Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow
This work addresses time series modeling for scientific workflows, offering incremental improvements in prediction accuracy for file transfer tasks.
The paper tackled the problem of predicting file transfer times in a scientific workflow by extracting dynamic information from recent past events, reducing prediction error by 12% compared to static models, and by using an application-specific technique to reduce error by 44%.
In modeling time series data, we often need to augment the existing data records to increase the modeling accuracy. In this work, we describe a number of techniques to extract dynamic information about the current state of a large scientific workflow, which could be generalized to other types of applications. The specific task to be modeled is the time needed for transferring a file from an experimental facility to a data center. The key idea of our approach is to find recent past data transfer events that match the current event in some ways. Tests showed that we could identify recent events matching some recorded properties and reduce the prediction error by about 12% compared to the similar models with only static features. We additionally explored an application specific technique to extract information about the data production process, and was able to reduce the average prediction error by 44%.