T
Thomas Fahringer
Researcher at University of Innsbruck
Publications - 324
Citations - 9567
Thomas Fahringer is an academic researcher from University of Innsbruck. The author has contributed to research in topics: Grid & Workflow. The author has an hindex of 45, co-authored 315 publications receiving 9000 citations. Previous affiliations of Thomas Fahringer include University of Vienna.
Papers
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Journal ArticleDOI
Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
TL;DR: The results indicate that the current clouds need an order of magnitude in performance improvement to be useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer and demand.
Journal ArticleDOI
Examining the Challenges of Scientific Workflows
Yolanda Gil,Ewa Deelman,Mark H. Ellisman,Thomas Fahringer,Geoffrey C. Fox,Dennis Gannon,Carole Goble,Miron Livny,Luc Moreau,James D. Myers +9 more
TL;DR: A recent National Science Foundation workshop brought together domain, computer, and social scientists to discuss requirements of future scientific applications and the challenges they present to current workflow technologies.
Proceedings Article
A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing
TL;DR: In this paper, the authors evaluate the performance of the Amazon EC2 platform using micro-benchmarks and kernels and conclude that the current cloud services need an order of magnitude in performance improvement to be useful to the scientific community.
Journal ArticleDOI
Scheduling of scientific workflows in the ASKALON grid environment
TL;DR: This paper evaluates three algorithms namely genetic, HEFT, and simple "myopic" and compares incremental workflow partitioning against the full-graph scheduling strategy and demonstrates that full- graph scheduling with the HEFT algorithm performs best.
Book ChapterDOI
A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing
TL;DR: In this paper, the authors evaluate the performance of the Amazon EC2 platform using micro-benchmarks and kernels and conclude that the current cloud services need an order of magnitude in performance improvement to be useful to the scientific community.