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

Document Type

Conference Proceeding

Publication Date

1-1-2019

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.

Identifier

85065668175 (Scopus)

ISBN

[9781450359337]

Publication Title

Proceedings of the ACM Symposium on Applied Computing

External Full Text Location

https://doi.org/10.1145/3297280.3297293

First Page

114

Last Page

121

Volume

Part F147772

Grant

61472320

Fund Ref

Rutgers Cancer Institute of New Jersey

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