Abstract

Fundamental progress towards reliable modern science depends on accurate anomaly detection during application execution. In this paper, we suggest a novel approach to tackle this problem by applying Convolutional Neural Network (CNN) classification methods to high-resolution visualizations that capture the end-to-end workflow execution timeline. Subtle differences in the timeline reveal information about the performance of the application and infrastructure’s components. We collect 1000 traces of a scientific workflow’s executions. We explore and evaluate the performance of CNNs trained from scratch and pre-trained on ImageNet [7]. Our initial results are promising with over 90% accuracy.

Metadata

publication

Practice and Experience in Advanced Research Computing, 1-5, 2021

year

2021

publication date

2021/7/17

authors

Patrycja Krawczuk, George Papadimitriou, Shubham Nagarkar, Mariam Kiran, Anirban Mandal, Ewa Deelman

link

https://dl.acm.org/doi/abs/10.1145/3437359.3465597

resource_link

https://dl.acm.org/doi/pdf/10.1145/3437359.3465597

book

Practice and Experience in Advanced Research Computing 2021: Evolution Across All Dimensions

pages

1-5