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A framework for dynamically generating predictive models of workflow execution

Lookup NU author(s): Dr Hugo Hiden, Dr Simon Woodman, Professor Paul WatsonORCiD


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The ability to accurately predict the performance of soft- ware components executing within a Cloud environment is an area of intense interest to many researchers. The avail- ability of an accurate prediction of the time taken for a piece of code to execute would be beneficial for both planning and cost optimisation purposes. To that end, this paper proposes a performance data capture and modelling architecture that can be used to generate models of code execution time that are dynamically updated as additional performance data is collected. To demonstrate the utility of this approach, the workflow engine within the e-Science Central Cloud platform has been instrumented to capture execution data with a view to generating predictive models of workflow performance. Models have been generated for both simple and more com- plex workflow components operating on local hardware and within a virtualised Cloud environment and the ability to generate accurate performance predictions given a number of caveats is demonstrated.

Publication metadata

Author(s): Hiden H, Woodman S, Watson P

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science

Year of Conference: 2013

Pages: 77-87

Date deposited: 10/04/2014

ISSN: 9781450325028

Publisher: ACM Digital Library


DOI: 10.1145/2534248.2534256