Integrity Protection for Scientific Workflow Data: Motivation and Initial Experiences. Rynge, M., Poehlman, W., L., Feltus, F., A., Vahi, K., Deelman, E., Mandal, A., Baldin, I., Bhide, O., Heiland, R., Welch, V., & Hill, R. In Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC '19), pages 1-8, 2019. Association for Computing Machinery (ACM). ![Integrity Protection for Scientific Workflow Data: Motivation and Initial Experiences [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex With the continued rise of scientific computing and the enormous increases in the size of data being processed, scientists must consider whether the processes for transmitting and storing data sufficiently assure the integrity of the scientific data. When integrity is not preserved, computations can fail and result in increased computational cost due to reruns, or worse, results can be corrupted in a manner not apparent to the scientist and produce invalid science results. Technologies such as TCP checksums, encrypted transfers, checksum validation, RAID and erasure coding provide integrity assurances at different levels, but they may not scale to large data sizes and may not cover a workflow from end-to-end, leaving gaps in which data corruption can occur undetected. In this paper we explore an approach of assuring data integrity - considering either malicious or accidental corruption - for workflow executions orchestrated by the Pegasus Workflow Management System. To validate our approach, we introduce Chaos Jungle - a toolkit providing an environment for validating integrity verification mechanisms by allowing researchers to introduce a variety of integrity errors during data transfers and storage. In addition to controlled experiments with Chaos Jungle, we provide analysis of integrity errors that we encountered when running production workflows.
@inproceedings{
title = {Integrity Protection for Scientific Workflow Data: Motivation and Initial Experiences},
type = {inproceedings},
year = {2019},
pages = {1-8},
publisher = {Association for Computing Machinery (ACM)},
id = {6d46f628-0077-3408-b9f8-0c6201e5e7f8},
created = {2019-10-01T17:21:14.292Z},
accessed = {2019-09-11},
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last_modified = {2020-05-11T14:43:34.750Z},
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citation_key = {Rynge2019},
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abstract = {With the continued rise of scientific computing and the enormous increases in the size of data being processed, scientists must consider whether the processes for transmitting and storing data sufficiently assure the integrity of the scientific data. When integrity is not preserved, computations can fail and result in increased computational cost due to reruns, or worse, results can be corrupted in a manner not apparent to the scientist and produce invalid science results. Technologies such as TCP checksums, encrypted transfers, checksum validation, RAID and erasure coding provide integrity assurances at different levels, but they may not scale to large data sizes and may not cover a workflow from end-to-end, leaving gaps in which data corruption can occur undetected. In this paper we explore an approach of assuring data integrity - considering either malicious or accidental corruption - for workflow executions orchestrated by the Pegasus Workflow Management System. To validate our approach, we introduce Chaos Jungle - a toolkit providing an environment for validating integrity verification mechanisms by allowing researchers to introduce a variety of integrity errors during data transfers and storage. In addition to controlled experiments with Chaos Jungle, we provide analysis of integrity errors that we encountered when running production workflows.},
bibtype = {inproceedings},
author = {Rynge, Mats and Poehlman, William L. and Feltus, F. Alex and Vahi, Karan and Deelman, Ewa and Mandal, Anirban and Baldin, Ilya and Bhide, Omkar and Heiland, Randy and Welch, Von and Hill, Raquel},
doi = {10.1145/3332186.3332222},
booktitle = {Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC '19)}
}
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