Science Clouds: Early Experiences in Cloud Computing for Scientific Applications

By K. Keahey, R. Figueiredo, José A. B. Fortes, T. Freeman, Maurício O. Tsugawa. 2008

353 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b62f2ebc8d35e52c1f3b7bd687ebe8274e8a8f1e

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Citations in the corpus

Runtime measurements in the cloud (2010, background): “There also exist a number of studies comparing the actual performance difference between cloud computing and traditional high performance clusters so as to evaluate the applicability of cloud computing to scientific applications [19, 23].”
Comparison of Several Cloud Computing Platforms (2009, background): “And all the resources on the cloud are transplant to the users, that is, users can dynamically lease physical or virtual resources and don’t need to know the exact places of the resources existed.”
Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud (2010, methodology): “The High-Energy and Nulclear Physics (HENP) STAR experiment has examined the costs and challenges associated with running their analysis application on the EC2 cloud [3], [21], [4].”
Improved cost-based algorithm for task scheduling in cloud computing (2010, background): “Cloud computing delivers three kinds of services : Infrastructure as a service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) [4].”
High-Performance Cloud Computing: A View of Scientific Applications (2009, methodology): “One of the first cloud-based infrastructures for computational science, Science Cloud [12], has been already deployed by the joint efforts of the University of Chicago, the University of Illinois, Purdue University, and Masaryk University.”
A data placement strategy in scientific cloud workflows (2010, unknown): “And the Nimbus [12] toolkit can directly turn a cluster into a cloud and it has already been used to build a cloud for scientific applications.”