Publication Details

k-Dispatch: Enabling Cost-Optimized Biomedical Workflow Offloading

JAROŠ, M.; JAROŠ, J. k-Dispatch: Enabling Cost-Optimized Biomedical Workflow Offloading. HPDC '24: Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing. New York: Association for Computing Machinery, 2024. p. 358-360. ISBN: 979-8-4007-0413-0.
Czech title
k-Dispatch: Cenove optimalizovane spousteni workflows
Type
conference paper
Language
English
Authors
Keywords

Workflow scheduling, Multi-criteria optimization, HPC, Cloud

Abstract

Automated execution of computational workflows has become a critical issue in achieving high productivity in various research and development fields. Over the last few years, workflows have emerged as a significant abstraction of numerous real-world processes and phenomena, including digital twins, personalized medici-ne, and simulation-based science   in general. k-Dispatch is a novel tool designed for the efficient offloading of biomedical workflows to remote high-performance computing clusters or cloud. In addition to data transfers, reporting, error handling and remote computations monitoring, k-Dispatch leverages a set of optimizations to dynamically determine suitable execution parameters for individual tasks within workflows, aiming to meet predefined constraints and optimization criteria. k-Dispatch has been successfully deployed within k-Plan, an advanced modelling tool for planning transcranial ultrasound stimulation (TUS) procedures. 

Published
2024
Pages
358–360
Proceedings
HPDC '24: Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing
ISBN
979-8-4007-0413-0
Publisher
Association for Computing Machinery
Place
New York
DOI
BibTeX
@inproceedings{BUT189461,
  author="Marta {Jaroš} and Jiří {Jaroš}",
  title="k-Dispatch: Enabling Cost-Optimized Biomedical Workflow Offloading",
  booktitle="HPDC '24: Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing",
  year="2024",
  pages="358--360",
  publisher="Association for Computing Machinery",
  address="New York",
  doi="10.1145/3625549.3658828",
  isbn="979-8-4007-0413-0",
  url="https://www.fit.vut.cz/research/publication/13179/"
}
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