Int. J. of Big Data Intelligence   »   2014 Vol.1, No.1/2

 

 

Title: Designing and implementing a cloud-hosted SaaS for data movement and sharing with SlapOS

 

Authors: Walid Saad; Heithem Abbes; Mohamed Jemni; Christophe Cérin

 

Addresses:
LaTICE, Université de Tunis, ESSTT 5, Av. Taha Hussein, B.P. 56, Bab Mnara, Tunis, Tunisia
LaTICE, Université de Tunis, ESSTT 5, Av. Taha Hussein, B.P. 56, Bab Mnara, Tunis, Tunisia
LaTICE, Université de Tunis, ESSTT 5, Av. Taha Hussein, B.P. 56, Bab Mnara, Tunis, Tunisia
LIPN/CNRS UMR 7030, Université de Paris 13, 99, Avenue Jean-Baptiste Clément, 93430 Villetaneuse, France

 

Abstract: For over a decade, the data requirements of e-Science applications increase drastically with the emergence of data-intensive applications. Several tools and frameworks have been developed to manage and handle the big amount of data for the grid platforms. However, the use of these tools by the basic scientist and the grid computing community is not well adopted because of the complexity of the installation and configuration processes. Recently, an open source distributed operating system for clouds emerged, namely SlapOS. The aim of SlapOS is to hide the complexity of IT infrastructures and software deployments from users. In this work, we propose a cloud-hosted data grid using the SlapOS cloud. Through a software as a service (SaaS) solution, users can request and install automatically any data movement and sharing tools like Stork and Bitdew without any intervention of a system administrator. The entire solution is now running in production into the SlapOS cloud at Paris 13 University. Intensive experiments have been conducted on the Grid '5000 testbed to validate our approach.

 

Keywords: data-intensive applications; big data management; software as a service; SaaS; Stork; Bitdew; SlapOS; grid tools; cloud federation; software integration; cloud computing; data movement; data sharing; open source; distributed operating system; grid computing.

 

DOI: 10.1504/IJBDI.2014.063860

 

Int. J. of Big Data Intelligence, 2014 Vol.1, No.1/2, pp.18 - 35

 

Submission date: 12 Dec 2013
Date of acceptance: 27 Mar 2014
Available online: 23 Jul 2014

 

 

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