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Title

A novel statistical time-series pattern based interval forecasting strategy for activity durations in workflow systems

Author(s)

Liu, Xiao;  Ni, Zhiwei;  Yuan, Dong;  Jiang, Yuanchun;  Wu, Zhangjun;  Chen, Jinjun;  Yang, Yun

Abstract

Forecasting workflow activity durations is of great importance to support satisfactory QoS in workflow systems. Traditionally, a workflow system is often designed to facilitate the process automation in a specific application domain where activities are of the similar nature. Hence, a particular forecasting strategy is employed by a workflow system and applied uniformly to all its workflow activities. However, with newly emerging requirement to serve as a type of middleware services for high performance computing infrastructures such as grid and cloud computing, more and more workflow systems are designed to be general purpose to support workflow applications from many different domains. Due to such a problem, the forecasting strategies in workflow systems must adapt to different workflow applications which are normally executed repeatedly such as data/computation intensive scientific applications (mainly with long-duration activities) and instance intensive business applications (mainly with short-duration activities). In this paper, with a systematic analysis of the above issues, we propose a novel statistical time-series pattern based interval forecasting strategy which has two different versions, a complex version for long-duration activities and a simple version for short-duration activities. The strategy consists of four major functional components: duration series building, duration pattern recognition, duration pattern matching and duration interval forecasting. Specifically, a novel hybrid non-linear time-series segmentation algorithm is designed to facilitate the discovery of duration-series patterns. The experimental results on real world examples and simulated test cases demonstrate the excellent performance of our strategy in the forecasting of activity duration intervals for both long-duration and short-duration activities in comparison to some representative time-series forecasting strategies in traditional workflow systems.

Publication type

Journal article

Research centre

Swinburne University of Technology. Faculty of Information and Communication Technologies

Source

Journal of Systems and Software, Vol. 84, no. 3 (Mar 2011), pp. 354-376

Publication year

2011

FOR Code(s)

0803 Computer Software;  0806 Information Systems

Keyword(s)

Activity duration;  Interval forecasting;  Statistical time series;  Time-series patterns;  Workflow systems

Publisher

Elsevier

ISSN

0164-1212

Publisher URL

http://dx.doi.org/10.1016/j.jss.2010.11.927

Copyright

Copyright © 2010 Elsevier Inc. The accepted manuscript is reproduced in accordance with the copyright policy of the publisher.

Full text

Peer reviewed