Evidence map›Paper›PMID 37624874›Full record

ArticleGigaScience2022

Container Profiler: Profiling resource utilization of containerized big data pipelines.

Varik Hoang, Ling-Hong Hung, David Perez, Huazeng Deng, Raymond Schooley, Niharika Arumilli, Ka Yee Yeung, Wes Lloyd

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Varik HoangSchool of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA.ORCID 0000-0001-7311-1308
Ling-Hong HungSchool of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA.ORCID 0000-0002-5209-2248
David PerezSchool of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA.
Huazeng DengSchool of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA.
Raymond SchooleySchool of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA.ORCID 0009-0009-1958-9525
Niharika ArumilliSchool of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA.
Ka Yee YeungSchool of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA.ORCID 0000-0002-1754-7577
Wes LloydSchool of Engineering and Technology, University of Washington, Tacoma, WA 98402, USA.ORCID 0000-0003-2021-8501

Funding

MorPhiC Data Resource and Administrative Coordinating CenterU24HG012674 · NHGRI · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI Helen Elizabeth Parkinson, Stephan C Schurer · 2022 to 2026
$6.9M
Intelligent deployment of containerized bioinformatics workflows on the cloudR01GM126019 · NIGMS · UNIVERSITY OF WASHINGTON · PI YEUNG-RHEE, KA YEE · 2018 to 2020
$1.1M
Integrative and interactive analyses of host transcriptional response to COVID-19 and other respiratory viral infectionsR03AI159286 · NIAID · UNIVERSITY OF WASHINGTON · PI YEUNG-RHEE, KA YEE · 2022 to 2023
$155k
NHGRI NIH HHS U24 HG012674NIAID NIH HHS R03 AI159286NIGMS NIH HHS R01 GM126019
6 · The paper itself

Abstract

backgroundThis article presents the Container Profiler, a software tool that measures and records the resource usage of any containerized task. Our tool profiles the CPU, memory, disk, and network utilization of containerized tasks collecting over 60 Linux operating system metrics at the virtual machine, container, and process levels. The Container Profiler supports performing time-series profiling at a configurable sampling interval to enable continuous monitoring of the resources consumed by containerized tasks and pipelines.

resultsTo investigate the utility of the Container Profiler, we profile the resource utilization requirements of a multistage bioinformatics analytical pipeline (RNA sequencing using unique molecular identifiers). We examine profiling metrics to assess patterns of CPU, disk, and network resource utilization across the different stages of the pipeline. We also quantify the profiling overhead of our Container Profiler tool to assess the impact of profiling a running pipeline with different levels of profiling granularity, verifying that impacts are negligible.

conclusionsThe Container Profiler provides a useful tool that can be used to continuously monitor the resource consumption of long and complex containerized applications that run locally or on the cloud. This can help identify bottlenecks where more resources are needed to improve performance.

Indexed as

Big DataComputational BiologySoftwareTime Factorscloud computingperformanceresource profilingRNA sequencingtesting

Identifiers

PMID37624874
PMCPMC10452954

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.