What is the Difference Between Cluster Computing and Grid Computing?

Troy Holmes

Cluster computing and grid computing both refer to systems that use multiple computers to perform a task. The primary difference between the two is that grid computing relies on an application to be broken into discrete modules, where each module can run on a separate server. Cluster computing typically runs an entire application on each server, with redundancy between servers.

A group of computers are linked together to operate as a single entity during cluster computing.
A group of computers are linked together to operate as a single entity during cluster computing.

Standard cluster computing is designed to produce a redundant environment that will ensure an application will continue to function in the event of a hardware or software failure. This cluster design requires that each node in the cluster mirror the existing nodes in both hardware environment and operating systems.

Load balancing is used to evenly distribute incoming requests across a cluster of computers.
Load balancing is used to evenly distribute incoming requests across a cluster of computers.

General cluster computing is the process by which two or more computers are integrated to complete a specified process or task within an application. This integration can be tightly coupled or loosely coupled, depending on the desired objective of the cluster. Cluster computing began with the need to create redundancy for software applications but has expanded into a distributed grid model for some complex implementations.

Grid computing is more of a distributed approach to solving complex problems that could not be solved with a typical cluster computing design. Cluster computing is a replication of servers and environments to create a redundant environment and a grid cluster is a set of computers loosely coupled together to solve independent modules or problems. Grid computing is designed to work independent problems in parallel, thereby leveraging the computer processing power of a distributed model.

Prior to grid computing, any advanced algorithmic process was only available with super computers. These super computers were huge machines that took an enormous amount of energy and processing power to perform advanced problem solving. Grid computing is following the same paradigm as a super computer but distributing the model across many computers on a loosely coupled network. Each computer shares a few cycles of computer processing power to support the grid.

The typical cluster design for an enterprise is a tightly coupled set of computers that act as one computer. These computers can be load balanced to support work load and network requests. In the event of a server failure within a cluster computing farm, the load balancer automatically routes traffic to another server on the cluster farm, which seamlessly continues the core functionality of the application. Grid computing and cluster computing are very similar as they each use the resources of additional servers and computer processing units (CPU) to complete the load requirements of an application.

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Discussion Comments


@SkyWhisperer - That day has come and gone. It used to be called the super computer, but those machines were more powerful than today’s desktops I believe, even the quad core computers.

The cluster and grid frameworks will always be better, however, because there is virtually no limit to the number of computers you can bundle in the cluster. A supercomputer is limited by its processing power.


@hamje32 - I think it’s clear from the article that computer networks can be configured in both grids and clusters, each designed with their own applications in mind.

The advantage in either case is that you can use standard processors. I know that nowadays you see things like dual and quad processors on a single system, so you can simulate some distributed processing on one standalone computer.

While this is possible, I still believe it’s not as powerful as stringing along a bunch of separate computers in a grid or a cluster.

Perhaps the day will come when standalone computers will provide us with the processing power that is in the cluster or grid.


Cluster and grid computing are very popular in academic research environments. I had a professor in college set up a Linux cluster to work on some of his problems in computer science.

The advantage of Linux is that it’s basically free, and of course the computers themselves were fairly cheap. He was able to run these system 24 hours a day for several weeks, just on one of his problems alone.

If he had tried to duplicate the same processing power using a super computer or something like that, it would have been considerably more expensive, putting an added burden on the university’s strained budget.

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