Abstract

Modern scientific workflows are data-driven and are often executed on distributed, heterogeneous, high-performance computing infrastructures. Anomalies and failures in the work-flow execution cause loss of scientific productivity and inefficient use of the infrastructure. Hence, detecting, diagnosing, and mitigating these anomalies are immensely important for reliable and performant scientific workflows. Since these workflows rely heavily on high-performance network transfers that require strict QoS constraints, accurately detecting anomalous network performance is crucial to ensure reliable and efficient workflow execution. To address this challenge, we have developed X-FLASH, a network anomaly detection tool for faulty TCP workflow transfers. X-FLASH incorporates novel hyperparameter tuning and data mining approaches for improving the performance of the machine learning algorithms to accurately classify …

Date

2021

Authors

Huy Tu, George Papadimitriou, Mariam Kiran, Cong Wang, Anirban Mandal, Ewa Deelman, Tim Menzies

Conference

2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)

Pages

1-12

Publisher

IEEE