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A framework for dynamically generating predictive models of workflow execution

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Paul WatsonAuthors Info & Claims

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

Pages 77 - 87

https://doi.org/10.1145/2534248.2534256

Published: 17 November 2013 Publication History

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.

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Cited By

McGough AForshaw M(2023)Analysis of Reinforcement Learning for Determining Task Replication in WorkflowsComputer Performance Engineering10.1007/978-3-031-25049-1_8(117-132)Online publication date: 25-Jan-2023
https://doi.org/10.1007/978-3-031-25049-1_8
Zeng WKoutny M(2021)Quantitative Analysis of Opacity in Cloud Computing SystemsIEEE Transactions on Cloud Computing10.1109/TCC.2019.28947689:3(1210-1219)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TCC.2019.2894768
Nadeem FAlghazzawi DMashat AFaqeeh KAlmalaise A(2019)Using Machine Learning Ensemble Methods to Predict Execution Time of e-Science Workflows in Heterogeneous Distributed SystemsIEEE Access10.1109/ACCESS.2019.28999857(25138-25149)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2899985
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A framework for dynamically generating predictive models of workflow execution

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Information & Contributors

Information

Published In

cover image ACM Conferences

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

November 2013

133 pages

ISBN:9781450325028

DOI:10.1145/2534248

Conference Chairs:
Johan Montagnat
CNRS
,
Ian Taylor
Cardiff University

Copyright © 2013 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 November 2013

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Research-article

Funding Sources

SiDE project, the RCUK Digital Economy Research Hub on Social Inclusion through the Digital Economy
Newcastle University

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SC13

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SC13: International Conference for High Performance Computing, Networking, Storage and Analysis

November 17, 2013

Colorado, Denver

Acceptance Rates

WORKS '13 Paper Acceptance Rate 13 of 16 submissions, 81%;

Overall Acceptance Rate 30 of 54 submissions, 56%

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Citations

Cited By

McGough AForshaw M(2023)Analysis of Reinforcement Learning for Determining Task Replication in WorkflowsComputer Performance Engineering10.1007/978-3-031-25049-1_8(117-132)Online publication date: 25-Jan-2023
https://doi.org/10.1007/978-3-031-25049-1_8
Zeng WKoutny M(2021)Quantitative Analysis of Opacity in Cloud Computing SystemsIEEE Transactions on Cloud Computing10.1109/TCC.2019.28947689:3(1210-1219)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TCC.2019.2894768
Nadeem FAlghazzawi DMashat AFaqeeh KAlmalaise A(2019)Using Machine Learning Ensemble Methods to Predict Execution Time of e-Science Workflows in Heterogeneous Distributed SystemsIEEE Access10.1109/ACCESS.2019.28999857(25138-25149)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2899985
Ferreira da Silva RFilgueira RDeelman EPairo-Castineira EOverton IAtkinson M(2019)Using simple PID-inspired controllers for online resilient resource management of distributed scientific workflowsFuture Generation Computer Systems10.1016/j.future.2019.01.015Online publication date: Jan-2019
https://doi.org/10.1016/j.future.2019.01.015
Viil JSrirama S(2018)Framework for automated partitioning and execution of scientific workflows in the cloudThe Journal of Supercomputing10.1007/s11227-018-2296-774:6(2656-2683)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11227-018-2296-7
Singh ARao APurawat SAltintas IMontagnat JTaylor IGesing SSakellariou R(2017)A machine learning approach for modular workflow performance predictionProceedings of the 12th Workshop on Workflows in Support of Large-Scale Science10.1145/3150994.3150998(1-11)Online publication date: 12-Nov-2017
https://dl.acm.org/doi/10.1145/3150994.3150998
Nadeem FAlghazzawi DMashat AFakeeh KAlmalaise AHagras H(2017)Modeling and predicting execution time of scientific workflows in the Grid using radial basis function neural networkCluster Computing10.1007/s10586-017-1018-x20:3(2805-2819)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1007/s10586-017-1018-x
Woodman SHiden HWatson PMontagnat JTaylor I(2015)Workflow provenanceProceedings of the 10th Workshop on Workflows in Support of Large-Scale Science10.1145/2822332.2822341(1-9)Online publication date: 15-Nov-2015
https://dl.acm.org/doi/10.1145/2822332.2822341
McGough AForshaw M(2015)Energy-aware simulation of workflow execution in High Throughput Computing systemsProceedings of the 19th International Symposium on Distributed Simulation and Real Time Applications10.1109/DS-RT.2015.31(25-32)Online publication date: 14-Oct-2015
https://dl.acm.org/doi/10.1109/DS-RT.2015.31
Pietri IJuve GDeelman ESakellariou RMontagnat JTaylor I(2014)A performance model to estimate execution time of scientific workflows on the cloudProceedings of the 9th Workshop on Workflows in Support of Large-Scale Science10.1109/WORKS.2014.12(11-19)Online publication date: 16-Nov-2014
https://dl.acm.org/doi/10.1109/WORKS.2014.12
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