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Comparison of resource platform selection approaches for scientific workflows

Authors:

Yogesh Simmhan,

Lavanya RamakrishnanAuthors Info & Claims

HPDC '10: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

Pages 445 - 450

https://doi.org/10.1145/1851476.1851541

Published: 21 June 2010 Publication History

Abstract

Cloud computing is increasingly considered as an additional computational resource platform for scientific workflows. The cloud offers opportunity to scale-out applications from desktops and local cluster resources. Each platform has different properties (e.g., queue wait times in high performance systems, virtual machine startup overhead in clouds) and characteristics (e.g., custom environments in cloud) that makes choosing from these diverse resource platforms for a workflow execution a challenge for scientists. Scientists are often faced with deciding resource platform selection trade-offs with limited information on the actual workflows. While many workflow planning methods have explored resource selection or task scheduling, these methods often require fine-scale characterization of the workflow that is onerous for a scientist. In this paper, we describe our early exploratory work in using blackbox characteristics for a cost-benefit analysis of using different resource platforms. In our blackbox method, we use only limited high-level information on the workflow length, width, and data sizes. The length and width are indicative of the workflow duration and parallelism. We compare the effectiveness of this approach to other resource selection models using two exemplar scientific workflows on desktop, local cluster, HPC center, and cloud platforms. Early results suggest that the blackbox model often makes the same resource selections as a more fine-grained whitebox model. We believe the simplicity of the blackbox model can help inform a scientist on the applicability of a new resource platform, such as cloud resources, even before porting an existing workflow.

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Cited By

Godard PLoechner VBastoul CSoulier FMuller G(2019)A Flexible and Distributed Runtime System for High-Throughput Constrained Data Streams Generation2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2019.00120(718-728)Online publication date: May-2019
https://doi.org/10.1109/IPDPSW.2019.00120
Angeli DMasala E(2019)A cost-effective cloud computing framework for accelerating multimedia communication simulationsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2012.06.00572:10(1373-1385)Online publication date: 4-Jan-2019
https://dl.acm.org/doi/10.1016/j.jpdc.2012.06.005
Clematis AD'Agostino DGalizia AQuarati A(2018)Profiling e-Science infrastructures with kernel and application benchmarksInternational Journal of Grid and Utility Computing10.1504/IJGUC.2014.0602045:2(123-134)Online publication date: 16-Dec-2018
https://dl.acm.org/doi/10.1504/IJGUC.2014.060204
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Index Terms

Comparison of resource platform selection approaches for scientific workflows
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

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Published In

cover image ACM Conferences

HPDC '10: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

June 2010

911 pages

ISBN:9781605589428

DOI:10.1145/1851476

General Chairs:
Salim Hariri
University of Arizona
,
Kate Keahey
University of Chicago

Copyright © 2010 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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University of Arizona: University of Arizona
SIGARCH: ACM Special Interest Group on Computer Architecture

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2010

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U.S. Department of Energy

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HPDC '10

Sponsor:

University of Arizona
SIGARCH

HPDC '10: The 19th International Symposium on High Performance Distributed Computing

June 21 - 25, 2010

Illinois, Chicago

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Overall Acceptance Rate 166 of 966 submissions, 17%

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Cited By

Godard PLoechner VBastoul CSoulier FMuller G(2019)A Flexible and Distributed Runtime System for High-Throughput Constrained Data Streams Generation2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2019.00120(718-728)Online publication date: May-2019
https://doi.org/10.1109/IPDPSW.2019.00120
Angeli DMasala E(2019)A cost-effective cloud computing framework for accelerating multimedia communication simulationsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2012.06.00572:10(1373-1385)Online publication date: 4-Jan-2019
https://dl.acm.org/doi/10.1016/j.jpdc.2012.06.005
Clematis AD'Agostino DGalizia AQuarati A(2018)Profiling e-Science infrastructures with kernel and application benchmarksInternational Journal of Grid and Utility Computing10.1504/IJGUC.2014.0602045:2(123-134)Online publication date: 16-Dec-2018
https://dl.acm.org/doi/10.1504/IJGUC.2014.060204
Turilli MLiu FZhang ZMerzky AWilde MWeissman JKatz DJha S(2016)Integrating Abstractions to Enhance the Execution of Distributed Applications2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS.2016.64(953-962)Online publication date: May-2016
https://doi.org/10.1109/IPDPS.2016.64
Sugumaran RBurnett JArmstrong MKarimi H(2014)Using a Cloud Computing Environment to Process Large 3D Spatial DatasetsBig Data10.1201/b16524-4(55-68)Online publication date: 22-Jan-2014
https://doi.org/10.1201/b16524-4
Clematis AD'Agostino DGalizia AQuarati A(2012)Efficiency-Aware Jobs Allocation for e-Science EnvironmentsProceedings of the 2012 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing10.1109/PDP.2012.17(162-169)Online publication date: 15-Feb-2012
https://dl.acm.org/doi/10.1109/PDP.2012.17
Masala E(2012)Moving Multimedia Simulations into the CloudProceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications10.1109/ISPA.2012.13(32-39)Online publication date: 10-Jul-2012
https://dl.acm.org/doi/10.1109/ISPA.2012.13
Oryspayev DSugumaran RDegroote JGray P(2012)LiDAR data reduction using vertex decimation and processing with GPGPU and multicore CPU technologyComputers & Geosciences10.1016/j.cageo.2011.09.01343(118-125)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1016/j.cageo.2011.09.013
Gorton ILiu YJian YKulkarni AWynne A(2011)Towards Composing Data Aware Systems Biology Workflows on Cloud PlatformsProceedings of the 2011 IEEE World Congress on Services10.1109/SERVICES.2011.22(184-191)Online publication date: 4-Jul-2011
https://dl.acm.org/doi/10.1109/SERVICES.2011.22
Galizia AQuarati ASchiffers MYampolskiy M(2011)Influences between performance based scheduling and service level agreementsProceedings of the 2011 international conference on Parallel Processing - Volume 210.1007/978-3-642-29740-3_12(96-105)Online publication date: 29-Aug-2011
https://dl.acm.org/doi/10.1007/978-3-642-29740-3_12

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