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1 Papers

ScaBIA: Scalable Brain Image Analysis in the Cloud

Topics: Cloud Middleware Frameworks; Cloud Application Architectures; Cloud Application Scalability and Availability

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

Digital Object Identifier: 10.5220/0004358003290336

Proceedings of CLOSER 2013, ISBN: 978-989-8565-52-5

Quick 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 submission and enables users to share data securely using storage access keys across different organizations.

Related Ontology Subjects/Areas/Topics (5)