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Title

Forecasting duration intervals of scientific workflow activities based on time-series patterns

Author(s)

Liu, Xiao;  Chen, Jinjun;  Liu, Ke;  Yang, Yun

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 well 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 utilises 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.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Information and Communication Technologies

Source

Proceedings of the 4th IEEE International Conference on eScience (eScience 2008), Indianapolis, Indiana, United States, 07-12 December 2008, pp. 23-30

Publication year

2008

FOR Code(s)

080501 Distributed and Grid Systems;  080699 Information Systems not elsewhere classified

Keyword(s)

Activity durations;  Algorithms;  Duration intervals;  Grid services;  Intelligent networks;  Scientific workflow;  Time-series forecasting;  Workflow scheduling

Publisher

IEEE

ISBN

9780769535357

Publisher URL

http://dx.doi.org/10.1109/eScience.2008.14

Copyright

Copyright © 2008 IEEE. Published version of the paper reproduced here in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Full text

Peer reviewed