Load Balancing using High Performance Computing Cluster Programming
High-performance computing has created a new approach to science. Modeling is now a viable and respected alternative to the more traditional experiential and theoretical approaches. High performance is a key issue in data mining or in image rendering. Traditional high performance clusters have proved their worth in a variety of uses from predicting the weather to industrial design, from molecular dynamics to astronomical modeling. A multicomputer configuration, or cluster, is a group of computers that work together. A cluster has three basic elements—a collection of individual computers, a network connecting those computers, and software that enables a computer to share work among the other computers via the network. Clusters are also playing a greater role in business. Advances in clustering technology have led to high-availability and load-balancing clusters. Clustering is now used for mission-critical applications such as web and FTP servers. For permanent clusters there are, for lack of a better name, cluster kits, software packages that automate the installation process. A cluster kit provides all the software you are likely to need in a single distribution. Cluster kits tend to be very complete. For example, the OSCAR distribution. Open Source Cluster Application Resources is a software package that is designed to simplify cluster installation. A collection of open source cluster software, OSCAR includes everything that you are likely to need for a dedicated, high-performance cluster. OSCAR takes you completely through the installation of your cluster. In this Paper with the help of Open Source Cluster Application Resource (OSCAR) cluster kit, attempt to setup a high performance computational cluster with special concern to applications like Integration and Sorting. The ease use of cluster is possible globally and transparently managing cluster resources. Cluster computing approach nowadays is an ordinary configuration found in many organizations to target requirements of high performance computing.
Keywords: Clustering, Performance analysis, Web clustering, Workload characterization, High Performance
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