Minimizing financial cost of scientific workflows under deadline constraints in multi-cloud environments

Tianyu Gao, Yongqiang Wang, Chase Q. Wu, Ruxia Li, Aiqin Hou, Mingrui Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In recent years, cloud platforms have been rapidly developed and deployed around the globe and many large-scale scientific workflows have been migrated to multiple clouds for cost-effective data analysis. In such cloud-based workflow applications, financial cost is a major concern in addition to traditional performance requirements such as execution time. In this paper, we formulate a workflow mapping problem to minimize the financial cost of deadline-constrained scientific workflows executed in multi-cloud environments, referred to as MinCost-MC, which is shown to be NP-complete. Within a generic three-layer workflow execution framework, we propose a Workflow Mapping algorithm for Financial Cost Optimization, referred to as WMFCO. This algorithm takes in consideration storage requirements, I /O operations, and data transfers to minimize the financial cost of a given workflow within a specified deadline. Extensive simulation results show that WMFCO exhibits a superior performance over existing algorithms in terms of financial cost in multi-cloud environments.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM Symposium on Applied Computing
PublisherAssociation for Computing Machinery
Pages114-121
Number of pages8
ISBN (Print)9781450359337
DOIs
StatePublished - 2019
Event34th Annual ACM Symposium on Applied Computing, SAC 2019 - Limassol, Cyprus
Duration: Apr 8 2019Apr 12 2019

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F147772

Conference

Conference34th Annual ACM Symposium on Applied Computing, SAC 2019
Country/TerritoryCyprus
CityLimassol
Period4/8/194/12/19

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Cloud computing
  • Cost optimization
  • Workflow mapping

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