Genome analysis in a dynamically scaled hybrid cloud

Christopher Smowton, Georgiana Copil, Hong Linh Truong, Crispin Miller, Wei Xing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we explore the benefits of automatically determining the degree of parallelism used to perform genetic mutation calling in a hybrid cloud environment. We propose algorithms to automatically control both the hiring of hybrid cloud resources and the selection of the degree of parallelism employed in analysis tasks executed against that cloud. Using the Broad Institute's Genome Analysis Toolkit as a case study, we then conduct profile-driven simulation studies to characterise the circumstances in which our algorithms are beneficial or deleterious compared to simple, conventional baseline algorithms. We find that there are a wide range of cloud workload scenarios where our algorithms outperform the baselines, and thereby argue that automatic control of cloud scaling and task parallelism, using techniques like those proposed, are likely to be beneficially applicable to real-world biocomputing.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on eScience, eScience 2015
PublisherIEEE
Pages391-400
Number of pages10
ISBN (Electronic)9781467393256
DOIs
Publication statusPublished - 22 Oct 2015
Event11th IEEE International Conference on eScience - Munich, Germany
Duration: 31 Aug 20154 Sept 2015

Conference

Conference11th IEEE International Conference on eScience
Abbreviated titleeScience 2015
Country/TerritoryGermany
CityMunich
Period31/08/154/09/15

Keywords

  • Auto-scaling
  • Biocomputing
  • Cloud computing
  • Dynamic scalability
  • Genome analysis
  • Thread-level parallelism

Research Beacons, Institutes and Platforms

  • Manchester Cancer Research Centre

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