Sunday, January 22, 2012

Cluster computing

Cluster computing:

There is an enormous requirement of processing power in many enterprises in order to manage their desktop applications, databases and knowledge management. The solution for this is nothing but cluster computing. Cluster computing is thus referred as the use of multiple computers, typically PCs or UNIX workstations, multiple storage devices in order to provide a single highly available system. Cluster computing is used in many areas such as in hosting websites, in managing game server and in big and/or complex projects in which large mathematical calculations are to be executed.
Cluster computing is defined as the technique of linking two or more computers into a network by using the advantages of the parallel processing power of those computers.
• Cluster computing is a form of computing in which a group of computers are connected with each other and acts like a single entity.

Benefits of computer clusters are as follows:
1) Helps in reducing the cost.
2) Parallel processing power of clustering are very effective and faster.
3) Provides improved network technology.
4) Computer clusters can be easily expanded by adding more nodes into a network as per the requirements.
5) If a node fails in network then its operation can be simply transferred to another node within cluster, ensuring that there is no interruption in service.
Types of computer clusters:
<1 load-balancing clusters:
It divides the workload efficiently between the available nodes. Thus by distributing the workload, it ensures the optimization of limited processing power.

2 High availability clusters:
Whenever the problems arise due to mainframe failure in the organizations then high availability clusters are used. It ensures 24/7 access to computational power. In business where data processing is time sensitive, this feature of high availability clusters becomes important.

3 high performance clusters:
They are designed to perform functions that requires node to communicate as they perform their tasks. E. g, when calculating results from one node will affect future results from another.


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