Optimal deployment of geographically distributed workflow engines on the cloud

Long Thai, Adam Barker, Blesson Varghese, Ozgur Akgun, Ian Miguel

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

7 Citations (Scopus)
237 Downloads (Pure)

Abstract

When orchestrating Web service workflows, the geographical placement of the orchestration engine (s) can greatly affect workflow performance. Data may have to be transferred across long geographical distances, which in turn increases execution time and degrades the overall performance of a workflow. In this paper, we present a framework that, given a DAG-based workflow specification, computes the optimal Amazon EC2 cloud regions to deploy the orchestration engines and execute a workflow. The framework incorporates a constraint model that solves the workflow deployment problem, which is generated using an automated constraint modelling system. The feasibility of the framework is evaluated by executing different sample workflows representative of scientific workloads. The experimental results indicate that the framework reduces the workflow execution time and provides a speed up of 1.3x-2.5x over centralised approaches.
Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and Science
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages811-816
Number of pages6
ISBN (Print)978-1-4799-4093-6
DOIs
Publication statusPublished - Dec 2014
Event2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom) - Singapore, Singapore
Duration: 15 Dec 201418 Dec 2014

Conference

Conference2014 IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom)
CountrySingapore
CitySingapore
Period15/12/201418/12/2014

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  • Cite this

    Thai, L., Barker, A., Varghese, B., Akgun, O., & Miguel, I. (2014). Optimal deployment of geographically distributed workflow engines on the cloud. In Proceedings of the 2014 IEEE 6th International Conference on Cloud Computing Technology and Science (pp. 811-816). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CloudCom.2014.30