Performance Evaluation of the Karma Provenance Framework for Scientific
Workflows
Y. Simmhan, B. Plale, и D. Gannon. International Provenance and Annotation Workshop (IPAW), том 4145 из Lecture Notes in Computer Science (LNCS), стр. 222-236. Springer Berlin / Heidelberg, (2006)
DOI: 10.1007/11890850_23
Аннотация
Provenance about workflow executions and data derivations in scientific
applications help estimate data quality, track resources, and validate
in silico experiments. The Karma provenance framework provides a
means to collect workflow, process, and data provenance from data-driven
scientific workflows and is used in the Linked Environments for Atmospheric
Discovery (LEAD) project. This paper presents a performance analysis
of the Karma service as compared against the contemporary PReServ
provenance service. Our study finds that Karma scales exceedingly
well for collecting and querying provenance records, showing linear
or sub-linear scaling with increasing number of provenance records
and clients when tested against workloads in the order of 10,000
application-service invocations and over 36 concurrent clients.
%0 Conference Paper
%1 Simmhan:ipaw:2006
%A Simmhan, Yogesh L.
%A Plale, Beth
%A Gannon, Dennis
%B International Provenance and Annotation Workshop (IPAW)
%D 2006
%E Moreau, Luc
%E Foster, Ian
%I Springer Berlin / Heidelberg
%K escience, iu, karma, peer provenance, reviewed workflows,
%P 222-236
%R 10.1007/11890850_23
%T Performance Evaluation of the Karma Provenance Framework for Scientific
Workflows
%V 4145
%X Provenance about workflow executions and data derivations in scientific
applications help estimate data quality, track resources, and validate
in silico experiments. The Karma provenance framework provides a
means to collect workflow, process, and data provenance from data-driven
scientific workflows and is used in the Linked Environments for Atmospheric
Discovery (LEAD) project. This paper presents a performance analysis
of the Karma service as compared against the contemporary PReServ
provenance service. Our study finds that Karma scales exceedingly
well for collecting and querying provenance records, showing linear
or sub-linear scaling with increasing number of provenance records
and clients when tested against workloads in the order of 10,000
application-service invocations and over 36 concurrent clients.
@inproceedings{Simmhan:ipaw:2006,
abstract = {Provenance about workflow executions and data derivations in scientific
applications help estimate data quality, track resources, and validate
in silico experiments. The Karma provenance framework provides a
means to collect workflow, process, and data provenance from data-driven
scientific workflows and is used in the Linked Environments for Atmospheric
Discovery (LEAD) project. This paper presents a performance analysis
of the Karma service as compared against the contemporary PReServ
provenance service. Our study finds that Karma scales exceedingly
well for collecting and querying provenance records, showing linear
or sub-linear scaling with increasing number of provenance records
and clients when tested against workloads in the order of 10,000
application-service invocations and over 36 concurrent clients.},
added-at = {2014-08-13T04:08:36.000+0200},
author = {Simmhan, Yogesh L. and Plale, Beth and Gannon, Dennis},
biburl = {https://www.bibsonomy.org/bibtex/2f7e694d48cb9e068cdccdbb940ef1c07/simmhan},
booktitle = {International Provenance and Annotation Workshop (IPAW)},
doi = {10.1007/11890850_23},
editor = {Moreau, Luc and Foster, Ian},
interhash = {2385d297effbd0beaf3de4ab5619f9d6},
intrahash = {f7e694d48cb9e068cdccdbb940ef1c07},
keywords = {escience, iu, karma, peer provenance, reviewed workflows,},
owner = {ysimmhan},
pages = {222-236},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science (LNCS)},
timestamp = {2014-08-13T04:08:36.000+0200},
title = {Performance Evaluation of the Karma Provenance Framework for Scientific
Workflows},
volume = 4145,
year = 2006
}