  Modern scientific workflows are data-driven and are often executed on
distributed, heterogeneous, high-performance computing infrastructures.
Anomalies and failures in the workflow 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 the anomalous TCP packets. X-FLASH leverages XGBoost as an
ensemble model and couples XGBoost with a sequential optimizer, FLASH, borrowed
from search-based Software Engineering to learn the optimal model parameters.
X-FLASH found configurations that outperformed the existing approach up to
28\%, 29\%, and 40\% relatively for F-measure, G-score, and recall in less than
30 evaluations. From (1) large improvement and (2) simple tuning, we recommend
future research to have additional tuning study as a new standard, at least in
the area of scientific workflow anomaly detection.
