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WorkflowHub: Community Framework for Enabling Scientific Workflow Research and Development

Authors: Rafael Ferreira da Silva; Loïc Pottier; Tainã Coleman; Ewa Deelman; Henri Casanova;

WorkflowHub: Community Framework for Enabling Scientific Workflow Research and Development

Abstract

Scientific workflows are a cornerstone of modern scientific computing. They are used to describe complex computational applications that require efficient and robust management of large volumes of data, which are typically stored/processed on heterogeneous, distributed resources. The workflow research and development community has employed a number of methods for the quantitative evaluation of existing and novel workflow algorithms and systems. In particular, a common approach is to simulate workflow executions. In previous work, we have presented a collection of tools that have been used for aiding research and development activities in the Pegasus project, and that have been adopted by others for conducting workflow research. Despite their popularity, there are several shortcomings that prevent easy adoption, maintenance, and consistency with the evolving structures and computational requirements of production workflows. In this work, we present WorkflowHub, a community framework that provides a collection of tools for analyzing workflow execution traces, producing realistic synthetic workflow traces, and simulating workflow executions. We demonstrate the realism of the generated synthetic traces by comparing simulated executions of these traces with actual workflow executions. We also contrast these results with those obtained when using the previously available collection of tools. We find that our framework not only can be used to generate representative synthetic workflow traces (i.e., with workflow structures and task characteristics distributions that resemble those in traces obtained from real-world workflow executions), but can also generate representative workflow traces at larger scales than that of available workflow traces.

Subjects by Vocabulary

Microsoft Academic Graph classification: Consistency (database systems) Workflow Development (topology) business.industry Computer science Software engineering business Task (project management)

16 references, page 1 of 2

[1] D. G. Amalarethinam and G. J. Mary. Dagen-a tool to generate arbitrary directed acyclic graphs used for multiprocessor scheduling. International Journal of Research and Reviews in Computer Science, 2(3):782, 2011.

[2] M. A. Amer and R. Lucas. Evaluating workow tools with sdag. In 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, pages 54{63. IEEE, 2012.

[3] P. Amstutz, M. R. Crusoe, N. Tijanic, B. Chapman, J. Chilton, M. Heuer, A. Kartashov, D. Leehr, H. Menager, M. Nedeljkovich, et al. Common work ow language, v1. 0. 2016.

[4] L.-C. Canon, A. K. W. Chang, Y. Robert, and F. Vivien. Scheduling independent stochastic tasks under deadline and budget constraints. The International Journal of High Performance Computing Applications, 34(2):246{264, 2020.

[5] H. Casanova, R. Ferreira da Silva, R. Tanaka, S. Pandey, G. Jethwani, W. Koch, S. Albrecht, J. Oeth, and F. Suter. Developing accurate and scalable simulators of production work ow management systems with wrench. Future Generation Computer Systems, 112:162{175, 2020.

[6] H. Casanova, S. Pandey, J. Oeth, R. Tanaka, F. Suter, and R. Ferreira da Silva. Wrench: A framework for simulating work ow management systems. In 13th Workshop on Work ows in Support of Large-Scale Science (WORKS'18), pages 74{85, 2018.

[7] E. Deelman, T. Peterka, I. Altintas, C. D. Carothers, K. K. van Dam, K. Moreland, M. Parashar, L. Ramakrishnan, M. Taufer, and J. Vetter. The future of scienti c work ows. International Journal of High Performance Computing Applications, 32(1), 4 2017.

[8] E. Deelman, K. Vahi, G. Juve, M. Rynge, S. Callaghan, P. J. Maechling, R. Mayani, W. Chen, R. Ferreira da Silva, M. Livny, and K. Wenger. Pegasus, a work ow management system for science automation. Future Generation Computer Systems, 46(0):17{35, 2015. [OpenAIRE]

[9] E. Deelman, K. Vahi, M. Rynge, R. Mayani, R. Ferreira da Silva, G. Papadimitriou, and M. Livny. The evolution of the pegasus work ow management software. Computing in Science & Engineering, 21(4):22{36, 2019.

conference on cluster, cloud and grid computing, pages 398{407. IEEE, 2010.

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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%