Paper Info
Title
MeDiCi-Cloud: A Workflow Infrastructure for Large-scale Scientific Applications
Abstract
Cloud computing is attractive for large-scale scientific applications. However, unlike typical commercial applications, large-scale scientific applications often need to process enormous amount of data in the terabyte or even petabyte range and require special high performance hardware with low latency connections to complete computation in a reasonable amount of time. To address these challenges, we build an infrastructure that can dynamically select high performance computing hardware across institutions and dynamically adapt the computation to the selected resources to achieve high performance. We have also demonstrated the effectiveness of our infrastructure by building a system biology application and an uncertainty quantification application for carbon sequestration, which can efficiently utilize data and computation resources across several institutions.
Year
DOI
Venue
2011
10.1109/UCC.2011.56
UCC
Keywords
Field
DocType
system biology application,high performance,large-scale scientific applications,computation resource,scientific computing,workflow,large-scale scientific application,high performance computing hardware,carbon sequestration,computation resource utilization,natural sciences computing,resource allocation,special high performance hardware,quantification application,low latency connections,typical commercial application,medici-cloud,reasonable amount,enormous amount,cloud computing,workflow infrastructure,data processing,system biology,uncertainty quantification,virtual machine,biomedical imaging,systems biology,low latency
Uncertainty quantification,Supercomputer,Petabyte,Computer science,Terabyte,Resource allocation,Latency (engineering),Workflow,Distributed computing,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-4577-2116-8
0
0.34
References 
Authors
2
4
Name
Order
Citations
PageRank
Jian Yin186197.01
Guang Lin222338.16
Ian Gorton31488134.37
Binh Han4453.74