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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 Watson

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Abstract

The ability to accurately predict the performance of software components executing within a Cloud environment is an area of intense interest to many researchers. The availability 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 complex 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: Report

Publication status: Published

Series Title: School of Computing Science Technical Report Series

Year: 2013

Pages: 11

Print publication date: 01/10/2013

Source Publication Date: October 2013

Report Number: 1396

Institution: School of Computing Science, University of Newcastle upon Tyne

Place Published: Newcastle upon Tyne

URL: http://www.cs.ncl.ac.uk/publications/trs/papers/1396.pdf


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