CMK: Enhancing Resource Usage Monitoring across Diverse Bioinformatics Workflow Management Systems

The increasing use of multiple Workflow Management Systems (WMS) employing various workflow languages and shared workflow repositories enhances the open-source bioinformatics ecosystem. Efficient resource utilization in these systems is crucial for keeping costs low and improving processing times, e...

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Vydáno v:	Journal of grid computing Ročník 22; číslo 3; s. 62
Hlavní autoři:	Nica, Robert, Götz, Stefan, Moltó, Germán
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.09.2024 Springer Nature B.V
Témata:	Bioinformatics Cloud computing Computer Science Configuration management Cost analysis Data analysis Data processing Management of Computing and Information Systems Management systems Modularity Monitoring Open source software Processor Architectures Resource scheduling Resource utilization Task scheduling User Interfaces and Human Computer Interaction Web services Workflow management systems Workflow software Cloud computing High-throughput computing Bioinformatics infrastructure Monitoring Workflow management systems
ISSN:	1570-7873, 1572-9184
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Abstract	The increasing use of multiple Workflow Management Systems (WMS) employing various workflow languages and shared workflow repositories enhances the open-source bioinformatics ecosystem. Efficient resource utilization in these systems is crucial for keeping costs low and improving processing times, especially for large-scale bioinformatics workflows running in cloud environments. Recognizing this, our study introduces a novel reference architecture, Cloud Monitoring Kit (CMK), for a multi-platform monitoring system. Our solution is designed to generate uniform, aggregated metrics from containerized workflow tasks scheduled by different WMS. Central to the proposed solution is the use of task labeling methods, which enable convenient grouping and aggregating of metrics independent of the WMS employed. This approach builds upon existing technology, providing additional benefits of modularity and capacity to seamlessly integrate with other data processing or collection systems. We have developed and released an open-source implementation of our system, which we evaluated on Amazon Web Services (AWS) using a transcriptomics data analysis workflow executed on two scientific WMS. The findings of this study indicate that CMK provides valuable insights into resource utilization. In doing so, it paves the way for more efficient management of resources in containerized scientific workflows running in public cloud environments, and it provides a foundation for optimizing task configurations, reducing costs, and enhancing scheduling decisions. Overall, our solution addresses the immediate needs of bioinformatics workflows and offers a scalable and adaptable framework for future advancements in cloud-based scientific computing.
AbstractList	The increasing use of multiple Workflow Management Systems (WMS) employing various workflow languages and shared workflow repositories enhances the open-source bioinformatics ecosystem. Efficient resource utilization in these systems is crucial for keeping costs low and improving processing times, especially for large-scale bioinformatics workflows running in cloud environments. Recognizing this, our study introduces a novel reference architecture, Cloud Monitoring Kit (CMK), for a multi-platform monitoring system. Our solution is designed to generate uniform, aggregated metrics from containerized workflow tasks scheduled by different WMS. Central to the proposed solution is the use of task labeling methods, which enable convenient grouping and aggregating of metrics independent of the WMS employed. This approach builds upon existing technology, providing additional benefits of modularity and capacity to seamlessly integrate with other data processing or collection systems. We have developed and released an open-source implementation of our system, which we evaluated on Amazon Web Services (AWS) using a transcriptomics data analysis workflow executed on two scientific WMS. The findings of this study indicate that CMK provides valuable insights into resource utilization. In doing so, it paves the way for more efficient management of resources in containerized scientific workflows running in public cloud environments, and it provides a foundation for optimizing task configurations, reducing costs, and enhancing scheduling decisions. Overall, our solution addresses the immediate needs of bioinformatics workflows and offers a scalable and adaptable framework for future advancements in cloud-based scientific computing.
ArticleNumber	62
Author	Götz, Stefan Moltó, Germán Nica, Robert
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Cites_doi	10.7490/F1000RESEARCH.1114634.1 10.12688/f1000research.10137.1 10.1007/10968987_3 10.1109/MCSE.2017.2421459 10.1109/ISPASS.2015.7095802 10.1101/gr.4086505 10.1038/s41592-021-01254-9 10.1038/533452A 10.1145/2452376.2452478 10.1007/978-3-030-24322-7_59/FIGURE 10.3390/LIFE12050648 10.1093/GIGASCIENCE/GIZ095 10.5281/ZENODO.4605654 10.1093/GIGASCIENCE/GIZ052 10.1201/9781003441601-2 10.1186/1471-2105-9-82/TABLES/1 10.1007/S10723-018-09471-X/METRICS 10.1109/BIGDATA55660.2022.10020864 10.6084/M9.FIGSHARE.3115156 10.1038/NBT.3820 10.1093/NAR/GKN176 10.1109/ISCON47742.2019.9036238 10.1038/nbt.3772 10.1007/S00778-017-0486-1 10.1109/TENCON.2017.8228349 10.1093/BIOINFORMATICS/BTZ054 10.1162/DINT_A_00033
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SubjectTerms	Bioinformatics Cloud computing Computer Science Configuration management Cost analysis Data analysis Data processing Management of Computing and Information Systems Management systems Modularity Monitoring Open source software Processor Architectures Resource scheduling Resource utilization Task scheduling User Interfaces and Human Computer Interaction Web services Workflow management systems Workflow software
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