Application scientists need to track the status of their workflows in real time, detect execution anomalies automatically, and perform troubleshooting -- ...

Application scientists need to track the status of their workflows in real time, detect execution anomalies automatically, and perform troubleshooting – without ...

Application scientists need to track the status of their workflows in real time, detect execution anomalies automatically, and perform troubleshooting--without ...

Nov 13, 2024 · One major challenge for anomaly detection methods is the high dimensionality of data generated in large-scale cloud computing environments [14] ...

May 13, 2024 · Workflows have played a key role in advancing science by allowing scientists to orchestrate compu- tations at massive scales, from cancer ...

This paper presents a set of methods and an implemented prototype for anomaly detection in cloud-based infrastructures with specific focus on the deployment of ...

May 30, 2023 · We develop graph neural networks (GNNs) to learn patterns in the DAGs and to detect anomalies at the node (job) and graph (workflow) levels.

In this work, we propose the use of Hierarchical Temporal Memory (HTM) to detect performance anomalies on real-time infrastructure metrics collected by ...

Online fault and anomaly detection for large-scale scientific workflows. In International Conference on High Performance. Computing and Communications, pages ...

In this work, we address the challenges of online detection of anomalies while executing scientific workflow applications on networked clouds. Using an ...