QTIP - Platform Performance Benchmarking¶

QTIP is the project for Platform Performance Benchmarking in OPNFV. It aims to provide user a simple indicator for performance, simple but supported by comprehensive testing data and transparent calculation formula.

QTIP Release Notes¶

Danube¶

This document provides the release notes for Danube of QTIP.

Version history
Important notes
Summary
Release Data
Known Limitations, Issues and Workarounds
- Limitations
- Known issues
Test Result

Version history ¶

Date	Ver.	Author	Comment
2017-03-30	Danube 1.0	Yujun Zhang
2017-05-04	Danube 2.0	Yujun Zhang

Important notes ¶

QTIP is totally reworked in Danube release. The legacy benchmarks released in Brahmaputra (compute, network and storage) are deprecated.

Summary ¶

QTIP Danube release introduces QPI, a.k.a. QTIP Performance Index, which is calculated from metrics collected in performance tests.

A PoC of compute performance benchmark plan is provided as a sample use case.

Available benchmark plans can be listed, shown and executed from command line or over API.

Release Data ¶

Project	QTIP
Repo/commit-ID	qtip/danube.2.0
Release designation	Danube bug fix release
Release date	2017-05-04
Purpose of the delivery	OPNFV quality assurance

Version change ¶

New in Danube 2.0 ¶

Bug fix in regex of ssl

Module version changes ¶

The following Python packages are used in this release:

ansible==2.1.2.0
click==6.7
connexion==1.1.5
Jinja2==2.9.5
numpy==1.12.1
paramiko==2.1.2
pbr==2.0.0
prettytable==0.7.2
six==1.10.0
PyYAML==3.12

It is considered as a baseline for future releases.

Reason for version ¶

Features additions ¶

Compute QPI (QTIP Performance Index) specification and benchmarking plan
Command line interface
API server

Framework evolution ¶

The following components are implemented and integrated

Native runner
File loader
Ansible driver
Logfile collector
Grep parser
Console reporter

See JIRA for full change log

Deliverables ¶

Software ¶

QTIP Docker image (tag: danube.2.0)
QTIP Docker image (tag: danube.1.0)

Documentation ¶

Known Limitations, Issues and Workarounds ¶

Limitations ¶

The compute benchmark plan is hard coded in native runner
Baseline for Compute QPI is not created yet, therefore scores are not available

Known issues ¶

QTIP-230 - logger warns about socket /dev/log when running in container

Test Result ¶

QTIP has undergone QA test runs with the following results:

TEST-SUITES	Results:
qtip-verify-danube	94/94 passed
qtip-os-nosdn-kvm-ha-zte-pod3-daily-danube	Successful
qtip-os-nosdn-nofeature-ha-zte-pod3-daily-danube	Successful
qtip-os-odl_l2-nofeature-ha-zte-pod1-daily-danube	Successful

Brahmaputra¶

NOTE: The release note for OPNFV Brahmaputra is missing. This is a copy of the README.

QTIP Benchmark Suite¶

QTIP is a benchmarking suite intended to benchmark the following components of the OPNFV Platform:

Computing components
Networking components
Storage components

The efforts in QTIP are mostly focused on identifying

Benchmarks to run
Test cases in which these benchmarks to run
Automation of suite to run benchmarks within different test cases
Collection of test results

QTIP Framework can now be called: (qtip.py).

The Framework can run 5 computing benchmarks:

Dhrystone
Whetstone
RamBandwidth
SSL
nDPI

These benchmarks can be run in 2 test cases:

VM vs Baremetal
Baremetal vs Baremetal

Instructions to run the script:

Download and source the OpenStack adminrc file for the deployment on which you want to create the VM for benchmarking
run python qtip.py -s {SUITE} -b {BENCHMARK}
run python qtip.py -h for more help
list of benchmarks can be found in the qtip/test_cases directory
SUITE refers to compute, network or storage

Requirements:

Ansible 1.9.2
Python 2.7
PyYAML

Configuring Test Cases:

Test cases can be found within the test_cases directory. For each Test case, a Config.yaml file contains the details for the machines upon which the benchmarks would run. Edit the IP and the Password fields within the files for the machines on which the benchmark is to run. A robust framework that would allow to include more tests would be included within the future.

Jump Host requirements:

The following packages should be installed on the server from which you intend to run QTIP.

1: Heat Client 2: Glance Client 3: Nova Client 4: Neutron Client 5: wget 6: PyYaml

Networking

1: The Host Machines/compute nodes to be benchmarked should have public/access network 2: The Host Machines/compute nodes should allow Password Login

QTIP support for Foreman

{TBA}

QTIP Installation & Configuration¶

Configuration¶

QTIP currently supports by using a Docker image. Detailed steps about setting up QTIP can be found below.

To use QTIP you should have access to an OpenStack environment, with at least Nova, Neutron, Glance, Keystone and Heat installed. Add a brief introduction to configure OPNFV with this specific installer

Installing QTIP using Docker¶

QTIP docker image¶

QTIP has a Docker images on the docker hub. Pulling opnfv/qtip docker image from docker hub:

docker pull opnfv/qtip:stable

Verify that opnfv/qtip has been downloaded. It should be listed as an image by running the following command.

docker images

Run and enter the docker instance¶

1. If you want to run benchmarks:

envs="INSTALLER_TYPE={INSTALLER_TYPE} -e INSTALLER_IP={INSTALLER_IP}"
docker run -p [HOST_IP:]<HOST_PORT>:5000 --name qtip -id -e $envs opnfv/qtip
docker exec -i -t qtip /bin/bash

INSTALLER_TYPE should be one of OPNFV installer, e.g. apex, compass, daisy, fuel and joid. Currenty, QTIP only supports installer fuel.

INSTALLER_IP is the ip address of the installer that can be accessed by QTIP.

2. If you do not want to run any benchmarks:

docker run --name qtip -id opnfv/qtip
docker exec -i -t qtip /bin/bash

Now you are in the container and QTIP can be found in the /repos/qtip and can be navigated to using the following command.

cd repos/qtip

Install from source code¶

You may try out the latest version of QTIP by installing from source code. It is recommended to run it under Python virtualenv so it won’t screw system libraries.

Run the following commands:

git clone https://git.opnfv.org/qtip && cd qtip
virtualenv .venv && source .venv/bin/activate
pip install -e .

Use the following command to exit virtualenv:

deactivate

Re-enter the virtualenv with:

cd <qtip-directory>
source .venv/bin/activate

Environment configuration¶

Hardware configuration¶

QTIP does not have specific hardware requriements, and it can runs over any OPNFV installer.

Jumphost configuration¶

Installer Docker on Jumphost, which is used for running QTIP image.

You can refer to these links:

Ubuntu: https://docs.docker.com/engine/installation/linux/ubuntu/

Centos: https://docs.docker.com/engine/installation/linux/centos/

Platform components configuration¶

Describe the configuration of each component in the installer.

QTIP User Guide¶

Overview¶

QTIP is the project for Platform Performance Benchmarking in OPNFV. It aims to provide user a simple indicator for performance, simple but supported by comprehensive testing data and transparent calculation formula.

QTIP introduces a concept called QPI, a.k.a. QTIP Performance Index, which aims to be a TRUE indicator of performance. TRUE reflects the core value of QPI in four aspects

Transparent: being an open source project, user can inspect all details behind QPI, e.g. formulas, metrics, raw data
Reliable: the integrity of QPI will be guaranteed by traceability in each step back to raw test result
Understandable: QPI is broke down into section scores, and workload scores in report to help user to understand
Extensible: users may create their own QPI by composing the existed metrics in QTIP or extend new metrics

Benchmarks¶

The builtin benchmarks of QTIP are located in <package_root>/benchmarks folder

QPI: specifications about how an QPI is calculated and sources of metrics
metric: performance metrics referred in QPI, currently it is categorized by performance testing tools
plan: executable benchmarking plan which collects metrics and calculate QPI

Run with Ansible¶

QTIP benchmarking tasks are built upon Ansible playbooks and roles. If you are familiar with Ansible, it is possible to run it with ansible-playbook command.

Create workspace¶

There is a playbook in tests/integration used for creating a new workspace for QTIP benchmarking:

cd qtip/tests/integration
ansible-playbook workspace-create.yml

You will be prompted for required information for the Pod under test:

(qtip) ➜  integration git:(master) ✗ ansible-playbook workspace-create.yml
name of the pod under test (used in reporting) [qtip-pod]:
scenario deployed in the pod: [default]:
installer type of the pod (apex|fuel|other) [fuel]:
master host/vm of the installer (accessible by `ssh <hostname>`): f5
workspace name (new directory will be created) [workspace]:

PLAY [localhost] ***************************************************************

TASK [qtip-workspace : generating workspace] ***********************************

PLAY RECAP *********************************************************************
localhost                  : ok=0    changed=0    unreachable=0    failed=0

You may hit Enter to accept default value

NOTE: if this playbook is moved to other directory, configuration in ansible.cfg needs to be updated accordingly. The ansible roles from QTIP, i.e. <path_of_qtip>/resources/ansible_roles must be added to roles_path in Ansible configuration file. For example:

roles_path = ../../resources/ansible_roles

Executing benchmark¶

Before executing the setup playbook, make sure ~/.ssh/config has been configured properly so that you can login the master node “directly”. Skip next section, if you can login with ssh <master-host> from localhost,

SSH access to master node¶

It is common that the master node is behind some jump host. In this case, ssh option ProxyCommand and ssh-agent shall be required.

Assume that you need to login to deploy server, then login to the master node from there. An example configuration is as following:

Host fuel-deploy
  HostName 172.50.0.250
  User root

Host fuel-master
  HostName 192.168.122.63
  User root
  ProxyCommand ssh -o 'ForwardAgent yes' apex-deploy 'ssh-add && nc %h %p'

If several jumps are required to reach the master node, we may chain the jump hosts like below:

Host jumphost
  HostName 10.62.105.31
  User zte
  Port 22

Host fuel-deploy
  HostName 172.50.0.250
  User root
  ProxyJump jumphost

Host fuel-master
  HostName 192.168.122.63
  User root
  ProxyCommand ssh -o 'ForwardAgent yes' apex-deploy 'ssh-add && nc %h %p'

NOTE: ProxyJump is equivalent to the long ProxyCommand option, but it is only available since OpenSSH 7.3

Setup testing environment¶

Run the setup playbook to generate ansible inventory of system under test by querying the slave nodes from the installer master:

cd workspace
ansible-playbook setup.yml

Currently, QTIP supports automatic discovery from apex and fuel

It will update the hosts and ssh.cfg

Run the tests¶

It is important to note that ssh-agent is required to run the tests. It must started correctly to ensure execution. The way to do it is

eval $(ssh-agent)

One can also kill the process as

eval $(ssh-agent -k)

Run the benchmarks with the following command:

ansible-playbook run.yml

CAVEAT: QTIP will install required packages in system under test.

Inspect the results¶

The test results and calculated output are stored in results:

current/
    node-2/
        arithmetic/
            metric.json
            report
            unixbench.log
        dpi/
        ...
    node-4/
    ...
    qtip-pod-qpi.json
qtip-pod-20170425-1710/
qtip-pod-20170425-1914/
...

The folders are named as <pod_name>-<start_time>/ and the results are organized by hosts under test. Inside each host, the test data are organized by metrics as defined in QPI specification.

For each metrics, it usually includes the following content

log file generated by the performance testing tool
metrics collected from the log files
reported rendered with the metrics collected

Teardown the test environment¶

QTIP will create temporary files for testing in system under test. Execute the teardown playbook to clean it up:

ansible-playbook teardown.yml

CLI User Manual¶

QTIP consists of a number of benchmarking tools or metrics, grouped under QPI’s. QPI’s map to the different components of a NFVI ecosystem, such as compute, network and storage. Depending on the type of application, a user may group them under plans.

QTIP CLI provides interface to all of the above the components. A help page provides a list of all the commands along with a short description.

qtip [-h|--help]

Typically a complete plan is executed at the target environment. QTIP defaults to a number of sample plans. A list of all the available plans can be viewed

qtip plan list

In order to view the details about a specific plan.

qtip plan show <plan_name>

where plan_name is one of those listed from the previous command.

To execute a complete plan

qtip plan run <plan_name> -p <path_to_result_directory>

QTIP does not limit result storage at a specific directory. Instead a user may specify his own result storage as above. An important thing to remember is to provide absolute path of result directory.

mkdir result
qtip plan run <plan_name> -p $PWD/result

Similarly, the same commands can be used for the other two components making up the plans, i.e QPI’s and metrics. For example, in order to run a single metric

qtip metric run <metric_name> -p $PWD/result

The same can be applied for a QPI.

QTIP also provides the utility to view benchmarking results on the console. One just need to provide to where the results are stored. Extending the example above

qtip report show <metric_name> -p $PWD/result

Debug option helps identify the error by providing a detailed traceback. It can be enabled as

qtip [-d|--debug] plan run <plan_name>

API User Manual¶

QTIP consists of a number of benchmarking tools or metrics, grouped under QPI’s. QPI’s map to the different components of an NFVI ecosystem, such as compute, network and storage. Depending on the type of application, a user may group them under plans.

QTIP API provides a RESTful interface to all of the above components. User can retrieve list of plans, QPIs and metrics and their individual information.

Running¶

After installing QTIP. API server can be run using command qtip-api on the local machine.

All the resources and their corresponding operation details can be seen at /v1.0/ui.

The whole API specification in json format can be seen at /v1.0/swagger.json.

The data models are given below:

Plan

Metric

QPI

Plan:

{
  "name": <plan name>,
  "description": <plan profile>,
  "info": <{plan info}>,
  "config": <{plan configuration}>,
  "QPIs": <[list of qpis]>,
},

Metric:

{
  "name": <metric name>,
  "description": <metric description>,
  "links": <[links with metric information]>,
  "workloads": <[cpu workloads(single_cpu, multi_cpu]>,
},

QPI:

{
  "name": <qpi name>,
  "description": <qpi description>,
  "formula": <formula>,
  "sections": <[list of sections with different metrics and formulaes]>,
}

The API can be described as follows

Plans:

Method Path Description

GET /v1.0/plans Get the list of of all plans

GET /v1.0/plans/{name} Get details of the specified plan

Metrics:

Method Path Description

GET /v1.0/metrics Get the list of all metrics

GET /v1.0/metrics/{name} Get details of specified metric

QPIs:

Method Path Description

GET /v1.0/qpis Get the list of all QPIs

GET /v1.0/qpis/{name} Get details of specified QPI

Note:: running API with connexion cli does not require base path (/v1.0/) in url

Compute Performance Benchmarking¶

The compute QPI aims to benchmark the compute components of an OPNFV platform. Such components include, the CPU performance, the memory performance.

The compute QPI consists of both synthetic and application specific benchmarks to test compute components.

All the compute benchmarks could be run in the scenario: On Baremetal Machines provisioned by an OPNFV installer (Host machines)

Note: The Compute benchmank constains relatively old benchmarks such as dhrystone and whetstone. The suite would be updated for better benchmarks such as Linbench for the OPNFV E release.

Getting started¶

Notice: All descriptions are based on QTIP container.

Inventory File¶

QTIP uses Ansible to trigger benchmark test. Ansible uses an inventory file to determine what hosts to work against. QTIP can automatically generate a inventory file via OPNFV installer. Users also can write their own inventory infomation into /home/opnfv/qtip/hosts. This file is just a text file containing a list of host IP addresses. For example:

[hosts]
10.20.0.11
10.20.0.12

QTIP key Pair¶

QTIP use a SSH key pair to connect to remote hosts. When users execute compute QPI, QTIP will generate a key pair named QtipKey under /home/opnfv/qtip/ and pass public key to remote hosts.

If environment variable CI_DEBUG is set to true, users should delete it by manual. If CI_DEBUG is not set or set to false, QTIP will delete the key from remote hosts before the execution ends. Please make sure the key deleted from remote hosts or it can introduce a security flaw.

Commands¶

In a QTIP container, you can run compute QPI by using QTIP CLI:

mkdir result
qtip plan run <plan_name> -p $PWD/result

QTIP generates results in the $PWD/result directory are listed down under the timestamp name.

you can get more details from userguide/cli.rst.

Metrics¶

The benchmarks include:

Dhrystone 2.1¶

Dhrystone is a synthetic benchmark for measuring CPU performance. It uses integer calculations to evaluate CPU capabilities. Both Single CPU performance is measured along multi-cpu performance.

Dhrystone, however, is a dated benchmark and has some short comings. Written in C, it is a small program that doesn’t test the CPU memory subsystem. Additionally, dhrystone results could be modified by optimizing the compiler and insome cases hardware configuration.

References: http://www.eembc.org/techlit/datasheets/dhrystone_wp.pdf

Whetstone¶

Whetstone is a synthetic benchmark to measure CPU floating point operation performance. Both Single CPU performance is measured along multi-cpu performance.

Like Dhrystone, Whetstone is a dated benchmark and has short comings.

References:

http://www.netlib.org/benchmark/whetstone.c

OpenSSL Speed¶

OpenSSL Speed can be used to benchmark compute performance of a machine. In QTIP, two OpenSSL Speed benchmarks are incorporated:

RSA signatunes/sec signed by a machine
AES 128-bit encryption throughput for a machine for cipher block sizes

References:

https://www.openssl.org/docs/manmaster/apps/speed.html

RAMSpeed¶

RAMSpeed is used to measure a machine’s memory perfomace. The problem(array)size is large enough to ensure Cache Misses so that the main machine memory is used.

INTmem and FLOATmem benchmarks are executed in 4 different scenarios:

Copy: a(i)=b(i)
Add: a(i)=b(i)+c(i)
Scale: a(i)=b(i)*d
Tniad: a(i)=b(i)+c(i)*d

INTmem uses integers in these four benchmarks whereas FLOATmem uses floating points for these benchmarks.

References:

http://alasir.com/software/ramspeed/

https://www.ibm.com/developerworks/community/wikis/home?lang=en#!/wiki/W51a7ffcf4dfd_4b40_9d82_446ebc23c550/page/Untangling+memory+access+measurements

DPI¶

nDPI is a modified variant of OpenDPI, Open source Deep packet Inspection, that is maintained by ntop. An example application called pcapreader has been developed and is available for use along nDPI.

A sample .pcap file is passed to the pcapreader application. nDPI classifies traffic in the pcap file into different categories based on string matching. The pcapreader application provides a throughput number for the rate at which traffic was classified, indicating a machine’s computational performance. The results are run 10 times and an average is taken for the obtained number.

nDPI may provide non consistent results and was added to Brahmaputra for experimental purposes

References:

http://www.ntop.org/products/deep-packet-inspection/ndpi/

http://www.ntop.org/wp-content/uploads/2013/12/nDPI_QuickStartGuide.pdf

QTIP Developer Guide¶

Overview¶

QTIP uses Python as primary programming language and build the framework from the following packages

Module	Package
api	Connexion - API first applications with OpenAPI/Swagger and Flask
cli	Click - the “Command Line Interface Creation Kit”
template	Jinja2 - a full featured template engine for Python
docs	sphinx - a tool that makes it easy to create intelligent and beautiful documentation
testing	pytest - a mature full-featured Python testing tool that helps you write better programs

Source Code¶

The structure of repository is based on the recommended sample in The Hitchhiker’s Guide to Python

Path	Content
`./benchmarks/`	builtin benchmark assets including plan, QPI and metrics
`./contrib/`	independent project/plugin/code contributed to QTIP
`./docker/`	configuration for building Docker image for QTIP deployment
`./docs/`	release notes, user and developer documentation, design proposals
`./legacy/`	legacy obsoleted code that is unmaintained but kept for reference
`./opt/`	optional component, e.g. scripts to setup infrastructure services for QTIP
`./qtip/`	the actual package
`./tests/`	package functional and unit tests
`./third-party/`	third part included in QTIP project

Coding Style¶

QTIP follows OpenStack Style Guidelines for source code and commit message.

Specially, it is recommended to link each patch set with a JIRA issue. Put:

JIRA: QTIP-n

in commit message to create an automatic link.

Testing¶

All testing related code are stored in ./tests/

Path	Content
`./tests/data/`	data fixtures for testing
`./tests/unit/`	unit test for each module, follow the same layout as ./qtip/
`./conftest.py`	pytest configuration in project scope

tox is used to automate the testing tasks

cd <project_root>
pip install tox
tox

The test cases are written in pytest. You may run it selectively with

pytest tests/unit/reporter

Branching¶

Stable branches are created when features are frozen for next release. According to OPNFV release milestone description, stable branch window is open on MS6 and closed on MS7.

Contact gerrit admin <opnfv-helpdesk@rt.linuxfoundation.org> to create branch for project.
Setup qtip jobs and docker jobs for stable branch in releng
Follow instructions for stable branch.

NOTE: we do NOT create branches for feature development as in the popular GitHub Flow

Releasing¶

Tag Deliverable and write release note

Git repository¶

Follow the example in Git Tagging Instructions for Danube to tag the source code:

git fetch gerrit
git checkout stable/<release-name>
git tag -am "<release-version>" <release-version>
git push gerrit <release-version>

Docker image¶

Login OPNFV Jenkins
Go to the `qtip-docker-build-push-<release>`_ and click “Build With Parameters”
Fill in RELEASE_VERSION with version number not including release name, e.g. 1.0
Trigger a manual build

Python Package¶

QTIP is also available as a Python Package. It is hosted on the Python Package Index(PyPI).

Install twine with pip install twine
Build the distributions python setup.py sdist bdist_wheel
Upload the distributions built with twine upload dist/*

NOTE: only package maintainers are permitted to upload the package versions.

Release note¶

Create release note under qtip/docs/release/release-notes and update index.rst

Architecture¶

In Danube, QTIP releases its standalone mode, which is also know as solo:

The runner could be launched from CLI (command line interpreter) or API (application programming interface) and drives the testing jobs. The generated data including raw performance data and testing environment are fed to collector. Performance metrics will be parsed from the raw data and used for QPI calculation. Then the benchmark report is rendered with the benchmarking results.

The execution can be detailed in the diagram below:

Framework¶

QTIP is built upon Ansible by extending modules, playbook roles and plugins.

Modules¶

QTIP creates dedicated modules to gather slave node list and information from installer master. See embedded document in qtip/ansible_library/modules for details

Plugins¶

Stored in qtip/ansible_library/plugins

Action plugins¶

Several action plugins have been created for test data post processing

collect - parse and collect metrics from raw test results like log files
calculate - calculate score according to specification
aggregate - aggregate calculated results from all hosts under test

Playbook roles¶

QTIP roles¶

qtip - main qtip tasks
qtip-common - common tasks required in QTIP
qtip-workspace - generate a workspace for running benchmarks

qtip roles should be included with a specified action and output directory, e.g.:

- { role: inxi, output: "{{ qtip_results }}/sysinfo", tags: [run, inxi, sysinfo] }

testing roles¶

Testing roles are organized by testing tools

inxi - system information tool
nDPI
openssl
ramspeed
unixbench

supporting roles

opnfv-testapi - report result to testapi

Tags¶

Tags are used to categorize the test tasks from different aspects.

stages like run, collect, calculate, aggregate, report
test tools like inxi, ndpi and etc
information or metrics like sysinfo, dpi, ssl

Use

ansible-playbook run.yml --list-tags to list all tags
ansible-playbook run.yml --list-tasks to list all tasks

During development of post processing, you may skip run stage to save time, e.g. ansible-playbook run.yml --tags collect,calculate,aggregate

CLI - Command Line Interface¶

QTIP consists of different tools(metrics) to benchmark the NFVI. These metrics fall under different NFVI subsystems(QPI’s) such as compute, storage and network. A plan consists of one or more QPI’s, depending upon how the end user would want to measure performance. CLI is designed to help the user, execute benchmarks and view respective scores.

Framework¶

QTIP CLI has been created using the Python package Click, Command Line Interface Creation Kit. It has been chosen for number of reasons. It presents the user with a very simple yet powerful API to build complex applications. One of the most striking features is command nesting.

As explained, QTIP consists of metrics, QPI’s and plans. CLI is designed to provide interface to all these components. It is responsible for execution, as well as provide listing and details of each individual element making up these components.

Design¶

CLI’s entry point extends Click’s built in MultiCommand class object. It provides two methods, which are overridden to provide custom configurations.

class QtipCli(click.MultiCommand):

    def list_commands(self, ctx):
        rv = []
        for filename in os.listdir(cmd_folder):
            if filename.endswith('.py') and \
                filename.startswith('cmd_'):
                rv.append(filename[4:-3])
        rv.sort()
        return rv

    def get_command(self, ctx, name):
        try:
            if sys.version_info[0] == 2:
                name = name.encode('ascii', 'replace')
            mod = __import__('qtip.cli.commands.cmd_' + name,
                             None, None, ['cli'])
        except ImportError:
            return
        return mod.cli

Commands and subcommands will then be loaded by the get_command method above.

Extending the Framework¶

Framework can be easily extended, as per the users requirements. One such example can be to override the builtin configurations with user defined ones. These can be written in a file, loaded via a Click Context and passed through to all the commands.

class Context:

    def __init__():

        self.config = ConfigParser.ConfigParser()
        self.config.read('path/to/configuration_file')

    def get_paths():

        paths = self.config.get('section', 'path')
        return paths

The above example loads configuration from user defined paths, which then need to be provided to the actual command definitions.

from qtip.cli.entry import Context

pass_context = click.make_pass_decorator(Context, ensure=False)

@cli.command('list', help='List the Plans')
@pass_context
def list(ctx):
    plans = Plan.list_all(ctx.paths())
    table = utils.table('Plans', plans)
    click.echo(table)

API - Application Programming Interface¶

QTIP consists of different tools(metrics) to benchmark the NFVI. These metrics fall under different NFVI subsystems(QPI’s) such as compute, storage and network. A plan consists of one or more QPI’s, depending upon how the end-user would want to measure performance. API is designed to expose a RESTful interface to the user for executing benchmarks and viewing respective scores.

Framework¶

QTIP API has been created using the Python package Connexion. It has been chosen for a number of reasons. It follows API First approach to create micro-services. Hence, firstly the API specifications are defined from the client side perspective, followed by the implementation of the micro-service. It decouples the business logic from routing and resource mapping making design and implementation cleaner.

It has two major components:

API Specifications

The API specification is defined in a yaml or json file. Connexion follows Open API specification to determine the design and maps the endpoints to methods in python.

Micro-service Implementation: Connexion maps the operationId corresponding to every operation in API Specification to methods in python which handles request and responses.

As explained, QTIP consists of metrics, QPI’s and plans. The API is designed to provide a RESTful interface to all these components. It is responsible to provide listing and details of each individual element making up these components.

Design¶

Specification¶

API’s entry point (main) runs connexion App class object after adding API Specification using App.add_api method. It loads specification from swagger.yaml file by specifying specification_dir.

Connexion reads API’s endpoints(paths), operations, their request and response parameter details and response definitions from the API specification i.e. swagger.yaml in this case.

Following example demonstrates specification for the resource plans.

paths:
  /plans/{name}:
    get:
      summary: Get a plan by plan name
      operationId: qtip.api.controllers.plan.get_plan
      tags:
        - Plan
        - Standalone
      parameters:
        - name: name
          in: path
          description: Plan name
          required: true
          type: string
      responses:
        200:
          description: Plan information
          schema:
            $ref: '#/definitions/Plan'
        404:
          description: Plan not found
          schema:
            $ref: '#/definitions/Error'
        501:
          description: Resource not implemented
          schema:
            $ref: '#/definitions/Error'
        default:
          description: Unexpected error
          schema:
            $ref: '#/definitions/Error'
definitions:
  Plan:
    type: object
    required:
      - name
    properties:
      name:
        type: string
      description:
        type: string
      info:
        type: object
      config:
        type: object

Every operationId in above operations corresponds to a method in controllers. QTIP has three controller modules each for plan, QPI and metric. Connexion will read these mappings and automatically route endpoints to business logic.

Swagger Editor can be explored to play with more such examples and to validate the specification.

Controllers¶

The request is handled through these methods and response is sent back to the client. Connexion takes care of data validation.

@common.check_endpoint_for_error(resource='Plan')
def get_plan(name):
    plan_spec = plan.Plan(name)
    return plan_spec.content

In above code get_plan takes a plan name and return its content.

The decorator check_endpoint_for_error defined in common is used to handle error and return a suitable error response.

During Development the server can be run by passing specification file(swagger.yaml in this case) to connexion cli -

connexion run <path_to_specification_file> -v

Extending the Framework¶

Modifying Existing API:¶

API can be modified by adding entries in swagger.yaml and adding the corresponding controller mapped from operationID.

Adding endpoints:
New endpoints can be defined in paths section in swagger.yaml. To add a new resource dummy -
paths:
  /dummies:
    get:
      summary: Get all dummies
      operationId: qtip.api.controllers.dummy.get_dummies
      tags:
        - dummy
      responses:
        200:
          description: Foo information
          schema:
            $ref: '#/definitions/Dummy
        default:
          description: Unexpected error
          schema:
            $ref: '#/definitions/Error'
And then model of the resource can be defined in the definitions section.
definitions:
  Dummy:
    type: object
    required:
      - name
    properties:
      name:
        type: string
      description:
        type: string
      id:
        type: string
Adding controller methods:
Methods for handling requests and responses for every operation for the endpoint added can be implemented in controller.

In controllers.dummy
def get_dummies():
    all_dummies = [<code to get all dummies>]
    return all_dummies, httplib.OK
Adding error responses
Decorators for handling errors are defined in common.py in api.
from qtip.api import common

@common.check_endpoint_for_error(resource='dummy',operation='get')
def get_dummies()
    all_dummies = [<code to get all dummies>]
    return all_dummies

Adding new API:¶

API can easily be extended by adding more APIs to Connexion.App class object using add_api class method.

In __main__
def get_app():
app = connexion.App(__name__, specification_dir=swagger_dir)
app.add_api('swagger.yaml', base_path='/v1.0', strict_validation=True)
return app
Extending it to add new APIs. The new API should have all endpoints mapped using operationId.
from qtip.api import __main__
my_app = __main__.get_app()
my_app.add_api('new_api.yaml',base_path'api2',strict_validation=True)
my_app.run(host="0.0.0.0", port=5000)

Compute QPI¶

The compute QPI gives user an overall score for system compute performace.

Summary¶

The compute QPI are calibrated a ZTE E9000 server as a baseline with score of 2500 points. Higher scores are better, with double the score indicating double the performance. The compute QPI provides three different kinds of scores:

Workload Scores
Section Scores
Compute QPI Scores

Baseline¶

ZTE E9000 server with an 2 Deca core Intel Xeon CPU processor,128560.0MB Memory.

Workload Scores¶

Each time a workload is executed QTIP calculates a score based on the computer’s performance compared to the baseline performance.

Section Scores¶

QTIP uses a number of different tests, or workloads, to measure performance. The workloads are divided into five different sections:

Section	Detail	Indication
Integer	Integer workloads measure the integer instruction performace of host or vm by performing Dhrystone test.	All app relies on integer performance
Floating point	Floating point workloads measure the floating pointperfo rmance by performing Whetstone test.	Floating point performance is especially important in video games,digital content creation applications.
Memory	Memory workloads measure memory bandwidth by performing RamSpeed test.	Software working with cipher large amounts data relies on SSL Performace.
DPI	DPI workloads measure deep-packet inspection speed by performing nDPI test.	Software working with network packet anlysis relies on DPI performance.
SSL	SSL Performance workloads measure cipher speeds by using the OpenSSL tool.	Software working with cipher large amounts data relies on SSL Performace

A section score is the geometric mean of all the workload scores for workloads that are part of the section. These scores are useful for determining the performance of the computer in a particular area.

Compute QPI Scores¶

The compute QPI score is the weighted arithmetic mean of the five section scores. The compute QPI score provides a way to quickly compare performance across different computers and different platforms without getting bogged down in details.

Method	Path	Description
GET	/v1.0/plans	Get the list of of all plans
GET	/v1.0/plans/{name}	Get details of the specified plan

Method	Path	Description
GET	/v1.0/metrics	Get the list of all metrics
GET	/v1.0/metrics/{name}	Get details of specified metric

Method	Path	Description
GET	/v1.0/qpis	Get the list of all QPIs
GET	/v1.0/qpis/{name}	Get details of specified QPI