Gnu Queue: Linux Clustering Made Easy
So, your organization has finally decided to double the number of Linux workstations in your cluster. Now you've got twice as much computer power as before, right?
Wrong. It's not that simple. Old habits die hard, and your organization will probably continue trying to submit most of its jobs to the old computers. Or, used to the old computers being overloaded, your users will submit most of their jobs to the new computers, leaving the old ones idle. Let's face it, it's just too much of a pain to log into every computer on your network to see which one's the least utilized. It's simpler just to send the job somewhere and get on with the rest of the day's work, especially if it's a quick and dirty job and there are lots of computers. The result, however, is slower overall performance and wasted resources.
What you need is a simple utility for sending your job to the least utilized machine automatically. You could install a batch processing system like NQS—maybe you've already installed one—but it's annoying to check your e-mail or run special commands to see if your quick and dirty job has finished running in some batch queue. If something goes wrong, you might need to use nonstandard commands or track down which remote machine is executing your job, do a ps to learn its process id, and then do a kill. Users moving to new departments or new jobs often find that they need to relearn a complex set of nonstandard commands, because their new organization uses a different batch processing system than what they're used to.
You'd like something really simple, something that works through the shell, so that you could check your job's status with a command like jobs, and allow the shell to notify you when the job has terminated, just as if you were running it in the background on your local machine. You'd like to be able to send the job into the background and foreground with bg and fg and kill the job with kill, just as if the job were running on the local node. This way, you can control remote jobs using the same standard shell commands you and your users already know how to use.
Enter GNU Queue. GNU Queue makes it easy to cluster Linux workstations. If you already know how to control jobs running on your local machine, you already know how to control remote jobs using GNU Queue. You don't even need special privileges to install and run GNU Queue on your cluster—anyone can do it. Once you've discovered how incredibly easy it is to cluster Linux environments with GNU Queue, you'll wonder why organizations continue to spend so much money on comparatively hard-to-cluster Windows NT environments.
With GNU Queue, all you have to do is write a simple wrapper shell script to cause software applications to farm out every time to the network:
#!/bin/sh<\n> exec queue -i -w -p -- realbogobasicinterpreter $*
and name it “bogobasicinterpreter”, with the real bogobasicinterpreter renamed “realbogobasicinterpreter”. This assumes, of course, that you have administrative privileges for your cluster (not necessary to install and run GNU Queue). When someone runs bogobasicinterpreter, GNU Queue is told to farm the job out of the network.
Another popular way to use GNU Queue is to set up an alias. You can do this even if you don't have administrative privileges on your cluster. If you are using csh, change to your home directory and add the following line to your .cshrc:
alias q queue -i -w -p --
and run the command source .cshrc. Then, you can simply farm out jobs by typing “q” before the name of the job you want to farm out.
Either way, GNU Queue does all the hard work, instantly finding a lightly loaded machine to run the job on. It then fires up a proxy job on your local machine that “pretends” to be the remotely executing job, so that you can background, foreground and kill the remotely running job through normal shell commands. There's no need to teach other users new commands to interact with some complicated batch processing system—if they understand how to use the UNIX shell to control local jobs, they understand how to use GNU Queue to control remotely executing jobs.
Of course, GNU Queue supports many additional features. It supports a traditional batch processing mode, where output can optionally be returned by e-mail. Versions 1.20.1 and higher now have alpha support for various modes of job migration, which lets the administrator to allow running jobs to actually move from one machine to another in order to maintain a constant load throughout the cluster. More importantly, GNU Queue allows administrators to place limits on the number of a type of job that can run (say, allow no more than five bogobasicinterpreter jobs to run on any node) or to prevent certain jobs from running when a machines's load is too great. For example, the bogobasicinterpreter can't be started if the load average exceeds five; running interpreters are suspended if the load average on the node exceeds seven. It's also possible to place restrictions on the time of day certain jobs may be run (no bogobasicinterpreters on Saturdays) or to have it periodically check the return value of a custom script to determine whether or not a program can be run. But, you'll probably never need these advanced features.
|illusive networks' Deceptions Everywhere||Aug 29, 2016|
|Happy Birthday Linux||Aug 25, 2016|
|ContainerCon Vendors Offer Flexible Solutions for Managing All Your New Micro-VMs||Aug 24, 2016|
|Updates from LinuxCon and ContainerCon, Toronto, August 2016||Aug 23, 2016|
|NVMe over Fabrics Support Coming to the Linux 4.8 Kernel||Aug 22, 2016|
|What I Wish I’d Known When I Was an Embedded Linux Newbie||Aug 18, 2016|
- illusive networks' Deceptions Everywhere
- Happy Birthday Linux
- New Version of GParted
- What I Wish I’d Known When I Was an Embedded Linux Newbie
- All about printf
- Download "Linux Management with Red Hat Satellite: Measuring Business Impact and ROI"
- Updates from LinuxCon and ContainerCon, Toronto, August 2016
- ContainerCon Vendors Offer Flexible Solutions for Managing All Your New Micro-VMs
- Blender for Visual Effects
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