Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries

Rina Singh; Jeffrey A. Graves; Valentine Anantharaj; Sreenivas R. Sukumar

doi:10.1109/BigData47090.2019.9006223

Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries

Rina Singh, Jeffrey A. Graves, Valentine Anantharaj, Sreenivas R. Sukumar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Workflow engines used to script scientific experiments involving numerical simulation, data analysis, instruments, edge sensors, and artificial intelligence have to deal with the complexities of hardware, software, resource availability, and the collaborative nature of science. In this paper, we survey workflow engines used in data-intensive and compute-intensive discovery pipelines from scientific disciplines such as astronomy, high energy physics, earth system science, bio-medicine, and material science and present a qualitative analysis of their respective capabilities. We compare 5 popular workflow engines and their differentiated approach to job orchestration, job launching, data management and provenance, security authentication, ease-ofuse, workflow description, and scripting semantics. The comparisons presented in this paper allow practitioners to choose the appropriate engine for their scientific experiment and lead to recommendations for future work.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4553-4560
Number of pages	8
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9006223
State	Published - Dec 2019
Externally published	Yes
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: Dec 9 2019 → Dec 12 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	12/9/19 → 12/12/19

Funding

This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doepublic-access-plan). This project was partially funded by the Laboratory Director’s Research and Development fund. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy.

Funders	Funder number
UT-Battelle, LLC
U.S. Department of Energy
Office of Science

Keywords

Converged Workloads
Data Intensive Discoveries
End-to-End Workflows
Scientific Experiments
Scientific Workflows
Workflow Engines

Access to Document

10.1109/BigData47090.2019.9006223

Cite this

Singh, R., Graves, J. A., Anantharaj, V., & Sukumar, S. R. (2019). Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 4553-4560). Article 9006223 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006223

Singh, Rina ; Graves, Jeffrey A. ; Anantharaj, Valentine et al. / Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 4553-4560 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

@inproceedings{e6a273c6f7e0432e9feda5daeff2d479,

title = "Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries",

abstract = "Workflow engines used to script scientific experiments involving numerical simulation, data analysis, instruments, edge sensors, and artificial intelligence have to deal with the complexities of hardware, software, resource availability, and the collaborative nature of science. In this paper, we survey workflow engines used in data-intensive and compute-intensive discovery pipelines from scientific disciplines such as astronomy, high energy physics, earth system science, bio-medicine, and material science and present a qualitative analysis of their respective capabilities. We compare 5 popular workflow engines and their differentiated approach to job orchestration, job launching, data management and provenance, security authentication, ease-ofuse, workflow description, and scripting semantics. The comparisons presented in this paper allow practitioners to choose the appropriate engine for their scientific experiment and lead to recommendations for future work.",

keywords = "Converged Workloads, Data Intensive Discoveries, End-to-End Workflows, Scientific Experiments, Scientific Workflows, Workflow Engines",

author = "Rina Singh and Graves, {Jeffrey A.} and Valentine Anantharaj and Sukumar, {Sreenivas R.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9006223",

language = "English",

series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4553--4560",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",

booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

}

Singh, R, Graves, JA, Anantharaj, V & Sukumar, SR 2019, Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9006223, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 4553-4560, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 12/9/19. https://doi.org/10.1109/BigData47090.2019.9006223

Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries. / Singh, Rina; Graves, Jeffrey A.; Anantharaj, Valentine et al.
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 4553-4560 9006223 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries

AU - Singh, Rina

AU - Graves, Jeffrey A.

AU - Anantharaj, Valentine

AU - Sukumar, Sreenivas R.

PY - 2019/12

Y1 - 2019/12

N2 - Workflow engines used to script scientific experiments involving numerical simulation, data analysis, instruments, edge sensors, and artificial intelligence have to deal with the complexities of hardware, software, resource availability, and the collaborative nature of science. In this paper, we survey workflow engines used in data-intensive and compute-intensive discovery pipelines from scientific disciplines such as astronomy, high energy physics, earth system science, bio-medicine, and material science and present a qualitative analysis of their respective capabilities. We compare 5 popular workflow engines and their differentiated approach to job orchestration, job launching, data management and provenance, security authentication, ease-ofuse, workflow description, and scripting semantics. The comparisons presented in this paper allow practitioners to choose the appropriate engine for their scientific experiment and lead to recommendations for future work.

AB - Workflow engines used to script scientific experiments involving numerical simulation, data analysis, instruments, edge sensors, and artificial intelligence have to deal with the complexities of hardware, software, resource availability, and the collaborative nature of science. In this paper, we survey workflow engines used in data-intensive and compute-intensive discovery pipelines from scientific disciplines such as astronomy, high energy physics, earth system science, bio-medicine, and material science and present a qualitative analysis of their respective capabilities. We compare 5 popular workflow engines and their differentiated approach to job orchestration, job launching, data management and provenance, security authentication, ease-ofuse, workflow description, and scripting semantics. The comparisons presented in this paper allow practitioners to choose the appropriate engine for their scientific experiment and lead to recommendations for future work.

KW - Converged Workloads

KW - Data Intensive Discoveries

KW - End-to-End Workflows

KW - Scientific Experiments

KW - Scientific Workflows

KW - Workflow Engines

UR - http://www.scopus.com/inward/record.url?scp=85081335274&partnerID=8YFLogxK

U2 - 10.1109/BigData47090.2019.9006223

DO - 10.1109/BigData47090.2019.9006223

M3 - Conference contribution

AN - SCOPUS:85081335274

T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 4553

EP - 4560

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Singh R, Graves JA, Anantharaj V, Sukumar SR. Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 4553-4560. 9006223. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). doi: 10.1109/BigData47090.2019.9006223

Evaluating Scientific Workflow Engines for Data and Compute Intensive Discoveries

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this