Scientific workflows are commonplace in eScience applications. Yet,
the lack of integrated support for data models, including streaming
data, structured collections and files, is limiting the ability of
workflows to support emerging applications in energy informatics
that are stream oriented. This is compounded by the absence of Cloud
data services that support reliable and performant streams. In this
paper, we propose and present a scientific workflow framework that
supports streams as first-class data, and is optimized for performant
and reliable execution across desktop and Cloud platforms. The workflow
framework features and its empirical evaluation on a private Eucalyptus
Cloud are presented.
%0 Conference Paper
%1 Zinn:ccgrid:2011
%A Zinn, Daniel
%A Hart, Quinn
%A McPhillips, Timothy M.
%A Ludäscher, Bertram
%A Simmhan, Yogesh
%A Giakkoupis, Michail
%A Prasanna, Viktor K.
%B International Symposium on Cluster, Cloud and Grid Computing (CCGRID)
%D 2011
%I IEEE
%K cloud, escience grid, peer reviewed, smart streaming, usc,
%P 235-244
%R 10.1109/CCGrid.2011.74
%T Towards Reliable, Performant Workflows for Streaming-Applications
on Cloud Platforms
%U http://ceng.usc.edu/~simmhan/pubs/zinn-ccgrid-2011.pdf
%X Scientific workflows are commonplace in eScience applications. Yet,
the lack of integrated support for data models, including streaming
data, structured collections and files, is limiting the ability of
workflows to support emerging applications in energy informatics
that are stream oriented. This is compounded by the absence of Cloud
data services that support reliable and performant streams. In this
paper, we propose and present a scientific workflow framework that
supports streams as first-class data, and is optimized for performant
and reliable execution across desktop and Cloud platforms. The workflow
framework features and its empirical evaluation on a private Eucalyptus
Cloud are presented.
@inproceedings{Zinn:ccgrid:2011,
abstract = {Scientific workflows are commonplace in eScience applications. Yet,
the lack of integrated support for data models, including streaming
data, structured collections and files, is limiting the ability of
workflows to support emerging applications in energy informatics
that are stream oriented. This is compounded by the absence of Cloud
data services that support reliable and performant streams. In this
paper, we propose and present a scientific workflow framework that
supports streams as first-class data, and is optimized for performant
and reliable execution across desktop and Cloud platforms. The workflow
framework features and its empirical evaluation on a private Eucalyptus
Cloud are presented.},
added-at = {2014-08-13T04:08:36.000+0200},
author = {Zinn, Daniel and Hart, Quinn and McPhillips, Timothy M. and Lud{\"a}scher, Bertram and Simmhan, Yogesh and Giakkoupis, Michail and Prasanna, Viktor K.},
biburl = {https://www.bibsonomy.org/bibtex/2aeb76c75913f3294eadbd72361f77517/simmhan},
booktitle = {International Symposium on Cluster, Cloud and Grid Computing (CCGRID)},
doi = {10.1109/CCGrid.2011.74},
interhash = {cd9f3a9ea529e77a58e0d6e0bbe3874d},
intrahash = {aeb76c75913f3294eadbd72361f77517},
keywords = {cloud, escience grid, peer reviewed, smart streaming, usc,},
month = May,
note = {[CORE A]},
owner = {Simmhan},
pages = {235-244},
publisher = {IEEE},
timestamp = {2014-08-13T04:08:36.000+0200},
title = {Towards Reliable, Performant Workflows for Streaming-Applications
on Cloud Platforms},
url = {http://ceng.usc.edu/~simmhan/pubs/zinn-ccgrid-2011.pdf},
year = 2011
}