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
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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