The Quantcast File System (QFS) is an efficient alternative to the Hadoop Distributed File System (HDFS). QFS is written in C++, is plugin compatible with Hadoop MapReduce, and offers several efficiency improvements relative to HDFS: 50% disk space savings through erasure coding instead of replication, a resulting doubling of write throughput, a faster name node, support for faster sorting and ...

Hadoop MapReduce assists companies and researchers to deal with processing large volumes of data. Hadoop has a lot of configuration parameters that must be tuned in order to obtain a better application performance. However, the best tuning of the parameters is not easily obtained by inexperienced users. Therefore, it is necessary to create environments that promote and motivate information shar...

Transitioning cloud-based Hadoop frameworks from IaaS to PaaS, which are commercially conceptualized as pay-as-you-go or pay-per-use, often reduces the associated system costs. However, managed systems obscure inner performance dynamics of platform and present a black-box behavior end-users. The aim this study was investigate resource utilization current platforms. Thus, we explored three promi...

A Storage Architecture for Data-Intensive Computing by Jeffrey Shafer The assimilation of computing into our daily lives is enabling the generation of data at unprecedented rates. In 2008, IDC estimated that the “digital universe” contained 486 exabytes of data [9]. The computing industry is being challenged to develop methods for the cost-effective processing of data at these large scales. The...

Enter Hadoop, the de facto open source standard that is increasingly being used by many companies in large data migration projects. Hadoop is an open-source framework that allows for the distributed processing of large data sets. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. As data from different sources flows into Hadoop,...

We explore the design and implementation of a scalable Datalog system using Hadoop as the underlying runtime system. Observing that several successful projects provide a relational algebra-based programming interface to Hadoop, we argue that a natural extension is to add recursion to support scalable social network analysis, internet traffic analysis, and general graph query. We implement semi-...

Nowadays cloud computing is emerging Technology. It is used to access anytime and anywhere through the internet. Hadoop is an open-source Cloud computing environment that implements the Googletm MapReduce framework. Hadoop is a framework for distributed processing of large datasets across large clusters of computers. This paper proposes the workload of jobs in clusters mode using Hadoop. MapRed...

MapReduce is a popular programming model for distributed processing of large data sets. Apache Hadoop is one of the most common open-source implementations of such paradigm. Performance analysis of concurrent job executions has been recognized as a challenging problem, at the same time, that it may provide reasonably accurate job response time at significantly lower cost than experimental evalu...