A Taxonomy and Survey of Scientific Computing in the Cloud

Amelie Chi Zhou 1 Bingsheng He 2 Shadi Ibrahim 1
1 KerData - Scalable Storage for Clouds and Beyond
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
Abstract : Cloud computing has evolved as a popular computing infrastructure for many applications. With (big) data acquiring a crucial role in eScience, efforts have been made recently exploring how to efficiently develop and deploy scientific applications on the unprecedentedly scalable cloud infrastructures. We review recent efforts in developing and deploying scientific computing applications in the cloud. In particular, we introduce a taxonomy specifically designed for scientific computing in the cloud, and further review the taxonomy with four major kinds of science applications, including life sciences, physics sciences, social and humanities sciences, and climate and earth sciences. Due to the large data size in most scientific applications, the performance of I/O operations can greatly affect the overall performance of the applications. We notice that, the dynamic I/O performance of the cloud has made the resource provisioning an important and complex problem for scientific applications in the cloud. We present our efforts on improving the resource provisioning efficiency and effectiveness of scientific applications in the cloud. Finally, we present the open problems for developing the next-generation eScience applications and systems in the cloud and conclude this chapter.
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Chapitre d'ouvrage
Big Data: Principles and Paradigms, Morgan Kaufmann, 2016, eScience and Big Data Workflows in Clouds: A Taxonomy and Survey, 978-0-12-805394-2
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Amelie Chi Zhou, Bingsheng He, Shadi Ibrahim. A Taxonomy and Survey of Scientific Computing in the Cloud. Big Data: Principles and Paradigms, Morgan Kaufmann, 2016, eScience and Big Data Workflows in Clouds: A Taxonomy and Survey, 978-0-12-805394-2. 〈hal-01346745〉

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