With the right configuration, clustering can prevent a single point of failure either by distributing workload to different aspects such as workload balancing or by having a back-up node such as HA (High Availability) clustering which implements efficient failover. The hardware components in clustering are very important. They can push the applications and files faster because of their increased capability.
All of these will not be possible if the hardware is not properly installed. Even the external factors that might single-handedly place the clusters in danger should be considered in terms of protecting the hardware from untoward inconveniences.
However, the hardware components will never function if no clustering application is implemented. The configurations will not be fully realized if there are no applications that will control the nodes or even shutdown a cluster for maintenance and other operational purposes.
The clustering applications also assist administrators to remotely control and monitor the performance of each node and the general environment the clusters that might be a factor for its performance.
There are a good number of clustering applications in the market today. Most of the applications are geared towards specific type of clustering while there are those that are versatile enough to be adapted to different types of clustering.
MPI as the Standard Clustering Application
While there are many clustering applications that could assist in control nodes and clusters, only a few of them could be at par with MPI or Message Passing Interface in terms of popularity.
Technically, MPI is not an application but an API (Application Programming Interface) which means it could be developed by itself or could be developed with another programming language. Its simplicity and adaptability to different programming language has made MPI a highly favored clustering application.
The proper implementation of MPI will enable simplified communication that could be used in simple two-node cluster setting or could be implemented in complicated load balancing to ensure that workload will not be focused on a single node only.
Different programming languages have been adapted to MPI and many open source clustering applications have been built on top of this clustering API.
Linux Clustering Applications
The open source community has also developed specific applications for clustering. The most popular applications that are freely distributed today could be divided into four groups. The first group refers to service based clustering application.
These applications accept information or function requests from its users and the workload will be distributed or connected to the cluster. The second group is the applications that will not only distribute requests but will move towards data and functions distribution which could easily react when the clustering environment changes.
The third group is the applications that are simple single-system image wherein the clusters are grouped into one for ease of control. The fourth group is the applications that are geared towards application workload control. Some of the Linux clustering applications could be applied in two groups as they offer versatility of function.
Some of the recognized clustering applications are the following:
• Linux Virtual Server
Windows Compute Cluster Server
The software giant Microsoft Corporation has also developed their own clustering application. This type of clustering application is closely linked to another product from the same company simply called Windows Server. Specifically, this form of cluster server is based on the Windows Server 2003 and currently adapted to Windows Server 2008.
This type of clustering is geared towards improvement of computing through the help of clustering. This cluster server comes with schedulers and other tools for ease of control particularly some functions based on MPI.
Choosing the Right Application
Choosing the right clustering application really depends on the business need. However, administrators have to make sure that the clustering application should not be too much for the nodes to handle or would have functions that could be worthless.
Implementing a powerful clustering application for simple clustering need is just a waste of resources not to mention funds in acquiring these applications. Instead of an efficient application, the clustering application becomes a bloatware. By determining the business need, the right clustering application could be selected. The right clustering application will also ensure the optimum performance of the hardware which could directly benefit users.