This paper focuses on two aspects of configuration choices of the Hadoop platform. Firstly we are looking to establish performance implications of expanding an existing Hadoop cluster beyond a single rack. In the second part of the testing we are focusing on performance differences when deploying clusters of different sizes. The study also examines constraints of the disk latency found on the t...

Enterprises are increasingly using Apache Hadoop, more specifically HDFS, as a central repository for all their data; data coming from various sources, including operational systems, social media and the web, sensors and smart devices, as well as their applications. At the same time many enterprise data management tools (e.g. from SAP ERP and SAS to Tableau) rely on SQL and many enterprise user...

17ANSP-BD-001 Hadoop Performance Modeling for JobEstimation and Resource Provisioning MapReduce has become a major computing model for data intensive applications. Hadoop, an open source implementationof MapReduce, has been adopted by an increasingly growing user community. Cloud computing service providers such as AmazonEC2 Cloud offer the opportunities for Hadoop users to lease a certain amou...

Nowadyas, we are witnessing the fast production of very large amount of data, particularly by the users of online systems on the Web. However, processing this big data is very challenging since both space and computational requirements are hard to satisfy. One solution for dealing with such requirements is to take advantage of parallel frameworks, such as MapReduce or Spark, that allow to make ...

SUMMARY MR-Tandem adapts the popular X!Tandem peptide search engine to work with Hadoop MapReduce for reliable parallel execution of large searches. MR-Tandem runs on any Hadoop cluster but offers special support for Amazon Web Services for creating inexpensive on-demand Hadoop clusters, enabling search volumes that might not otherwise be feasible with the compute resources a researcher has at ...

We present a data-driven approach for diagnosing performance issues in heterogeneous Hadoop clusters. Hadoop is a popular and extremely successful framework for horizontally scalable distributed computing over large data sets based on the MapReduce framework. In its current implementation, Hadoop assumes a homogeneous cluster of compute nodes. This assumption manifests in Hadoop’s scheduling al...

Traditional High Performance Computing (HPC) resources, such as those available on the TeraGrid, support batch job submissions using Distributed Resource Management Systems (DRMS) like TORQUE or the Sun Grid Engine (SGE). For large-scale data intensive computing, programming paradigms such as MapReduce are becoming popular. A growing number of codes in scientific domains such as Bioinformatics ...

In this paper, based on the application conditions of the existing traditional data warehouse and the future forecast of the Hadoop big data platform, this paper proposes the new framework of the cooperation of Hadoop and traditional data warehouse which focus on the cooperation between the traditional data warehouse and the Hadoop technique to solve the problem that the traditional data wareho...