_glyph = MultiLine( + line_color=Spectral4[2], line_width=5) + graph.edge_renderer.hover_glyph = MultiLine( + line_color=Spectral4[1], line_width=5) + + graph.selection_policy = NodesAndLinkedEdges() + graph.inspection_policy = EdgesAndLinkedNodes() + + plot.renderers.append(graph) + return plot diff --git a/analysis/twistmap/tub-hft-3.og.png b/analysis/twistmap/tub-hft-3.og.png new file mode 100644 index 0000000..203ef5e Binary files /dev/null and b/analysis/twistmap/tub-hft-3.og.png differ diff --git a/analysis/twistmap/wifi_nodes.csv b/analysis/twistmap/wifi_nodes.csv new file mode 100644 index 0000000..3d9fff0 --- /dev/null +++ b/analysis/twistmap/wifi_nodes.csv @@ -0,0 +1,22 @@ +node_id,platform,x,y,z,room,mac,ip,status +tplink01,tplink,12.36,3.02,11.42,FT326,a0:f3:c1:a8:76:e8,192.168.10.101,on +tplink03,tplink,13,0.7,7,FT224,a0:f3:c1:a8:76:58,192.168.10.103,on +tplink05,tplink,11,14.9,11,FT336,a0:f3:c1:49:36:44,192.168.10.105,on +tplink06,tplink,11,14.8,7,FT234,a0:f3:c1:49:36:da,192.168.10.106,on +tplink08,tplink,31,0.7,7,FT226,64:70:02:3e:aa:10,192.168.10.108,on +tplink09,tplink,2,0.7,7,FT222,64:70:02:3e:aa:d8,192.168.10.109,on +tplink10,tplink,4,14.8,7,FT235,64:70:02:3e:aa:ee,192.168.10.110,on +tplink11,tplink,21.5,14.8,7,FT231,64:70:02:3e:9f:62,192.168.10.111,on +tplink15,tplink,21.5,14.9,11,FT334,64:66:b3:c2:52:f8,192.168.10.115,on +tplink20,tplink,1.8,0.7,10.79,FT323,64:66:b3:c6:e6:cc,192.168.10.120,on +tplink22,tplink,29,0.7,11,FT330,64:66:b3:6F:8d:58,192.168.10.122,on +tplink23,tplink,2,14.9,11,FT338,64:66:b3:6f:91:28,192.168.10.123,on +nuc4,nuc,14.3,0.7,10.79,FT327,b8:ae:ed:75:29:00,,on +nuc6,nuc,16.74,15.2,10.79,FT335,b8:ae:ed:75:15:87,,on +nuc10,nuc,17.39,0.7,10.79,FT328,b8:ae:ed:75:bb:0c,,on +nuc11,nuc,4.98,0.7,10.79,FT324,b8:ae:ed:75:21:e4,,on +nuc12,nuc,20.47,0.7,10.79,FT329,b8:ae:ed:75:21:9a,,on +nuc13,nuc,8.08,15.2,10.79,FT337,b8:ae:ed:75:21:e5,,on +tbot-unit01,twistbot,31,6,6,FT226,,,on +tbot-mint01,twistbot,31,8,6,FT226,,,on + diff --git a/data/2018-01-17-144113/nuc10-nuc12.json b/data/2018-01-17-144113/nuc10-nuc12.json new file mode 100644 index 0000000..7a2b038 --- /dev/null +++ b/data/2018-01-17-144113/nuc10-nuc12.json @@ -0,0 +1,12069 @@ +{ + "title": "AP nuc10 STA nuc12 using phy 03:00", + "start": { + "connected": [{ + "socket": 4, + "local_host": "10.1.1.178", + "local_port": 51859, + "remote_host": "10.1.1.1", + "remote_port": 5201 + }], + "version": "iperf 3.0.11", + "system_info": "Linux nuc12 4.14.5-041405-generic #201712101332 SMP Sun Dec 10 13:33:10 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n", + "timestamp": { + "time": "Wed, 17 Jan 2018 14:42:17 GMT", + "timesecs": 1516200137 + }, + "connecting_to": { + "host": "10.1.1.1", + "port": 5201 + }, + "cookie": "nuc12.1516200137.114831.37801fa3457d", + "test_start": { + "protocol": "UDP", + "num_streams": 1, + "blksize": 8192, + "omit": 0, + "duration": 600, + "bytes": 0, + "blocks": 0, + "reverse": 0 + } + }, + "intervals": [{ + "streams": [{ + "socket": 4, + "start": 0, + "end": 1.00012, + "seconds": 1.00012, + "bytes": 147456, + 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b/data/2018-01-17-144113/nuc11-nuc13.json new file mode 100644 index 0000000..d8a5fe6 --- /dev/null +++ b/data/2018-01-17-144113/nuc11-nuc13.json @@ -0,0 +1,12069 @@ +{ + "title": "AP nuc11 STA nuc13 using phy 03:00", + "start": { + "connected": [{ + "socket": 4, + "local_host": "10.1.1.90", + "local_port": 37068, + "remote_host": "10.1.1.1", + "remote_port": 5201 + }], + "version": "iperf 3.0.11", + "system_info": "Linux nuc13 4.14.5-041405-generic #201712101332 SMP Sun Dec 10 13:33:10 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n", + "timestamp": { + "time": "Wed, 17 Jan 2018 16:25:32 GMT", + "timesecs": 1516206332 + }, + "connecting_to": { + "host": "10.1.1.1", + "port": 5201 + }, + "cookie": "nuc13.1516206332.039088.7b7558250852", + "test_start": { + "protocol": "UDP", + "num_streams": 1, + "blksize": 8192, + "omit": 0, + "duration": 600, + "bytes": 0, + "blocks": 0, + "reverse": 0 + } + }, + "intervals": [{ + "streams": [{ + "socket": 4, + "start": 0, + "end": 1.00014, + "seconds": 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b/data/2018-01-17-144113/nuc11-nuc6.json @@ -0,0 +1,12 @@ +{ + "title": "AP nuc11 STA nuc6 using phy 03:00", + "start": { + "connected": [], + "version": "iperf 3.0.11", + "system_info": "Linux nuc6 4.14.5-041405-generic #201712101332 SMP Sun Dec 10 13:33:10 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n" + }, + "intervals": [], + "end": { + }, + "error": "error - unable to connect to server: No route to host" +} diff --git a/data/2018-01-17-144113/nuc12-nuc10.json b/data/2018-01-17-144113/nuc12-nuc10.json new file mode 100644 index 0000000..12640e0 --- /dev/null +++ b/data/2018-01-17-144113/nuc12-nuc10.json @@ -0,0 +1,12 @@ +{ + "title": "AP nuc12 STA nuc10 using phy 03:00", + "start": { + "connected": [], + "version": "iperf 3.0.11", + "system_info": "Linux nuc10 4.14.5-041405-generic #201712101332 SMP Sun Dec 10 13:33:10 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n" + }, + "intervals": [], + "end": { + }, + "error": "error - unable to connect to server: No route to host" +} diff --git a/data/2018-01-17-144113/nuc12-nuc4.json b/data/2018-01-17-144113/nuc12-nuc4.json new file mode 100644 index 0000000..3c94acf --- /dev/null +++ b/data/2018-01-17-144113/nuc12-nuc4.json @@ -0,0 +1,12069 @@ +{ + "title": "AP nuc12 STA nuc4 using phy 03:00", + "start": { + "connected": [{ + "socket": 4, + "local_host": "10.1.1.84", + "local_port": 33344, + "remote_host": "10.1.1.1", + "remote_port": 5201 + }], + "version": "iperf 3.0.11", + "system_info": "Linux nuc4 4.14.5-041405-generic #201712101332 SMP Sun Dec 10 13:33:10 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n", + "timestamp": { + "time": "Wed, 17 Jan 2018 15:12:59 GMT", + "timesecs": 1516201979 + }, + "connecting_to": { + "host": "10.1.1.1", + "port": 5201 + }, + "cookie": "nuc4.1516201979.299071.0cd8d2f318b9f", + "test_start": { + "protocol": "UDP", + "num_streams": 1, + "blksize": 8192, + "omit": 0, + "duration": 600, + "bytes": 0, + "blocks": 0, + "reverse": 0 + } + }, + "intervals": [{ + "streams": [{ + "socket": 4, + "start": 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a/data/2018-01-17-144113/nuc13-nuc6.json b/data/2018-01-17-144113/nuc13-nuc6.json new file mode 100644 index 0000000..5e927c7 --- /dev/null +++ b/data/2018-01-17-144113/nuc13-nuc6.json @@ -0,0 +1,12069 @@ +{ + "title": "AP nuc13 STA nuc6 using phy 03:00", + "start": { + "connected": [{ + "socket": 4, + "local_host": "10.1.1.174", + "local_port": 56799, + "remote_host": "10.1.1.1", + "remote_port": 5201 + }], + "version": "iperf 3.0.11", + "system_info": "Linux nuc6 4.14.5-041405-generic #201712101332 SMP Sun Dec 10 13:33:10 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n", + "timestamp": { + "time": "Wed, 17 Jan 2018 16:56:13 GMT", + "timesecs": 1516208173 + }, + "connecting_to": { + "host": "10.1.1.1", + "port": 5201 + }, + "cookie": "nuc6.1516208173.156214.6cefffa06b5ef", + "test_start": { + "protocol": "UDP", + "num_streams": 1, + "blksize": 8192, + "omit": 0, + "duration": 600, + "bytes": 0, + "blocks": 0, + "reverse": 0 + } + }, + "intervals": [{ + "streams": [{ + "socket": 4, + 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+} diff --git a/data/2018-01-17-144113/nuc6-nuc10.json b/data/2018-01-17-144113/nuc6-nuc10.json new file mode 100644 index 0000000..34c58c8 --- /dev/null +++ b/data/2018-01-17-144113/nuc6-nuc10.json @@ -0,0 +1,12069 @@ +{ + "title": "AP nuc6 STA nuc10 using phy 03:00", + "start": { + "connected": [{ + "socket": 4, + "local_host": "10.1.1.102", + "local_port": 57648, + "remote_host": "10.1.1.1", + "remote_port": 5201 + }], + "version": "iperf 3.0.11", + "system_info": "Linux nuc10 4.14.5-041405-generic #201712101332 SMP Sun Dec 10 13:33:10 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n", + "timestamp": { + "time": "Wed, 17 Jan 2018 15:44:01 GMT", + "timesecs": 1516203841 + }, + "connecting_to": { + "host": "10.1.1.1", + "port": 5201 + }, + "cookie": "nuc10.1516203840.996350.33fd2dad1e22", + "test_start": { + "protocol": "UDP", + "num_streams": 1, + "blksize": 8192, + "omit": 0, + "duration": 600, + "bytes": 0, + "blocks": 0, + "reverse": 0 + } + }, + "intervals": [{ + "streams": [{ + 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3.0.11", + "system_info": "Linux nuc11 4.14.5-041405-generic #201712101332 SMP Sun Dec 10 13:33:10 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n" + }, + "intervals": [], + "end": { + }, + "error": "error - unable to connect to server: No route to host" +} diff --git a/data/2018-01-17-144113/nuc6-nuc12.json b/data/2018-01-17-144113/nuc6-nuc12.json new file mode 100644 index 0000000..1076aa3 --- /dev/null +++ b/data/2018-01-17-144113/nuc6-nuc12.json @@ -0,0 +1,12069 @@ +{ + "title": "AP nuc6 STA nuc12 using phy 03:00", + "start": { + "connected": [{ + "socket": 4, + "local_host": "10.1.1.178", + "local_port": 54955, + "remote_host": "10.1.1.1", + "remote_port": 5201 + }], + "version": "iperf 3.0.11", + "system_info": "Linux nuc12 4.14.5-041405-generic #201712101332 SMP Sun Dec 10 13:33:10 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n", + "timestamp": { + "time": "Wed, 17 Jan 2018 15:54:08 GMT", + "timesecs": 1516204448 + }, + "connecting_to": { + "host": "10.1.1.1", + "port": 5201 + }, + 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a/data/2018-01-17-192523/nuc11-nuc13.json b/data/2018-01-17-192523/nuc11-nuc13.json new file mode 100644 index 0000000..5795c87 --- /dev/null +++ b/data/2018-01-17-192523/nuc11-nuc13.json @@ -0,0 +1,12069 @@ +{ + "title": "AP nuc11 STA nuc13 using phy 03:00", + "start": { + "connected": [{ + "socket": 4, + "local_host": "10.1.1.90", + "local_port": 56496, + "remote_host": "10.1.1.1", + "remote_port": 5201 + }], + "version": "iperf 3.0.11", + "system_info": "Linux nuc13 3.18.87-031887-generic #201712091831 SMP Sat Dec 9 18:32:57 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n", + "timestamp": { + "time": "Wed, 17 Jan 2018 21:32:16 GMT", + "timesecs": 1516224736 + }, + "connecting_to": { + "host": "10.1.1.1", + "port": 5201 + }, + "cookie": "nuc13.1516224736.927199.63a496453289", + "test_start": { + "protocol": "UDP", + "num_streams": 1, + "blksize": 8192, + "omit": 0, + "duration": 600, + "bytes": 0, + "blocks": 0, + "reverse": 0 + } + }, + "intervals": [{ + "streams": [{ + "socket": 4, + 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b/data/2018-01-17-192523/nuc12-nuc6.json @@ -0,0 +1,12069 @@ +{ + "title": "AP nuc12 STA nuc6 using phy 03:00", + "start": { + "connected": [{ + "socket": 4, + "local_host": "10.1.1.174", + "local_port": 58883, + "remote_host": "10.1.1.1", + "remote_port": 5201 + }], + "version": "iperf 3.0.11", + "system_info": "Linux nuc6 3.18.87-031887-generic #201712091831 SMP Sat Dec 9 18:32:57 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux\n", + "timestamp": { + "time": "Wed, 17 Jan 2018 20:17:23 GMT", + "timesecs": 1516220243 + }, + "connecting_to": { + "host": "10.1.1.1", + "port": 5201 + }, + "cookie": "nuc6.1516220243.744263.588a2d8203fed", + "test_start": { + "protocol": "UDP", + "num_streams": 1, + "blksize": 8192, + "omit": 0, + "duration": 600, + "bytes": 0, + "blocks": 0, + "reverse": 0 + } + }, + "intervals": [{ + "streams": [{ + "socket": 4, + "start": 0, + "end": 1.00015, + "seconds": 1.00015, + "bytes": 1630208, + "bits_per_second": 1.30397e+07, + "packets": 199, + "omitted": false + }], + 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"seconds": 600, + "bytes": 2899173376, + "bits_per_second": 3.86556e+07, + "jitter_ms": 2.45964, + "lost_packets": 0, + "packets": 353902, + "lost_percent": 0 + } + }], + "sum": { + "start": 0, + "end": 600, + "seconds": 600, + "bytes": 2899173376, + "bits_per_second": 3.86556e+07, + "jitter_ms": 2.45964, + "lost_packets": 0, + "packets": 353902, + "lost_percent": 0 + }, + "cpu_utilization_percent": { + "host_total": 4.08572, + "host_user": 0.34017, + "host_system": 3.74555, + "remote_total": 4.17514, + "remote_user": 0.561065, + "remote_system": 3.61408 + } + } +} diff --git a/deployment/ansible.cfg b/deployment/ansible.cfg index d03c1fa..6428963 100644 --- a/deployment/ansible.cfg +++ b/deployment/ansible.cfg @@ -1,3 +1,8 @@ [defaults] inventory = ../node_selection/hosts +retry_files_enabled = False + +[ssh_connection] +ssh_args = -F ../.ssh.cfg +scp_if_ssh = True diff --git a/deployment/main.yml b/deployment/main.yml index 81bf0cb..f780a8d 100644 --- a/deployment/main.yml +++ b/deployment/main.yml @@ -8,26 +8,13 @@ dest: ~/exp-code delete: yes recursive: yes + use_ssh_args: yes rsync_opts: - "--no-motd" - "--exclude=.git" - "--exclude=data" - "--exclude=images" - - name: Install required packages - apt: name="{{ item }}" state=present - with_items: - - wpasupplicant - - hostapd - - tcpdump - - python3-setuptools - - python3-pip - - iperf3 - - netperf - - dtach - - htop - become: yes - - name: Set regulatory domain to DE command: iw reg set DE become: yes @@ -37,10 +24,18 @@ become: yes failed_when: false + - name: List kernels + raw: ls /boot/vmlinuz* + changed_when: false + register: kernels + - name: Check wireless devices raw: iw phy | grep Wiphy changed_when: false register: wdevs + - name: Show kernels + debug: var=kernels.stdout_lines + - name: Show wireless devices debug: var=wdevs.stdout_lines diff --git a/experiment/experiment.py b/experiment/experiment.py index cb3eaa0..cdab8ad 100755 --- a/experiment/experiment.py +++ b/experiment/experiment.py @@ -2,6 +2,7 @@ import click import measurement +import kernel import time import wifi import yaml @@ -13,7 +14,13 @@ from pprint import pprint from tqdm import tqdm -BASE_PATH = Path(__file__).parent / '..' +BASE_PATH = Path(__file__).absolute().parents[1] + +gateway = Connection( + 'api.twist.tu-berlin.de', + user='proxyuser', + port=2222, +) log = logging.getLogger('exp') @@ -24,12 +31,35 @@ def select_one(param): yield sel, cut +def get_all_nodes(user=None, limit=None): + with (BASE_PATH / 'node_selection' / 'hosts').open('r') as stream: + config = yaml.load(stream) + hosts = list(config['nuc']['hosts'].keys()) + log.info(f'Node info') + grp = [] + if limit: + hosts = [host for host in hosts if host in limit] + for host in hosts: + cnx = Connection( + host, + user=user, + gateway=gateway, + ) + wifi.info(cnx) + grp.append(cnx) + return grp + + @click.group( - context_settings=dict(help_option_names=['-h', '--help'])) + context_settings=dict(help_option_names=['-h', '--help'], obj={})) +@click.option('--user', '-u', + default=None, + help='Select user') @click.option('-v', '--verbose', count=True, help='Increase log verbosity level (up to 4)') -@click.version_option('v0.2.0') -def cli(verbose): +@click.version_option('v0.3.0') +@click.pass_context +def cli(ctx, user, verbose): level = {0: logging.WARNING, 1: logging.INFO, 2: logging.DEBUG} if verbose < 3: log.setLevel(level[verbose]) @@ -43,6 +73,8 @@ def cli(verbose): format='%(asctime)s:%(name)s:%(levelname)s:%(message)s', level=level[verbose]) + ctx.obj['user'] = user + @cli.command(short_help="Scan for networks") def scan(): @@ -67,22 +99,20 @@ def scan(): type=click.Choice(['udp', 'tcp']), default='udp', help='Choose traffic type') -def short(duration, access_point, client, traffic): - gateway = Connection( - 'api.twist.tu-berlin.de', - user='proxyuser', - port=2222, - ) - ap = Connection(access_point, gateway=gateway) - sta = Connection(client, gateway=gateway) +@click.option('--channel', + default=6, + help='Wifi channel to use') +@click.pass_context +def short(ctx, duration, access_point, client, traffic, channel): + ap = Connection(access_point, user=ctx.obj['user'], gateway=gateway) + sta = Connection(client, user=ctx.obj['user'], gateway=gateway) phy = '02:00' - channel = 11 for host in [ap, sta]: wifi.phy_clean(host) measurement.iperf_kill(host) - data_folder = Path.cwd() / 'data' / 'short' + data_folder = BASE_PATH / 'data' / 'short' if not data_folder.exists(): data_folder.mkdir(parents=True) @@ -113,17 +143,19 @@ def short(duration, access_point, client, traffic): @click.option('--duration', '-d', default=60, help='Iperf3 measurement duration') -def run(duration): - with (BASE_PATH / 'node_selection' / 'hosts').open('r') as stream: - config = yaml.load(stream) - hosts = list(config['nuc']['hosts'].keys()) +@click.option('--channel', '-c', + default=6, + help='Wifi channel to use') +@click.option('--limit', '-l', + help='Limit target hosts to comma separated list') +@click.pass_context +def run(ctx, duration, channel, limit): + if limit is not None: + limit = limit.split(",") + grp = get_all_nodes(ctx.obj['user'], limit) phy = '03:00' - log.info(f'Node info') - grp = SerialGroup(*hosts) - grp.run('uname -s -n -r') - wifi.info(grp) - data_folder = Path.cwd() / 'data' / time.strftime("%Y-%m-%d-%H%M%S") - data_folder.mkdir() + data_folder = BASE_PATH / 'data' / time.strftime("%Y-%m-%d-%H%M%S") + data_folder.mkdir(parents=True) log.info(f'Storing measurements in {data_folder}') for host in grp: wifi.phy_clean(host) @@ -134,7 +166,7 @@ def run(duration): pbar_ap.set_description(f'AP {ap.host}') # Create AP - wifi.create_ap(ap, phy=phy, ssid='exp1', channel=11) + wifi.create_ap(ap, phy=phy, ssid='exp1', channel=channel) measurement.iperf_server(ap) pbar_sta = tqdm(stations, dynamic_ncols=True) @@ -162,5 +194,18 @@ def run(duration): measurement.iperf_kill(ap) +@cli.command(short_help="Select kernel") +@click.pass_context +def select_kernel(ctx): + grp = get_all_nodes(ctx.obj['user']) + possible_kernels = kernel.kernels(grp[0]) + print('Possible kernels') + for ii, kern in enumerate(possible_kernels): + print(f'{ii}: {kern}') + value = click.prompt('Select kernel', type=int) + for node in grp: + kernel.switch(node, possible_kernels[value].release) + + if __name__ == '__main__': cli() diff --git a/experiment/kernel.py b/experiment/kernel.py new file mode 100644 index 0000000..463c678 --- /dev/null +++ b/experiment/kernel.py @@ -0,0 +1,39 @@ + +from collections import namedtuple +from fabric import Connection + +Kernel = namedtuple('Kernel', 'version release') + + +def kernels(ctx: Connection): + """returns sorted list of installed kernels + + """ + result = ctx.run('ls /boot/vmlinuz*', hide=True) + klist = [] + for release in result.stdout.split('\n'): + release = release.replace('/boot/vmlinuz-', '') + version = release.split('-')[0] + if version: + klist.append(Kernel(version, release)) + + def version_key(x: Kernel): + v = x.version.split('.') + return int(v[0]), int(v[1]), int(v[2]) + + return sorted(klist, key=version_key) + + +def switch(ctx: Connection, release: str): + """Reboot node using kernel `release` + + """ + result = ctx.run('ls /boot/vmlinuz*', hide=True) + if release not in result.stdout: + raise ValueError('Kernel {release} not found') + ctx.sudo('mv /vmlinuz /vmlinuz.old') + ctx.sudo('mv /initrd.img /initrd.img.old') + ctx.sudo(f'ln -fs boot/vmlinuz-{release} /vmlinuz') + ctx.sudo(f'ln -fs boot/initrd.img-{release} /initrd.img') + + # ctx.sudo('reboot', hide=True) diff --git a/experiment/measurement.py b/experiment/measurement.py index 0108897..42a285c 100644 --- a/experiment/measurement.py +++ b/experiment/measurement.py @@ -38,7 +38,7 @@ def iperf_client( conf = conf + f' --title "{title}"' if title else conf result = cnx.run( (f'iperf3 --client {ip} -t {duration}' - f' --json --get-server-output' + f' --json --reverse' f' {conf} {extra_args}'), hide=True, warn=True) diff --git a/templates/hostapd.conf.jn2 b/experiment/templates/hostapd.conf.jn2 similarity index 100% rename from templates/hostapd.conf.jn2 rename to experiment/templates/hostapd.conf.jn2 diff --git a/templates/wpasup.conf.jn2 b/experiment/templates/wpasup.conf.jn2 similarity index 100% rename from templates/wpasup.conf.jn2 rename to experiment/templates/wpasup.conf.jn2 diff --git a/experiment/wifi.py b/experiment/wifi.py index 2ee5c6c..f2c934d 100644 --- a/experiment/wifi.py +++ b/experiment/wifi.py @@ -4,6 +4,7 @@ from collections import namedtuple from fabric import Connection +from hashlib import sha256 from invoke.exceptions import UnexpectedExit from io import StringIO from jinja2 import Environment @@ -11,7 +12,7 @@ from pathlib import Path from typing import Optional -template_path = (Path(__file__).parent / '..' / 'templates').resolve() +template_path = (Path(__file__).parent / 'templates').resolve() jinja_env = Environment(loader=FileSystemLoader([template_path])) WiFiDev = namedtuple('WiFiDev', 'phy, interface') @@ -134,10 +135,18 @@ def info(cnx): cnx (Connection): fabric connection context """ # cnx.run('lshw -C network') - results = cnx.run('lspci -nnk | grep "Wireless" -A2', hide=True) - for connection, result in results.items(): - print(connection.host) - print(result.stdout) + cnx.run('uname -s -n -r') + cnx.run('lspci -nnk | grep "Wireless" -A2') + + +def generate_ip(cnx): + """Generates persistent IP address for node + + """ + hashval = sha256(cnx.host.encode('utf-8')).hexdigest() + ip_hash = int(hashval, 16) % 2**8 + ip = f'10.1.1.{ip_hash}' + return ip def reload(cnx): @@ -187,8 +196,9 @@ def create_ap( """ phy, interface = phy_check(cnx, phy, interface, '_ap') - result = cnx.sudo(f'pkill -f hostapd.*{phy}', warn=True) - if result: + old_wpa = cnx.sudo(f'pkill -f wpa_supplicant.*{phy}', warn=True) + old_hostapd = cnx.sudo(f'pkill -f hostapd.*{phy}', warn=True) + if old_wpa or old_hostapd: time.sleep(2) tmpl = jinja_env.get_template('hostapd.conf.jn2') @@ -204,6 +214,7 @@ def create_ap( ) # Clean up interface + cnx.sudo('rfkill unblock wifi', warn=True, hide=True) cnx.sudo(f'ip link set {interface} down') cnx.sudo(f'ip addr flush dev {interface}') cnx.sudo(f'iw dev {interface} set type managed') @@ -240,14 +251,15 @@ def connect( phy, interface = phy_check(cnx, phy, interface, '_sta') if ip is None: - ip_hash = hash(cnx.host) % 2**8 - ip = f'10.1.1.{ip_hash}' + ip = generate_ip(cnx) - result = cnx.sudo(f'pkill -f wpa_supplicant.*{phy}', warn=True) - if result: + old_wpa = cnx.sudo(f'pkill -f wpa_supplicant.*{phy}', warn=True) + old_hostapd = cnx.sudo(f'pkill -f hostapd.*{phy}', warn=True) + if old_wpa or old_hostapd: time.sleep(2) # Clean up interface + cnx.sudo('rfkill unblock wifi', warn=True, hide=True) cnx.sudo(f'ip link set {interface} down') cnx.sudo(f'ip addr flush dev {interface}') cnx.sudo(f'iw dev {interface} set type managed') diff --git a/images/add_elements/twist/package-installs.yaml b/images/add_elements/twist/package-installs.yaml deleted file mode 100644 index d7a8df8..0000000 --- a/images/add_elements/twist/package-installs.yaml +++ /dev/null @@ -1,2 +0,0 @@ -python3: -python3-pip: diff --git a/images/add_elements/ubuntu-squashfs/README.rst b/images/add_elements/ubuntu-squashfs/README.rst deleted file mode 100644 index 87a9804..0000000 --- a/images/add_elements/ubuntu-squashfs/README.rst +++ /dev/null @@ -1,18 +0,0 @@ -====== -ubuntu -====== - -Use Ubuntu cloud images as the baseline for built disk images. This element -is widely based on the stock `ubuntu` element, but uses the squashfs-packaged -cloud images provided by ubuntu. This allows to build diskimages based on more -recent Ubuntu, which is no longer distributed as rootfs tarball since xenial. - -Overrides: - - * To use a non-default URL for downloading base Ubuntu cloud images, - use the environment variable ``DIB_CLOUD_IMAGES`` - * To download a non-default release of Ubuntu cloud images, use the - environment variable ``DIB_RELEASE``. This element will export the - ``DIB_RELEASE`` variable. - -.. element_deps:: diff --git a/images/add_elements/ubuntu-squashfs/element-deps b/images/add_elements/ubuntu-squashfs/element-deps deleted file mode 100644 index 3089055..0000000 --- a/images/add_elements/ubuntu-squashfs/element-deps +++ /dev/null @@ -1,5 +0,0 @@ -cache-url -cloud-init-datasources -dkms -dpkg -ubuntu-common diff --git a/images/add_elements/ubuntu-squashfs/element-provides b/images/add_elements/ubuntu-squashfs/element-provides deleted file mode 100644 index a72e049..0000000 --- a/images/add_elements/ubuntu-squashfs/element-provides +++ /dev/null @@ -1 +0,0 @@ -operating-system diff --git a/images/add_elements/ubuntu-squashfs/environment.d/10-ubuntu-distro-name.bash b/images/add_elements/ubuntu-squashfs/environment.d/10-ubuntu-distro-name.bash deleted file mode 100644 index d89cee2..0000000 --- a/images/add_elements/ubuntu-squashfs/environment.d/10-ubuntu-distro-name.bash +++ /dev/null @@ -1,2 +0,0 @@ -export DISTRO_NAME=ubuntu -export DIB_RELEASE=${DIB_RELEASE:-artful} diff --git a/images/add_elements/ubuntu-squashfs/environment.d/99-cloud-init-datasources.bash b/images/add_elements/ubuntu-squashfs/environment.d/99-cloud-init-datasources.bash deleted file mode 100644 index bd25409..0000000 --- a/images/add_elements/ubuntu-squashfs/environment.d/99-cloud-init-datasources.bash +++ /dev/null @@ -1,4 +0,0 @@ -# NOTE(adam_g): Until (LP: #1316475) is resolved in Ubuntu, default to only -# allowing the Ec2 data source from being queried on first boot, unless -# specified otherwise. -export DIB_CLOUD_INIT_DATASOURCES=${DIB_CLOUD_INIT_DATASOURCES:-"Ec2"} diff --git a/images/add_elements/ubuntu-squashfs/install.d/99-autoremove b/images/add_elements/ubuntu-squashfs/install.d/99-autoremove deleted file mode 100755 index e04006d..0000000 --- a/images/add_elements/ubuntu-squashfs/install.d/99-autoremove +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -if [ ${DIB_DEBUG_TRACE:-1} -gt 0 ]; then - set -x -fi -set -eu -set -o pipefail - -apt-get -y autoremove diff --git a/images/add_elements/ubuntu-squashfs/package-installs.yaml b/images/add_elements/ubuntu-squashfs/package-installs.yaml deleted file mode 100644 index 83f64fc..0000000 --- a/images/add_elements/ubuntu-squashfs/package-installs.yaml +++ /dev/null @@ -1 +0,0 @@ -linux-image-generic: diff --git a/images/add_elements/ubuntu-squashfs/pre-install.d/00-remove-apt-xapian-index b/images/add_elements/ubuntu-squashfs/pre-install.d/00-remove-apt-xapian-index deleted file mode 100755 index 9d04ccd..0000000 --- a/images/add_elements/ubuntu-squashfs/pre-install.d/00-remove-apt-xapian-index +++ /dev/null @@ -1,11 +0,0 @@ -#!/bin/bash - -if [ ${DIB_DEBUG_TRACE:-0} -gt 0 ]; then - set -x -fi -set -eu -set -o pipefail - -# This package is broken and causes real issues on update: -# https://bugs.launchpad.net/ubuntu/+source/apt-xapian-index/+bug/1227420 -apt-get --yes remove apt-xapian-index || : diff --git a/images/add_elements/ubuntu-squashfs/pre-install.d/00-remove-grub b/images/add_elements/ubuntu-squashfs/pre-install.d/00-remove-grub deleted file mode 100755 index 094a3f7..0000000 --- a/images/add_elements/ubuntu-squashfs/pre-install.d/00-remove-grub +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash -# The grub post-kernel install hook will barf if the block device can't be -# found (as happens in a chroot). -# Temporarily remove grub, to avoid that confusion. - -if [ ${DIB_DEBUG_TRACE:-0} -gt 0 ]; then - set -x -fi -set -eu -set -o pipefail - -if dpkg-query -W grub-pc; then - apt-get -y remove grub-pc -fi - -if dpkg-query -W grub2-common; then - apt-get -y remove grub2-common -fi diff --git a/images/add_elements/ubuntu-squashfs/pre-install.d/01-set-ubuntu-mirror b/images/add_elements/ubuntu-squashfs/pre-install.d/01-set-ubuntu-mirror deleted file mode 100755 index cc07297..0000000 --- a/images/add_elements/ubuntu-squashfs/pre-install.d/01-set-ubuntu-mirror +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/bash - -if [ ${DIB_DEBUG_TRACE:-0} -gt 0 ]; then - set -x -fi -set -eu -set -o pipefail - -DIB_DISTRIBUTION_MIRROR=${DIB_DISTRIBUTION_MIRROR:-} - -[ -n "$DIB_DISTRIBUTION_MIRROR" ] || exit 0 - -sudo sed -ie "s&http://\(archive\|security\).ubuntu.com/ubuntu&$DIB_DISTRIBUTION_MIRROR&" \ - /etc/apt/sources.list diff --git a/images/add_elements/ubuntu-squashfs/root.d/10-cache-ubuntu-tarball b/images/add_elements/ubuntu-squashfs/root.d/10-cache-ubuntu-tarball deleted file mode 100755 index 22b10e6..0000000 --- a/images/add_elements/ubuntu-squashfs/root.d/10-cache-ubuntu-tarball +++ /dev/null @@ -1,61 +0,0 @@ -#!/bin/bash -# These are useful, or at worst not harmful, for all images we build. - -if [ ${DIB_DEBUG_TRACE:-1} -gt 0 ]; then - set -x -fi -set -eu -set -o pipefail - -[ -n "$ARCH" ] -[ -n "$TARGET_ROOT" ] - -shopt -s extglob - -DIB_CLOUD_IMAGES=${DIB_CLOUD_IMAGES:-http://cloud-images.ubuntu.com} -DIB_RELEASE=${DIB_RELEASE:-xenial} -BASE_IMAGE_FILE=${BASE_IMAGE_FILE:-$DIB_RELEASE-server-cloudimg-$ARCH.squashfs} -SHA256SUMS=${SHA256SUMS:-https://${DIB_CLOUD_IMAGES##http?(s)://}/$DIB_RELEASE/current/SHA256SUMS} -CACHED_FILE=$DIB_IMAGE_CACHE/$BASE_IMAGE_FILE -CACHED_FILE_LOCK=$DIB_IMAGE_CACHE/$BASE_IMAGE_FILE.lock -CACHED_SUMS=$DIB_IMAGE_CACHE/SHA256SUMS.ubuntu.$DIB_RELEASE.$ARCH - -function get_ubuntu_tarball() { - if [ -n "$DIB_OFFLINE" -a -f "$CACHED_FILE" ] ; then - echo "Not checking freshness of cached $CACHED_FILE." - else - echo "Fetching Base Image" - $TMP_HOOKS_PATH/bin/cache-url $SHA256SUMS $CACHED_SUMS - $TMP_HOOKS_PATH/bin/cache-url \ - $DIB_CLOUD_IMAGES/$DIB_RELEASE/current/$BASE_IMAGE_FILE $CACHED_FILE - pushd $DIB_IMAGE_CACHE - if ! grep "$BASE_IMAGE_FILE" $CACHED_SUMS | sha256sum --check - ; then - # It is likely that an upstream http(s) proxy has given us a skewed - # result - either a cached SHA file or a cached image. Use cache-busting - # to get (as long as caches are compliant...) fresh files. - # Try the sha256sum first, just in case that is the stale one (avoiding - # downloading the larger image), and then if the sums still fail retry - # the image. - $TMP_HOOKS_PATH/bin/cache-url -f $SHA256SUMS $CACHED_SUMS - if ! grep "$BASE_IMAGE_FILE" $CACHED_SUMS | sha256sum --check - ; then - $TMP_HOOKS_PATH/bin/cache-url -f \ - $DIB_CLOUD_IMAGES/$DIB_RELEASE/current/$BASE_IMAGE_FILE $CACHED_FILE - grep "$BASE_IMAGE_FILE" $CACHED_SUMS | sha256sum --check - - fi - fi - popd - fi - # Extract the base image (use --numeric-owner to avoid UID/GID mismatch between - # image tarball and host OS e.g. when building Ubuntu image on an openSUSE host) - sudo unsquashfs -f -d $TARGET_ROOT $DIB_IMAGE_CACHE/$BASE_IMAGE_FILE -} - -( - echo "Getting $CACHED_FILE_LOCK: $(date)" - # Wait up to 20 minutes for another process to download - if ! flock -w 1200 9 ; then - echo "Did not get $CACHED_FILE_LOCK: $(date)" - exit 1 - fi - get_ubuntu_tarball -) 9> $CACHED_FILE_LOCK diff --git a/images/deployment/action_plugins/bootos.py b/images/deployment/action_plugins/bootos.py new file mode 100644 index 0000000..12d6c1e --- /dev/null +++ b/images/deployment/action_plugins/bootos.py @@ -0,0 +1,171 @@ +import logging +import multiprocessing +import socket +import time + +from ansible.plugins.action import ActionBase +from contextlib import closing +from fabric import Connection + + +logger = logging.getLogger('bootos') +logger.setLevel(logging.DEBUG) + + +def check_port(host, port): + logger.debug('checking ssh port for {}:{}'.format(host, port)) + with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: + if sock.connect_ex((host, int(port))) == 0: + return True + else: + logger.debug('ssh port not open: {}@{}'.format(host, port)) + return False + + +def get_status(host, user): + host_string = '{:s}@{:s}'.format(user, host) + logger.debug('get status for {}'.format(host_string)) + res = run_cmd(host, user, 'grep -Fxq "True" /etc/baseosflag') + if(res == 0): + return 'base' + else: + return 'experiment' + + +def wait_for_host(host, user, timeout, delay=0): + host_string = '{:s}@{:s}'.format(user, host) + logger.debug("wait for host {}".format(host_string)) + time.sleep(delay) + t_start = time.time() + while time.time() < (t_start + timeout): + try: + res = run_cmd(host, user, '/bin/true') + if(res == 0): + logger.debug("success login to {}".format(host_string)) + return + except Exception: + logger.debug("failed login to {}".format(host_string)) + + logger.debug('waiting {:.2f}s for {}'.format( + t_start + timeout - time.time(), host_string)) + time.sleep(5) + raise Exception + + +class FabricProcess(multiprocessing.Process): + def __init__(self, user, host, cmd, privileged): + super(FabricProcess, self).__init__() + self.ctx = Connection('{:s}@{:s}'.format(user, host)) + self.operation = self.ctx.sudo if ( + user != 'root' and privileged) else self.ctx.run + self.cmd = cmd + self.return_code = multiprocessing.Value('i', -1) + + def run(self): + res = self.operation(self.cmd, shell=False, hide=True, warn=True) + self.ctx.close() + self.return_code.value = res.return_code + + +def run_cmd(host, user, cmd, privileged=False): + logger.debug("run cmd \"{}\" on {}@{}".format( + cmd, user, host)) + + port = 22 + command_timeout = 10 + + if not check_port(host, port): + raise Exception + + logger.debug("ssh port open on {}@{}".format(user, host)) + logger.debug("spawning fabric process..") + + fp = FabricProcess(user, host, cmd, privileged) + fp.start() + + t_start = time.time() + while time.time() < (t_start + command_timeout): + if(fp.exitcode is not None): + break + time.sleep(1) + + if(fp.exitcode is None): + fp.terminate() + logger.debug("timeout run cmd \"{}\" on {}@{}".format( + cmd, user, host)) + raise TimeoutError() + + logger.debug("passed run cmd \"{}\" on {}@{}. Exit code: {}".format( + cmd, user, host, fp.return_code.value)) + return fp.return_code.value + + +class ActionModule(ActionBase): + def run(self, tmp=None, task_vars=None): + if task_vars is None: + task_vars = dict() + + result = super(ActionModule, self).run(tmp, task_vars) + args = self._task.args.copy() + + # key_filename = task_vars['ansible_ssh_private_key_file'] + # logger.debug("using private key from {:s}".format(key_filename)) + + if 'experimentuser' not in args: + args['experimentuser'] = 'ansible_user' + + if not args['reboot']: + status = get_status( + task_vars['ansible_host'], task_vars['ansible_user']) + if(status == args['image']): + result['changed'] = False + return result + + if args['image'] == 'base': + self.boot_base( + task_vars['ansible_host'], task_vars['ansible_user']) + result['changed'] = True + + elif args['image'] == 'experiment': + res = self.boot_experiment( + task_vars['ansible_host'], + task_vars['ansible_user'], + args['experimentuser'], + args, + task_vars) + result.update(res) + + return result + + def boot_experiment( + self, host, user, experimentuser, args, task_vars): + logger.debug('boot {} to experiment'.format(host)) + + status = get_status(host, user) + if status != 'base': + self.boot_base(host, user) + + logger.debug('run kexec on {}'.format(host)) + res = self._execute_module( + module_args=args, task_vars=task_vars) + + wait_for_host(host, experimentuser, 240, delay=10) + status = get_status(host, experimentuser) + if status == 'experiment': + return res + else: + raise Exception + + def boot_base(self, host, user): + logger.debug('boot {} to base'.format(host)) + status = get_status(host, user) + if status != 'base': + ret = run_cmd(host, user, 'reboot', True) + if(ret != 0): + raise Exception + + wait_for_host(host, user, 240, delay=10) + if get_status(host, user) == 'base': + return + else: + raise Exception diff --git a/images/deployment/ansible.cfg b/images/deployment/ansible.cfg new file mode 100644 index 0000000..6428963 --- /dev/null +++ b/images/deployment/ansible.cfg @@ -0,0 +1,8 @@ +[defaults] + +inventory = ../node_selection/hosts +retry_files_enabled = False + +[ssh_connection] +ssh_args = -F ../.ssh.cfg +scp_if_ssh = True diff --git a/images/deployment/deploy.yml b/images/deployment/deploy.yml new file mode 100644 index 0000000..427f74d --- /dev/null +++ b/images/deployment/deploy.yml @@ -0,0 +1,115 @@ +--- +- name: Start twist-managed + hosts: nuc + vars: + experiment_user: "{{ lookup('env','USERNAME') }}" + image_path: 'image.tgz' + become: yes + tasks: + + - name: unmount target partition + mount: + name: "{{ rootfs_dir }}" + src: "{{ rootfs_dev }}" + fstype: ext4 + state: unmounted + + - name: clear target partition + shell: "mkfs.ext4 -q -F {{ rootfs_dev }}" + tags: + - image + + - name: mount target partition + mount: + name: "{{ rootfs_dir }}" + src: "{{ rootfs_dev }}" + fstype: ext4 + state: mounted + + - name: copy image + copy: + src: "{{ image_path }}" + dest: "{{ rootfs_dir }}/image.tar.gz" + tags: + - image + + - name: unpack rootfs + shell: tar xpzf "{{ rootfs_dir }}/image.tar.gz" -C "{{ rootfs_dir }}" + tags: + - image + + - name: Copy ssh host keys + copy: + src: "/etc/ssh/{{ item }}" + dest: "{{ rootfs_dir }}/etc/ssh/{{ item }}" + remote_src: yes + force: yes + with_items: + - ssh_host_dsa_key + - ssh_host_dsa_key.pub + - ssh_host_ecdsa_key + - ssh_host_ecdsa_key.pub + - ssh_host_ed25519_key + - ssh_host_ed25519_key.pub + - ssh_host_rsa_key + - ssh_host_rsa_key.pub + + - name: Change hostname + lineinfile: + dest: "{{ rootfs_dir }}/etc/hostname" + regexp: '\W*' + line: "{{ inventory_hostname }}" + backrefs: yes + + - name: Add udev rule + template: + src: 70-network-ifname.rules + dest: "{{ rootfs_dir }}/etc/udev/rules.d" + owner: root + group: root + mode: 0644 + + - name: Add interface configuration + lineinfile: + dest: "{{ rootfs_dir }}/etc/network/interfaces" + line: "{{ item }}" + create: yes + with_items: + - "auto eth0" + - "iface eth0 inet dhcp" + + - name: Ensure /proc and /sys are in place + file: + path: "{{ rootfs_dir }}{{ item }}" + state: directory + mode: 0551 + with_items: + - "/proc" + - "/sys" + + - name: Add root to fstab + lineinfile: + dest: "{{ rootfs_dir }}/etc/fstab" + line: "/dev/vg_user/lv_root / ext4 defaults 0 1" + regexp: '^\S+ /' + + - name: deploy controller keys + lineinfile: + create: yes + dest: "{{ rootfs_dir }}/root/.ssh/authorized_keys" + line: "{{ lookup('file', lookup('env','HOME') + '/.ssh/id_rsa.pub') }}" + + - name: Load experiment OS + bootos: + image: experiment + reboot: yes + kparams: "{{ kparams }}" + experimentuser: root + tags: + - bootos + + - include_tasks: postinstall.yml + vars: + ansible_ssh_user: "root" + tags: + - postinstall diff --git a/images/deployment/library/bootos b/images/deployment/library/bootos new file mode 100755 index 0000000..2941c8c --- /dev/null +++ b/images/deployment/library/bootos @@ -0,0 +1,139 @@ +#!/usr/bin/python +# -*- coding: utf-8 -*- +# +# Author: Kai Geissdoerfer +# +# Bootos module for ansible +# + +#---- Documentation Start ----------------------------------------------------# +DOCUMENTATION = ''' +--- +version_added: "2.3.1.0" +module: bootos +short_description: ansible interface for booting images +description: + - This is part of an action plugin allowing to boot boot experiment/base images from ansible. +options: + kparams: + description: + - Kexec parameters: kernel image, kernel command line, initrd image, experiment rootfs path. See bootparam(7). + required: no + default: no + reboot: + description: + - Force reboot to specified image + required: no + default: True + image: + description: + - Target image to be booted. One of base or experiment + required: yes + default: no + experimentuser: + description: + - Username for login to experiment image + required: no + default: ansible_ssh_user +notes: + - This module primarly serves as a wrapper around the kexec command to allow clean exit of ansible +requirements: + - kexec-tools + - fabric + +author: Kai Geissdoerfer +''' + +EXAMPLES = ''' +- name: Load experiment + bootos: + image: experiment + reboot: yes + kparams: "{{ kparams }}" + become: yes +''' +#---- Imports ---------------------------# + +from ansible.module_utils.basic import * +import os +import subprocess +import time +import resource + + +#---- Module Start ------------------------------------------------------------# + +def main(): + + module = AnsibleModule( + argument_spec=dict( + image=dict(required=True, choices=['base', 'experiment']), + reboot=dict(default=True, type='bool'), + experimentuser=dict(required=False, type='str'), + kparams=dict(required=False, type='dict') + ), + ) + + # extract parameters from dictionary + experiment_rfs = module.params['kparams']['experiment_rfs'] + kernel = module.params['kparams']['kernel'] + commandline = module.params['kparams']['commandline'] + image = module.params['image'] + reboot = module.params['reboot'] + if 'initrd' in module.params['kparams'].keys(): + initrd = module.params['kparams']['initrd'] + else: + initrd = None + + changed = False + + try: + kexec_load(experiment_rfs, kernel, commandline, initrd) + except Exception as e: + module.fail_json(msg="KEXEC_LOAD: Unexpected error: %s message: %s" %(sys.exc_info()[0],e)) + + kexec_exec() + module.exit_json(changed=True) + + +def kexec_load(rfs, kernel, cmdline, initrd=None): + call_list = ['/sbin/kexec', '-l', '/'.join((rfs, kernel))] + + if initrd: + call_list.append('--initrd={:s}/{:s}'.format(rfs, initrd)) + + call_list.append('--command-line={:s}'.format(cmdline)) + + r = subprocess.call(call_list) + if r != 0: + raise Exception('kexec returned {:s}'.format(r)) + + +def kexec_exec(): + pid = os.fork() + if(pid == 0): + os.setsid() + pid = os.fork() + if(pid == 0): + pass + else: + os._exit(0) + else: + return() + + maxfd = resource.getrlimit(resource.RLIMIT_NOFILE)[1] + if (maxfd == resource.RLIM_INFINITY): + maxfd = MAXFD + + for fd in range(0, maxfd): + try: + os.close(fd) + except OSError: + pass + + time.sleep(1) + FNULL = open(os.devnull, 'w') + subprocess.Popen("/sbin/kexec -e", stdout=FNULL, shell=True, stderr=FNULL) + + +main() diff --git a/images/deployment/postinstall.yml b/images/deployment/postinstall.yml new file mode 100644 index 0000000..c38230d --- /dev/null +++ b/images/deployment/postinstall.yml @@ -0,0 +1,29 @@ +- name: Install python + raw: test -e /usr/bin/python || (apt -y update && apt install -y python) + +- name: Install packages + apt: + name: "{{ item }}" + update_cache: yes + with_items: + - sudo + - kexec-tools + +- name: Add users + user: + name: "{{ experiment_user }}" + shell: /bin/bash + groups: sudo + append: yes + +- name: Set authorized key for experiment user copying it from current user + authorized_key: + user: "{{ experiment_user }}" + state: present + key: "{{ lookup('file', lookup('env','HOME') + '/.ssh/id_rsa.pub') }}" + +- name: Allow sudo without password + lineinfile: + create: yes + dest: "/etc/sudoers" + line: "%sudo ALL=(ALL) NOPASSWD: ALL" diff --git a/images/deployment/templates/70-network-ifname.rules b/images/deployment/templates/70-network-ifname.rules new file mode 100644 index 0000000..8f6573d --- /dev/null +++ b/images/deployment/templates/70-network-ifname.rules @@ -0,0 +1 @@ +SUBSYSTEM=="net", ACTION=="add", ATTR{address}=="{{ ansible_default_ipv4.macaddress }}", NAME="eth0" diff --git a/images/add_elements/mainline-kernel/README.rst b/images/preparation/add_elements/mainline-kernel/README.rst similarity index 100% rename from images/add_elements/mainline-kernel/README.rst rename to images/preparation/add_elements/mainline-kernel/README.rst diff --git a/images/add_elements/mainline-kernel/bin/download-kernels b/images/preparation/add_elements/mainline-kernel/bin/download-kernels similarity index 100% rename from images/add_elements/mainline-kernel/bin/download-kernels rename to images/preparation/add_elements/mainline-kernel/bin/download-kernels diff --git a/images/add_elements/mainline-kernel/element-deps b/images/preparation/add_elements/mainline-kernel/element-deps similarity index 100% rename from images/add_elements/mainline-kernel/element-deps rename to images/preparation/add_elements/mainline-kernel/element-deps diff --git a/images/add_elements/mainline-kernel/environment.d/10-kernel-versions.bash b/images/preparation/add_elements/mainline-kernel/environment.d/10-kernel-versions.bash similarity index 100% rename from images/add_elements/mainline-kernel/environment.d/10-kernel-versions.bash rename to images/preparation/add_elements/mainline-kernel/environment.d/10-kernel-versions.bash diff --git a/images/add_elements/mainline-kernel/install.d/90-install-kernels b/images/preparation/add_elements/mainline-kernel/install.d/90-install-kernels similarity index 100% rename from images/add_elements/mainline-kernel/install.d/90-install-kernels rename to images/preparation/add_elements/mainline-kernel/install.d/90-install-kernels diff --git a/images/add_elements/mainline-kernel/package-installs.yaml b/images/preparation/add_elements/mainline-kernel/package-installs.yaml similarity index 100% rename from images/add_elements/mainline-kernel/package-installs.yaml rename to images/preparation/add_elements/mainline-kernel/package-installs.yaml diff --git a/images/add_elements/twist/element-deps b/images/preparation/add_elements/twist/element-deps similarity index 100% rename from images/add_elements/twist/element-deps rename to images/preparation/add_elements/twist/element-deps diff --git a/images/preparation/add_elements/twist/package-installs.yaml b/images/preparation/add_elements/twist/package-installs.yaml new file mode 100644 index 0000000..7fb290a --- /dev/null +++ b/images/preparation/add_elements/twist/package-installs.yaml @@ -0,0 +1,10 @@ +dtach: +hostapd: +htop: +iperf3: +netperf: +python3-pip: +python3-setuptools: +python3: +tcpdump: +wpasupplicant: diff --git a/images/awss3.py b/images/preparation/awss3.py similarity index 100% rename from images/awss3.py rename to images/preparation/awss3.py diff --git a/images/prepare.py b/images/preparation/prepare.py similarity index 64% rename from images/prepare.py rename to images/preparation/prepare.py index 28faae0..96ccc57 100755 --- a/images/prepare.py +++ b/images/preparation/prepare.py @@ -3,13 +3,12 @@ import pathlib import jinja2 import yaml -import sys import tempfile import invoke import click import awss3 as cloudstorage -BASE_PATH = pathlib.Path(__file__).absolute().parents[1] +BASE_PATH = pathlib.Path(__file__).absolute().parents[2] def __build(diskimage, release, kernel): @@ -17,9 +16,9 @@ def __build(diskimage, release, kernel): print(f'Creating "{diskimage}"..') sources_tmpl = jinja2.Environment( - loader=jinja2.FileSystemLoader( - str(pathlib.Path(__file__).parents[0]))).get_template( - 'sources.list.jn2') + loader=jinja2.FileSystemLoader( + str(pathlib.Path(__file__).parents[0]))).get_template( + 'sources.list.jn2') sources_rendered = sources_tmpl.render({'release': release}) @@ -30,19 +29,21 @@ def __build(diskimage, release, kernel): print(f'sources.list rendered to {sources_filename}') cmd = ( - "sudo -E bash -c '" - "disk-image-create ubuntu-squashfs twist mainline-kernel" - f" -t tgz -o {diskimage}'" - ) + "sudo -E bash -c '" + "disk-image-create ubuntu twist mainline-kernel" + f" -t tgz -o {diskimage}'" + ) try: - invoke.run(cmd, - env={ - 'DIB_KERNEL_VERSIONS': ','.join(kernel), - 'DIB_APT_SOURCES': sources_filename, - 'DIB_RELEASE': release, - 'ELEMENTS_PATH': pathlib.Path(__file__) - .absolute().parents[0] / 'add_elements' - }) + invoke.run( + cmd, + env={ + 'DIB_KERNEL_VERSIONS': ','.join(kernel), + 'DIB_APT_SOURCES': sources_filename, + 'DIB_RELEASE': release, + 'ELEMENTS_PATH': pathlib.Path(__file__) + .absolute().parents[0] / 'add_elements', + } + ) finally: os.unlink(sources_filename) @@ -64,15 +65,15 @@ def __render_rspec(url): for node_type in node_spec: for node_name in node_spec[node_type]['hosts']: node = { - 'name': node_name, - 'type': node_type, - 'disk_image': url - } + 'name': node_name, + 'type': node_type, + 'disk_image': url + } nodes.append(node) rspec = jinja2.Environment( - loader=jinja2.FileSystemLoader( - str(BASE_PATH / 'node_selection'))).get_template('rspec.jn2') + loader=jinja2.FileSystemLoader( + str(BASE_PATH / 'node_selection'))).get_template('rspec.jn2') output = rspec.render({'nodes': nodes}) @@ -86,10 +87,14 @@ def __render_rspec(url): @click.group(invoke_without_command=True) @click.option('--diskimage', '-d', default='image.tgz', help='Filename for diskimage') -@click.option('--release', '-r', default='artful', help='Ubuntu release codename') -@click.option('--kernel', '-k', multiple=True, default='4.14.5', - help='Version number of mainline kernel to bake into image') -@click.version_option('v0.2.0') +@click.option( + '--release', '-r', default='xenial', help='Ubuntu release codename') +@click.option( + '--kernel', '-k', + multiple=True, + default=['3.18.87', '4.14.5', '4.15', '4.11.4'], + help='Version number of mainline kernel to bake into image') +@click.version_option('v0.3.0') @click.pass_context def cli(ctx, diskimage, release, kernel): if ctx.invoked_subcommand is None: diff --git a/images/sources.list.jn2 b/images/preparation/sources.list.jn2 similarity index 100% rename from images/sources.list.jn2 rename to images/preparation/sources.list.jn2 diff --git a/node_selection/group_vars/nuc b/node_selection/group_vars/nuc new file mode 100644 index 0000000..8de525b --- /dev/null +++ b/node_selection/group_vars/nuc @@ -0,0 +1,7 @@ +rootfs_dir: /media/lv_default +rootfs_dev: /dev/vg_images/default +kparams: + experiment_rfs: '{{ rootfs_dir }}' + kernel: 'vmlinuz' + initrd: 'initrd.img' + commandline: 'root={{ rootfs_dev }} ro crashkernel=384M-:128M net.ifnames=0 biosdevname=0 cloud-init=disabled' diff --git a/node_selection/rendered.rspec b/node_selection/rendered.rspec index 781b897..a668c88 100644 --- a/node_selection/rendered.rspec +++ b/node_selection/rendered.rspec @@ -1,52 +1,52 @@ - + - - - + + + - + - - - + + + - + - - - + + + - + - - - + + + - + - - - + + + - + - - - + + + - - \ No newline at end of file + + diff --git a/results/throughput_kernels.pdf b/results/throughput_kernels.pdf new file mode 100644 index 0000000..a52b46f Binary files /dev/null and b/results/throughput_kernels.pdf differ