Publication Details

Optimizing Biomedical Ultrasound Workflow Scheduling Using Cluster Simulations

JAROŠ, M.; KLUSÁČEK, D.; JAROŠ, J. Optimizing Biomedical Ultrasound Workflow Scheduling Using Cluster Simulations. In Job Scheduling Strategies for Parallel Processing. JSSPP 2020. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). New Orleans: Springer Nature Switzerland AG, 2020. p. 68-84. ISBN: 978-3-030-63170-3.
Czech title
Optimalizace plánování biomedicínských ultrazvukových řetězců úloh pomocí simulátoru clusterů
Type
conference paper
Language
English
Authors
Jaroš Marta, Ing., Ph.D. (DCSY)
KLUSÁČEK, D.
Jaroš Jiří, doc. Ing., Ph.D. (DCSY)
URL
Keywords

scheduling, workflow, k-Dispatch, simulation, ALEA

Abstract

Therapeutic ultrasound plays an increasing role in dealing
with oncological diseases, drug delivery and neurostimulation. To maximize the treatment outcome, thorough pre-operative planning using complex numerical models considering patient anatomy is crucial. From the
computational point of view, the treatment planning can be seen as the
execution of a complex workflow consisting of many different tasks with
various computational requirements on a remote cluster or in cloud. Since
these resources are precious, workflow scheduling plays an important part
in the whole process.
This paper describes an extended version of the k-Dispatch workflow
management system that uses historical performance data collected on
similar workflows to choose suitable amount of computational resources
and estimates execution time and cost of particular tasks. This paper
also introduces necessary extensions to the Alea cluster simulator that
enable the estimation of the queuing and total execution time of the
whole workflow. The conjunction of both systems then allows for finegrain optimization of the workflow execution parameters with respect to
the current cluster utilization. The experimental results show that this
approach is able to reduce the computational time by 26%.

Published
2020
Pages
68–84
Proceedings
Job Scheduling Strategies for Parallel Processing. JSSPP 2020
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
12326
Conference
Job Scheduling Strategies for Parallel Processing 23rd International Workshop, JSSPP 2020, New Orleans, LA, USA, May 22, US
ISBN
978-3-030-63170-3
Publisher
Springer Nature Switzerland AG
Place
New Orleans
DOI
10.1007/978-3-030-63171-0_4
EID Scopus
2-s2.0-85097306932
BibTeX 
@inproceedings{BUT168133,
  author="JAROŠ, M. and KLUSÁČEK, D. and JAROŠ, J.",
  title="Optimizing Biomedical Ultrasound Workflow Scheduling Using Cluster Simulations",
  booktitle="Job Scheduling Strategies for Parallel Processing. JSSPP 2020",
  year="2020",
  series="Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  volume="12326",
  pages="68--84",
  publisher="Springer Nature Switzerland AG",
  address="New Orleans",
  doi="10.1007/978-3-030-63171-0\{_}4",
  isbn="978-3-030-63170-3",
  url="https://link.springer.com/chapter/10.1007/978-3-030-63171-0_4"
}
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