Large-scale computational needs demand large-scale
processing systems and their accompanying large-scale price tags. Or do
they?Through distributed computing, a company can avoid costly cluster setups by harnessing the idle processes of any number of client and server machines. Combing all these remnant processing cycles can create the number-crunching power needed for almost any job.
In this article, I’ll discuss the benefits of distributed computing and show you how to install and use Condor, an open source, distributed computing batch system.
What is distributed computing?
A distributed computing system provides a specific set of services (applications, compilers, rendering functions, benchmarking) with certain properties (system names, security, user identification, access to similar functions, centralized management) throughout a network at times when available processing resources are idle.
Every distributed computing platform provides some type of service. In most cases, a distributed system’s goal is to harness the computing power of a great number of machines to perform calculations in areas of science, biology, finances, or other statistical analysis.
Why not just use a cluster?
Many distributed computing applications don’t need the kind of resources required by such number-crunching systems as the Internet-based SETI or protein folding initiatives. In fact, a distributed computing environment can be set up in almost any organization by making use of the spare CPU power on users’ desktop machines.
This approach is much different from setting up a cluster, which is a series of networked computers devoted to working on only cluster activities. A distributed node, on the other hand, can handle other tasks, such as e-mail and office productivity software, and tackles distributed tasks only when a set threshold of CPU resources are idle.
The most obvious benefit of this distributed approach is cost savings. You’ve already got all those Pentium-grade desktops sitting around, so why not use them?
Of course, nothing’s that simple, and the benefits of using a distributed system instead of a cluster come with corresponding drawbacks. While a distributed system can save you a bunch of money, such systems are not as powerful as pure clusters with the same number of machines. And while risk of failure is especially low in the distributed computing model—the system can handle machines coming and going at any time—distributed systems have less (sometimes no) administrative control over machines.
Play video
There are currently no comments for this post.