Cluster computing and grid computing both use multiple computers to perform tasks. The main difference is that grid computing breaks up applications into modules, while cluster computing runs entire applications with redundancy. Grid computing solves complex problems in parallel, while cluster computing creates a redundant environment. Grid computing distributes processing power across a network, while cluster computing acts as one computer. Both use additional resources to supplement application load requirements.
Cluster computing and grid computing both refer to systems that use multiple computers to perform a task. The main difference between the two is that grid computing relies on an application to be broken up 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.
Standard cluster computing is designed to produce a redundant environment that will ensure that an application continues to operate in the event of a hardware or software failure. This cluster design requires that each node in the cluster mirror existing nodes in both the hardware environment and operating systems.
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 goal of the cluster. Cluster computing started with the need to create redundancy for software applications but has expanded to 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 project. Cluster computing is a replication of servers and environments to create a redundant environment, and a cluster grid is a set of computers loosely coupled to solve independent modules or problems. Grid computing is designed to solve independent problems in parallel, thus leveraging the computer processing power of a distributed model.
Before grid computing, any advanced algorithmic processes were only available with supercomputers. These super computers were massive machines that required enormous amounts of energy and processing power to perform advanced problem solving. Grid computing follows the same paradigm as a super computer but distributes the model across many computers on a loosely coupled network. Each computer shares a few cycles of computer processing power to support the network.
The typical cluster design for a business is a tightly coupled set of computers that act as one computer. These computers can be balanced to support workload and network demands. In the event of server failure within a cluster computing farm, the load balancer automatically routes traffic to another server in the cluster farm, which continues the core functionality of the application smoothly. Grid computing and cluster computing are very similar in that they each use the resources of additional servers and computer processing units (CPUs) to supplement the load requirements of an application.
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