Mikhail Dubov 2a0558bb14 Data processing: aggregation plots
This is the first contribution to the automated data processing and
visualization in Rally. Here we introduce the method for plotting
the relation between the values of some config parameter of a benchmark
scenario and its runing time. Minimal, maximal and average runtimes
are plotted, in assumption that the benchmark scenario has been run
several times during the task execution with different values of the
parameter the data is aggregated on (e.g. the "active_users" parameter).

The patch also extends the CLI with a new command, namely
"task plot aggregated <parameter_name> <task_uuid>"

Blueprint data-processing

Change-Id: Ieee6622f6cfdc737878a0217cf965307d739b2ce
2013-11-28 17:03:56 +04:00
2013-10-15 14:59:18 +04:00
2013-11-28 17:03:56 +04:00
2013-11-28 17:03:56 +04:00
2013-10-15 11:53:54 +04:00
2013-09-18 19:04:57 +03:00
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2013-08-03 09:17:25 -07:00
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Rally

Introduction

Rally is a Benchmark-as-a-Service project for OpenStack.

Rally is intended to provide the community with a benchmarking tool that is capable of performing specific, complicated and reproducible test cases on real deployment scenarios.

In the OpenStack ecosystem there are currently several tools that are helpful in carrying out the benchmarking process for an OpenStack deployment. To name a few, there are DevStack and FUEL, which are intended for deploying and managing OpenStack clouds, the Tempest testing framework which validates OpenStack APIs, some tracing facilities like Tomograph with Zipkin. The challenge, however, is to compile all these tools together on a reproducible basis. That can be a rather difficult task since the number of compute nodes in a practical deployment can easily be large and also because one may be willing to use many different deployment strategies that pursue different goals (e.g., while benchmarking the Nova Scheduler, one usually does not care of virtualization details, but is more concerned with the infrastructure topologies; while in other specific cases it may be the virtualization technology that matters). What Rally aims to do is Compile many already existing benchmarking facilities into one project, making it flexible to user requirements and ensuring the reproducibility of test results.

Architecture

Rally is split into 4 main components:

  1. Deployment Engine, which is responsible for processing and deploying VM images (using DevStack or FUEL according to users preferences). The engine can do one of the following:

    • deploy an Operating System (OS) on already existing VMs;
    • starting VMs from a VM image with pre-installed OS and OpenStack;
    • delpoying multiple VMs inside each OpenStack compute node based on a VM image.
  2. VM Provider, which interacts with cloud provider-specific interfaces to load and destroy VM images;

  3. Benchmarking Tool, which carries out the benchmarking process in several stages:

    • runs Tempest tests, reduced to 5-minute length (to save the usually expensive computing time);
    • runs the user-defined test scenarios (using the Rally testing framework);
    • collects all the test results and processes the by Zipkin tracer;
    • puts together a benchmarking report and stores it on the machine Rally was lauched on.
  4. Orchestrator, which is the central component of the system. It uses the Deployment Engine, to run control and compute nodes, in addition to launching an OpenStack distribution. After that, it calls the Benchmarking Tool to start the benchmarking process.

Wiki page:

https://wiki.openstack.org/wiki/Rally

Launchpad page:

https://launchpad.net/rally

Code is hosted on github:

https://github.com/stackforge/rally

Description
Rally provides a framework for performance analysis and benchmarking of individual OpenStack components as well as full production OpenStack cloud deployments
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