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Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance

Tazro Ohta1, Tomoya Tanjo2, Osamu Ogasawara3

  • 1Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Mishima, Shizuoka 411-8540, Japan.

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Summary
This summary is machine-generated.

CWL-metrics analyzes workflow resource needs for cloud computing. This tool helps users select optimal instance types, reducing costs and execution times for bioinformatics workflows.

Keywords:
Common Workflow Languagecloud computinghigh-throughput nucleotide sequencing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cloud Computing

Background:

  • Containerization technologies like Docker enhance software portability and reproducibility in bioinformatics.
  • Common Workflow Language (CWL) standardizes workflow descriptors for multi-environment analysis.
  • Cloud computing platforms offer scalable resources but require careful instance selection for cost and time optimization.

Purpose of the Study:

  • To develop a tool for analyzing workflow resource requirements in cloud environments.
  • To aid users in optimizing cloud resource utilization for bioinformatics workflows.

Main Methods:

  • Developed CWL-metrics, a utility for cwltool, to collect Docker container runtime metrics and workflow metadata.
  • Analyzed seven transcriptome quantification workflows across six different cloud instance types.

Main Results:

  • Instance type selection significantly impacts financial costs and execution times.
  • CWL-metrics provides data to match workflow resource needs with appropriate cloud instances.
  • Optimized instance selection led to lower costs and faster processing for transcriptome quantification.

Conclusions:

  • CWL-metrics assists users in optimizing cloud computing by identifying suitable instances based on workflow resource requirements.
  • The collected runtime metrics facilitate workflow sharing across different management frameworks.