Main Conference Sessions
Forecasting Duration Intervals of Scientific Workflow Activities Based on Time-Series Patterns
Authors
- Xiao Liu, FICT, Swinburne University of Technology
- Jinjun Chen, FICT, Swinburne University of Technology
- Ke Liu, FICT, Swinburne University of Technology
- Yun Yang, FICT, Swinburne University of Technology
Abstract
In scientific workflow systems, time-related functionalities such as workflow scheduling and temporal verification normally require effective forecasting of activity durations due to the dynamic nature of underlying resources such as Web or Grid services. However, most existing strategies cannot handle the problems of limited sample size and frequent turning points, which are typical for the duration series of scientific workflow activities. To address such problems, we propose a novel pattern-based time-series forecasting strategy which utilizes a periodical sampling plan to build representative duration series, and then conducts time-series segmentation to discover the smallest pattern set and predicts the activity duration intervals with pattern matching results. The simulation experiment demonstrates the excellent performance of our segmentation algorithm and further shows the effectiveness of our strategy in the prediction of activity duration intervals, especially the ability of handling turning points.
Date and Time
Friday, December 12, 10:15 a.m. to 10:45 a.m.
Room Number
206
