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
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 submi
ssion and enables users to share data securely using storage access keys across different organizations.
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