As data volumes increase at a high speed in more and more application fields of science,
engineering, information services, etc., the challenges posed by data-intensive computing …

Hadoop is a software framework supporting the Map-Reduce programming model. It relies
on the Hadoop Distributed File System (HDFS) as its primary storage system. The efficiency …

As data volumes processed by large-scale distributed data-intensive applications grow at
high-speed, an increasing I/O pressure is put on the underlying storage service, which is …

MapReduce is an emerging programming model for data-intense application proposed by
Google, which has attracted a lot of attention recently. MapReduce borrows from functional …

Efficient namespace metadata management is increasingly important as next-generation file
systems are designed for peta and exascales. New schemes have been proposed, however …

Abstract Infrastructure-as-a-Service (IaaS) cloud computing has revolutionized the way we
think of acquiring computational resources: it allows users to deploy virtual machines (VMs) …

With data volumes increasing at a high rate and the emergence of highly scalable
infrastructures (cloud computing, petascale computing), distributed management of data …

As map-reduce emerges as a leading programming paradigm for data-intensive computing,
today's frameworks which support it still have substantial shortcomings that limit its potential …

As the rate, scale and variety of data increases in complexity, the need for flexible
applications that can crunch huge amounts of heterogeneous data fast and cost-effective is …

For over a decade, Desktop Grid systems have paved the way to high throughput computing
over large scale network of Desktop PCs. Nowadays, the aggregate computing power of the …