research-article

Towards efficient scheduling of data intensive high energy physics workflows

Authors:

Mahantesh Halappanavar,

Malachi Schram,

Luis de la Torre,

Nathan R. Tallent,

Darren J. KerbysonAuthors Info & Claims

WORKS '15: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science

Article No.: 3, Pages 1 - 9

https://doi.org/10.1145/2822332.2822335

Published: 15 November 2015 Publication History

Abstract

Data intensive high energy physics workflows executed on geographically distributed resources pose a tremendous challenge for efficient use of computing resources. In this early work paper, we present a hierarchical framework for efficient allocation of resources and energy-efficient assignment of tasks for a representative high energy physics application, the Belle II experiments. With an expected data rate of 25 peta bytes per year from experimental data and Monte Carlo simulations, the Belle II experiment provides an ideal platform for algorithmic development. Building on the analogy of the unit commitment problem in electric power grids, we present a novel cost-efficient method for resource allocation that feeds into energy-efficient assignment of tasks to resources using a novel semi-matching based algorithm. We demonstrate that this approach is both computationally efficient and effective. We expect the methods developed in this work to benefit Belle II and other complex workflows executed on distributed resources.

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Cited By

de la Torre LHalappanavar M(2023)Scaling Optimal Allocation of Cloud Resources Using Lagrange RelaxationJob Scheduling Strategies for Parallel Processing10.1007/978-3-031-43943-8_9(173-192)Online publication date: 15-Sep-2023
https://doi.org/10.1007/978-3-031-43943-8_9
Alves DObraczka KKabbani A(2021)GDSim: Benchmarking Geo-Distributed Data Center Schedulers2021 IEEE 10th International Conference on Cloud Networking (CloudNet)10.1109/CloudNet53349.2021.9657143(148-156)Online publication date: 8-Nov-2021
https://doi.org/10.1109/CloudNet53349.2021.9657143
Bhuiyan THalappanavar MFriese RMedal Hde la Torre LSathanur ATallent N(2019)Stochastic Programming Approach for Resource Selection Under Demand UncertaintyJob Scheduling Strategies for Parallel Processing10.1007/978-3-030-10632-4_6(107-126)Online publication date: 13-Jan-2019
https://doi.org/10.1007/978-3-030-10632-4_6
Show More Cited By

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Published In

cover image ACM Conferences

WORKS '15: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science

November 2015

98 pages

ISBN:9781450339896

DOI:10.1145/2822332

Conference Chairs:
Johan Montagnat
CNRS, France
,
Ian Taylor
Cardiff University, UK

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGHPC: ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing
SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS\DATC: IEEE Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2015

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Funding Sources

U. S. Department of Energy

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SC15

Sponsor:

SIGHPC
SIGARCH
IEEE-CS\DATC

SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 15, 2015

Texas, Austin

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WORKS '15 Paper Acceptance Rate 9 of 13 submissions, 69%;

Overall Acceptance Rate 30 of 54 submissions, 56%

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Cited By

de la Torre LHalappanavar M(2023)Scaling Optimal Allocation of Cloud Resources Using Lagrange RelaxationJob Scheduling Strategies for Parallel Processing10.1007/978-3-031-43943-8_9(173-192)Online publication date: 15-Sep-2023
https://doi.org/10.1007/978-3-031-43943-8_9
Alves DObraczka KKabbani A(2021)GDSim: Benchmarking Geo-Distributed Data Center Schedulers2021 IEEE 10th International Conference on Cloud Networking (CloudNet)10.1109/CloudNet53349.2021.9657143(148-156)Online publication date: 8-Nov-2021
https://doi.org/10.1109/CloudNet53349.2021.9657143
Bhuiyan THalappanavar MFriese RMedal Hde la Torre LSathanur ATallent N(2019)Stochastic Programming Approach for Resource Selection Under Demand UncertaintyJob Scheduling Strategies for Parallel Processing10.1007/978-3-030-10632-4_6(107-126)Online publication date: 13-Jan-2019
https://doi.org/10.1007/978-3-030-10632-4_6
Friese RTallent NSchram MHalappanavar MBarker K(2018)Optimizing Distributed Data-Intensive Workflows2018 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER.2018.00045(279-289)Online publication date: Sep-2018
https://doi.org/10.1109/CLUSTER.2018.00045
Friese RHalappanavar MSathanur ASchram MKerbyson Dde la Torre L(2018)Towards Efficient Resource Allocation for Distributed Workflows Under Demand UncertaintiesJob Scheduling Strategies for Parallel Processing10.1007/978-3-319-77398-8_6(103-121)Online publication date: 28-Feb-2018
https://doi.org/10.1007/978-3-319-77398-8_6
Schram MBansal VFriese RTallent NYin JBarker KStephan EHalappanavar MKerbyson D(2017)Integrating prediction, provenance, and optimization into high energy workflowsJournal of Physics: Conference Series10.1088/1742-6596/898/6/062052898(062052)Online publication date: 21-Nov-2017
https://doi.org/10.1088/1742-6596/898/6/062052
Friese R(2016)Efficient Genetic Algorithm Encoding for Large-Scale Multi-objective Resource Allocation2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2016.36(1360-1369)Online publication date: May-2016
https://doi.org/10.1109/IPDPSW.2016.36

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