WfCommons: A Framework for Enabling Scientific Workflow Research and Development

TL;DR

WfCommons is a comprehensive framework that enhances scientific workflow research by providing tools for analysis, synthetic workflow generation, and simulation, improving realism and scalability over previous tools.

Contribution

It introduces WfCommons, a new framework that addresses limitations of existing tools, enabling realistic, scalable workflow simulation and analysis for scientific computing research.

Findings

Generated workflows closely resemble real-world structures and task distributions.

Synthetic workflows can be scaled larger than existing real workflows.

Framework useful for estimating energy consumption of workflows.

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 works, we have presented a collection of tools that have been adopted by the community for conducting workflow research. Despite their popularity, they suffer from several shortcomings that prevent easy adoption, maintenance, and consistency with the evolving structures and computational requirements of production workflows. In this work, we present WfCommons, a framework that provides a collection of tools for analyzing workflow executions, for producing generators of synthetic workflows, and for simulating workflow executions. We demonstrate the realism of the generated synthetic workflows by comparing their simulated executions to real workflow executions. We also contrast these results with results obtained when using the previously available collection of tools. We find that the workflow generators that are automatically constructed by our framework not only generate representative same-scale workflows (i.e., with structures and task characteristics distributions that resemble those observed in real-world workflows), but also do so at scales larger than that of available real-world workflows. Finally, we conduct a case study to demonstrate the usefulness of our framework for estimating the energy consumption of large-scale workflow executions.