Paralyzing Bioinformatics Applications Using Conducive Hadoop Cluster

Abstract

Bioinformatics may be defined as the application of computer science to molecular biology in the form of statistics and analytics. The bioinformatics applications deal with bulk amount of data. Researchers are now facing problems with the analysis of such ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. More over big challenge is involved in processing, storing and analyzing these peta bytes of data without causing much delay. Most of the bioinformatics algorithms are sequential thus making situation rather worse. This implies that data manipulations by means of uniprocessor systems are impractical. However most of the biological problems have parallel nature. Hence a practical and effective approach involves the usage of parallel clusters of workstations. Hadoop can be used to tackle this class of problems with good performance and scalability. This technology could be the basis of a computational parallel platform for several problems in the context of bioinformatics applications. Normally, Hadoop is deployed over high performance computing systems which are expensive involving complex deployment scenarios that only big enterprises are able to make it possible. So for smaller research organizations where cost is an important factor cannot choose systems with high computational capabilities for cluster set up. Rocks cluster is a viable solution in such scenarios. Rocks Cluster Distribution originally called NPACI Rocks is a Linux distribution intended for high-performance computing clusters. This paper implements a cost-effective cluster for paralyzing bioinformatics applications by deploying Hadoop over rock cluster and Emphasizes on the usage of commodity clusters for paralyzing bioinformatics applications by providing necessary justifications. Results show that paralyzing bioinformatics application saves much time compared to stand alone mode of execution effectively under optimal cost considerations.