[arXiv] BigDataFr recommends StratOS – A Big Data Framework for Scientific Computing

<div id="wp-socials-general-btn"></div><div style="clear:both"></div></div><p><a title="@arxiv.org - StratOS - A Big Data Framework for Scientific Computing" href="http://www.big-data-fr.com/documents/stratOS/a-big-data-framework-for-scientific-computing" target="_blank">BigDataFr recommends StratOS – A Big Data Framework for Scientific Computing

Abstract

‘We introduce StratOS, a Big Data platform for general computing that allows a datacenter to be treated as a single computer. With StratOS, the process of writing a massively parallel program for a datacenter is no more complicated than writing a Python script for a desktop computer.

Users can run pre-existing analysis software on data distributed over thousands of machines with just a few keystrokes. This greatly reduces the time required to develop distributed
data analysis pipelines.

The platform is built upon industry-standard, open-source Big Data technologies, from which it inherits fast data throughput and fault tolerance. StratOS enhances these technologies by adding an intuitive user interface, automated task monitoring, and other usability features.’

Introduction

In the last decade we have seen an exponential increase in the volume of data generated from sensors, experiments and simulations. Disciplines that once were data starved are now being ooded with terabytes, and soon petabytes of data.
We are entering a new, data-driven, era of science in which discoveries will be made by analyzing data that is not only massive in size but heterogeneous and, in some cases, highly interconnected.
The challenge is how to extract meaningful patterns from the sea of information.’

Keywords: Computing platforms, Cloud computing, Monitors
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By Nathaniel R. Stickley and Miguel A. Aragon-Calvo
Department of Physics and Astronomy, University of California,
Riverside
Source: arxiv.org

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