WorkflowHub: Community Framework for Enabling Scientific Workflow Research and Development -- Technical Report

TL;DR

WorkflowHub is a community framework that enables analysis, realistic synthetic trace generation, and simulation of scientific workflows, improving upon previous tools for research and development in workflow systems.

Contribution

It introduces WorkflowHub, a comprehensive framework that addresses limitations of existing tools by providing realistic trace generation and scalable simulation capabilities.

Findings

Generated synthetic traces closely resemble real-world workflows.

WorkflowHub can produce larger-scale workflow traces than previous tools.

Simulated executions match actual workflow performance metrics.

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 at 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 resembles 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.