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Optimizing Biomedical Ultrasound Workflow Scheduling Using Cluster Simulations

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Job Scheduling Strategies for Parallel Processing (JSSPP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12326))

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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 fine-grain 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%.

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Notes

  1. 1.

    https://docs.it4i.cz/anselm/compute-nodes/.

  2. 2.

    This string corresponds to the list of direct predecessors used in Fig. 5.

  3. 3.

    The corresponding DAG looks like this: task 1\(\rightarrow \) task 2\(\rightarrow \) task 3.

  4. 4.

    The experiments were performed on a machine running Windows 10 with Intel Core i7-7500U CPU running at 2.7 Ghz and having 8 GB of RAM.

  5. 5.

    http://jsspp.org/workload/.

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Acknowledgments

We kindly acknowledge the support provided by the project Reg. No. CZ.02.1.01/0.0/0.0/16_013/0001797 co-funded by the Ministry of Education, Youth and Sports of the Czech Republic. Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures.

This work was also supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project IT4Innovations excellence in science - LQ1602 and by the IT4Innovations infrastructure which is supported from the Large Infrastructures for Research, Experimental Development and Innovations project IT4Innovations National Supercomputing Center - LM2015070. This project has received funding from the European Union’s Horizon 2020 research and innovation programme H2020 ICT 2016–2017 under grant agreement No 732411 and is an initiative of the Photonics Public Private Partnership.

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Authors and Affiliations

  1. Faculty of Information Technology, Centre of Excellence IT4Innovations, Brno University of Technology, Brno, Czech Republic

    Marta Jaros & Jiri Jaros

  2. CESNET a.l.e., Brno, Czech Republic

    Dalibor Klusáček

Authors
  1. Marta Jaros
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  2. Dalibor Klusáček
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  3. Jiri Jaros
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Correspondence to Marta Jaros .

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Editors and Affiliations

  1. CESNET, Prague, Czech Republic

    Dalibor Klusáček

  2. Google, Mountain View, CA, USA

    Walfredo Cirne

  3. Google, Seattle, WA, USA

    Narayan Desai

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Jaros, M., Klusáček, D., Jaros, J. (2020). Optimizing Biomedical Ultrasound Workflow Scheduling Using Cluster Simulations. In: Klusáček, D., Cirne, W., Desai, N. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2020. Lecture Notes in Computer Science(), vol 12326. Springer, Cham. https://doi.org/10.1007/978-3-030-63171-0_4

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  • DOI: https://doi.org/10.1007/978-3-030-63171-0_4

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  • Print ISBN: 978-3-030-63170-3

  • Online ISBN: 978-3-030-63171-0

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