Name
Abstract | ||
|---|---|---|
Large computing systems including clusters, clouds, and grids, provide high-performance capabilities that can be utilized for scientific applications. As the ubiquity of these systems increases and the scope of analysis performed on them expand, there is a growing need for applications that do not require users to learn the details of high-performance computing, and are flexible and adaptive to accommodate the best time-to-solution. In this paper we introduce a new adaptive capability for the MeDICi middleware and describe the applicability of this design to a scientific workflow application for biology. This adaptive framework provides a programming model for implementing a workflow using high-performance systems and enables the compute capabilities at one site to automatically analyze data being generated at another site. This adaptive design improves overall time-to-solution by moving the data analysis task to the most appropriate resource dynamically, automatically reacting to failures and load fluctuations. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IRI.2010.5558934 | Information Reuse and Integration |
Keywords | Field | DocType |
data analysis,middleware,ubiquitous computing,MeDICi middleware,adaptive middleware framework,data analysis,extreme scales,scientific computing,Middleware,adaptive,data intensive computing,scientific workflow,service oriented architectures | Middleware,Programming paradigm,Data-intensive computing,Computer science,Server,Ubiquitous computing,Workflow application,Workflow,Service-oriented architecture,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-4244-8097-5 | 2 | 0.38 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arzu Gosney | 1 | 2 | 0.38 |
| Christopher S. Oehmen | 2 | 2 | 0.38 |
| Adam Wynne | 3 | 68 | 9.41 |
| Justin Almquist | 4 | 42 | 5.88 |