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Lookup NU author(s): Professor Paolo Missier
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software stack, or even only a new dataset with some notably different properties is often challenging. Despite the structured definition of the steps (tasks) and their interdependencies during a complex data analysis in the DAW specification, relevant assumptions may remain unspecified and implicit. Such hidden assumptions often lead to crashing tasks without a reasonable error message, poor performance in general, non-terminating executions, or silent wrong results of the DAW, to name only a few possible consequences. Searching for the causes of such errors and drawbacks in a distributed compute cluster managed by a complex infrastructure stack, where DAWs for large datasets typically are executed, can be tedious and time-consuming. We propose validity constraints (VCs) as a new concept for DAW languages to alleviate this situation. A VC is a constraint specifying logical conditions that must be fulfilled at certain times for DAW executions to be valid. When defined together with a DAW, VCs help to improve the portability, adaptability, and reusability of DAWs by making implicit assumptions explicit. Once specified, VCs can be controlled automatically by the DAW infrastructure, and violations can lead to meaningful error messages and graceful behavior (e.g., termination or invocation of repair mechanisms). We provide a broad list of possible VCs, classify them along multiple dimensions, and compare them to similar concepts one can find in related fields. We also provide a proof-of-concept implementation for the workflow system Nextflow.
Author(s): Schintke F, Belhajjame K, De Mecquenem N, Frantz D, Guarino VE, Hilbrich M, Lehmann F, Missier P, Sattler R, Sparka JA, Speckhard DT, Stolte H, Vu AD, Leser U
Publication type: Article
Publication status: Published
Journal: Future Generation Computer Systems
Year: 2024
Volume: 157
Pages: 82-97
Print publication date: 01/08/2024
Online publication date: 25/03/2024
Acceptance date: 22/03/2024
Date deposited: 08/04/2024
ISSN (print): 0167-739X
ISSN (electronic): 1872-7115
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.future.2024.03.037
DOI: 10.1016/j.future.2024.03.037
Data Access Statement: No data was used for the research described in the article.
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