Performance Evaluation of the Karma Provenance Framework for Scientific Workflows

Yogesh L. Simmhan, Beth Plale, Dennis Gannon, and Suresh Marru

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 article 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.

Details

Publication typeInproceedings
Published inInternational Provenance and Annotation Workshop (IPAW)
URLwww.springerlink.com/content/5k27157840017367/fulltext.pdf
Pages222–236
Volume4145
SeriesLecture Notes in Computer Science (LNCS)
ISBN978-3540463023
AddressBerlin, Germany
PublisherSpringer
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