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A computational workflow, also known as workflow, consists of tasks that mustbe executed in a specific order to attain a specific goal. Often, in fieldssuch as biology, chemistry, physics, and data science, among others, theseworkflows are complex and are executed in large-scale, distributed, andheterogeneous computing environments that are prone to failures and performancedegradations. Therefore, anomaly detection for workflows is an importantparadigm that aims to identify unexpected behavior or errors in workflowexecution. This crucial task to improve the reliability of workflow executionsmust be assisted by machine learning-based techniques. However, suchapplication is limited, in large part, due to the lack of open datasets andbenchmarking. To address this gap, we make the following contributions in thispaper: (1) we systematically inject anomalies and collect raw execution logsfrom workflows executing on distributed infrastructures; (2) we summarize thestatistics of new datasets, as well as a set of open datasets, and provideinsightful analyses; (3) we benchmark unsupervised anomaly detection techniquesby converting workflows into both tabular and graph-structured data. Ourfindings allow us to examine the effectiveness and efficiencies of thebenchmark methods and identify potential research opportunities for improvementand generalization. The dataset and benchmark code are available online withMIT License for public usage.

Flow-Bench: A Dataset for Computational Workflow Anomaly Detection