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Authors: Ali Gholami 1 ; Gert Svensson 1 ; Erwin Laure 1 ; Matthias Eickhoff 2 and Götz Brasche 2

Affiliations: 1 Royal Institute of Technology, Sweden ; 2 Microsoft Research – Advanced Technology Labs (ATL) Europe, Germany

ISBN: 978-989-8565-52-5

Keyword(s): Cloud Computing, SPM, Microsoft Azure, e-Science as a Service, Brain Imaging, FMRI.

Related Ontology Subjects/Areas/Topics: Cloud Application Architectures ; Cloud Application Scalability and Availability ; Cloud Computing ; Cloud Middleware Frameworks ; Platforms and Applications

Abstract: The use of cloud computing as a new paradigm has become a reality. Cloud computing leverages the use of on-demand CPU power and storage resources while eliminating the cost of commodity hardware ownership. Cloud computing is now gaining popularity among many different organizations and commercial sectors. In this paper, we present the scalable brain image analysis (ScaBIA) architecture, a new model to run statistical parametric analysis (SPM) jobs using cloud computing. SPM is one of the most popular toolkits in neuroscience for running compute-intensive brain image analysis tasks. However, issues such as sharing raw data and results, as well as scalability and performance are major bottlenecks in the “single PC”-execution model. In this work, we describe a prototype using the generic worker (GW), an e-Science as a service middleware, on top of Microsoft Azure to run and manage the SPM tasks. The functional prototype shows that ScaBIA provides a scalable framework for multi-job submis sion and enables users to share data securely using storage access keys across different organizations. (More)

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Paper citation in several formats:
Gholami, A.; Gholami, A.; Svensson, G.; Laure, E.; Eickhoff, M. and Brasche, G. (2013). ScaBIA: Scalable Brain Image Analysis in the Cloud .In Proceedings of the 3rd International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-8565-52-5, pages 329-336. DOI: 10.5220/0004358003290336

@conference{closer13,
author={Ali Gholami. and Ali Gholami. and Gert Svensson. and Erwin Laure. and Matthias Eickhoff. and Götz Brasche.},
title={ScaBIA: Scalable Brain Image Analysis in the Cloud },
booktitle={Proceedings of the 3rd International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2013},
pages={329-336},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004358003290336},
isbn={978-989-8565-52-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - ScaBIA: Scalable Brain Image Analysis in the Cloud
SN - 978-989-8565-52-5
AU - Gholami, A.
AU - Gholami, A.
AU - Svensson, G.
AU - Laure, E.
AU - Eickhoff, M.
AU - Brasche, G.
PY - 2013
SP - 329
EP - 336
DO - 10.5220/0004358003290336

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