The Scalable Test Platform
The Open Source Development Lab (OSDL) is a nonprofit company working to enhance Linux scalability and telco capabilities. OSDL sponsors (www.osdlab.org/sponsors) have financed a full-scale test and development lab, complete with terabytes of storage and an array of SMP servers with anywhere from 2 to 16 CPUs. At the lab we provide developers with full access to enterprise-class machines via remote login.
We have been working with developers on the creation and execution of their tests. During this process, we have noticed a number of things that have to be done again and again for each test that comes through the lab. We listed the tasks that went into running an average test sequence and found a great deal of the process involved human interaction that could be automated. The Scalable Test Platform (STP) is the result of our attempt to automate the testing process from request to report.
Benchmarking itself has inherent concept problems that are outside both the scope of this article and the scope of the Scalable Test Platform effort. There are, however, solvable problems with current testing practices, and that is what the STP attempts to address. Please keep in mind, the benchmarking we focus on is completely different from methods used to get marketable benchmark numbers.
The configuration of a testing environment is rarely as well documented as it should be. Documentation on the setup of systems used in tests is usually limited to what the tester believes is relevant to their specific research goals. This lack of detail will cause problems later on, when other analysts are examining the report. It is not uncommon for an analyst to have to duplicate an entire test sequence to get the data required to answer questions that come up later. It is also common practice for a testing setup to be only partially automated. The resulting human interaction at undocumented moments will also affect the repeatability of the results.
Performance testing can require massive resources, both in the form of time and hardware. How many open-source developers can get access to 50 two-way client servers on a gigabit network in order to test a server farm made up of multiple 8-CPU servers and a 16-CPU server? Few companies would stretch to provide access to hardware like that and then only with a full entourage of managers and the potential revenue return to justify the expense. A good idea conceived by a developer without access to hardware like this is likely to remain unexplored.
Currently no central archive exists of well-documented results for performance, stability and standard compliance tests. Researchers are forced to run their own tests or pick and choose from mediocre results to come up with a less-than-accurate guess. System administrators have no central place to look for starter information on what combination of kernel, distribution and hardware tends to work well for a workload similar to what they anticipate. This lack of available research leads to confusion regarding the performance and reliability among the myriad of Linux choices.
Linux kernel developers cannot spend the time and effort required to run long performance and stability tests on their patches. Even if a developer is willing to spend the time testing a patch, testing software often requires a great deal of knowledge and specialized hardware just to install and configure. Occasionally this situation leads to problems being introduced into both the stable and development kernel trees. It also can allow problems solved previously to recur in future development but go unnoticed because of a lack of regression testing.
A number of developers have spoken up on the Linux kernel mailing list requesting a standard testing procedure for new patches. Many users and developers agree that a simple procedure, including performance, stability, standards compliance and regression testing, would benefit Linux kernel development.
While you can't test for every bug out there, you can check for common types of problems. It's generally not too difficult to add a regression test case to your testing suite after a bug is found and fixed. The problem is not in the creation of these tests. Most developers realize that it's a good idea to have a few synthetic tests available and very often do so. The problem is that most developers can't or won't take the time to configure a full range of verification tests. While coding can be fun, testing is often quite boring. If a developer could easily request a full test of their code and then continue working while someone else does the dirty work, we think they would be more inclined to attempt verification runs on their patches.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
Join Linux Journal's Mike Diehl and Pat Cameron of Help Systems.
Free to Linux Journal readers.Register Now!
- SUSE LLC's SUSE Manager
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- My +1 Sword of Productivity
- Managing Linux Using Puppet
- Non-Linux FOSS: Caffeine!
- Doing for User Space What We Did for Kernel Space
- SuperTuxKart 0.9.2 Released
- Google's SwiftShader Released
- Parsing an RSS News Feed with a Bash Script
- Rogue Wave Software's Zend Server
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide