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path: root/testcases/vIMS/CI
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2015-10-16Bugfix: venv pathboucherv1-3/+1
2015-10-15Bugfix: venv pathboucherv1-1/+1
2015-10-08Bigfix : function "download_and_add_image_on_glance"boucherv1-0/+2
2015-10-05vIMS deployment and cleanupboucherv3-0/+369
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.. This work is licensed under a Creative Commons Attribution 4.0 International
.. License.
.. http://creativecommons.org/licenses/by/4.0
.. (c) OPNFV, Ericsson AB and others.

=========
Yardstick
=========

Overview
========

Yardstick is a framework to test non functional characteristics of an NFV
Infrastructure as perceived by an application.

An application is a set of virtual machines deployed using the orchestrator of
the target cloud, for example OpenStack Heat.

Yardstick measures a certain service performance but can also validate the
service performance to be within a certain level of agreement.

Yardstick is _not_ about testing OpenStack functionality (tempest) or
benchmarking OpenStack APIs (rally).

Concepts
========

Benchmark - assess the relative performance of something

Benchmark configuration file - describes a single test case in yaml format

Context
- The set of cloud resources used by a benchmark (scenario)
– Is a simplified Heat template (context is converted into a Heat template)

Data
- Output produced by running a benchmark, written to a file in json format

Runner
- Logic that determines how the test is run
– For example number of iterations, input value stepping, duration etc

Scenario
- Type/class of measurement for example Ping, Pktgen, (Iperf, LmBench, ...)

SLA
- Some limit to be verified (specific to scenario), for example max_latency
– Associated action to automatically take: assert, monitor etc

Architecture
============

Yardstick is a command line tool written in python inspired by Rally. Yardstick
is intended to run on a computer with access and credentials to a cloud. The
test case is described in a configuration file given as an argument.

How it works: the benchmark task configuration file is parsed and converted into
an internal model. The context part of the model is converted into a Heat
template and deployed into a stack. Each scenario is run using a runner, either
serially or in parallel. Each runner runs in its own subprocess executing
commands in a VM using SSH. The output of each command is written as json
records to a file.

Install
=======

TBD

Run
===

TBD

Custom Image
============

pktgen test requires a ubuntu server cloud image
TBD

Development Environment
=======================

Example setup known to work for development and test:
- Development environment: Ubuntu14.04, eclipse, virtual environment
- Cloud: Mirantis OpenStack 6.0 deployed using Virtualbox

Install dependencies:
$ sudo apt-get install python-virtualenv python-dev libffi-dev libssl-dev libxml2-dev libxslt1-dev

Create a virtual environment:
$ virtualenv ~/yardstick_venv
$ source ~/yardstick_venv/bin/activate
$ easy_install -U setuptools
$ python setup.py develop