Optimal deployment of geographically distributed workflow engines on the Cloud

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

doi:10.1109/CloudCom.2014.30

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 proceeding › Conference contribution

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 op- timal 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 sci- entific 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 language	English
Title of host publication	6th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2014)
Publisher	IEEE
Pages	811-816
Number of pages	6
ISBN (Print)	9781479940936
DOIs	https://doi.org/10.1109/CloudCom.2014.30
Publication status	Published - 30 Oct 2014

Keywords

Workflow engine
Optimal deployment
Cloud computing
Workflow execution

Access to Document

10.1109/CloudCom.2014.30

Optimal Deployment of Geographically Distributed Workflow Engines on the Cloud
© 2014. IEEE. This is the accepted manuscript of a conference paper originally submitted to the IEEE 6th IEEE International Conference on Cloud Computing Technology and Science- Optimal deployment of geographically distributed workflow engines on the cloud Thai, L., Barker, A., Varghese, B., Akgun, O. & Miguel, I. 30 Oct 2014 6th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2014). IEEE, p. 811-816 available from http://dx.doi.org/10.1109/CloudCom.2014.30
Accepted author manuscript, 530 KB

Cite this

@inproceedings{09b16cf13ed94fb1b2fed6296b5a75d1,

title = "Optimal deployment of geographically distributed workflow engines on the Cloud",

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 op- timal 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 sci- entific 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.",

keywords = "Workflow engine, Optimal deployment, Cloud computing, Workflow execution",

author = "Long Thai and Adam Barker and Blesson Varghese and Ozgur Akgun and Ian Miguel",

note = "This research was pursued under the EPSRC {\textquoteleft}Working Together: Constraint Programming and Cloud Computing{\textquoteright} grant, a Royal Society Industry Fellowship {\textquoteleft}Bringing Science to the Cloud{\textquoteright}, and an Amazon Web Services Education Research Grant. Date of Acceptance: 02/09/2014",

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month = oct,

day = "30",

doi = "10.1109/CloudCom.2014.30",

language = "English",

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T1 - Optimal deployment of geographically distributed workflow engines on the Cloud

AU - Thai, Long

AU - Barker, Adam

AU - Varghese, Blesson

AU - Akgun, Ozgur

AU - Miguel, Ian

N1 - This research was pursued under the EPSRC ‘Working Together: Constraint Programming and Cloud Computing’ grant, a Royal Society Industry Fellowship ‘Bringing Science to the Cloud’, and an Amazon Web Services Education Research Grant. Date of Acceptance: 02/09/2014

PY - 2014/10/30

Y1 - 2014/10/30

N2 - 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 op- timal 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 sci- entific 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.

AB - 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 op- timal 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 sci- entific 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.

KW - Workflow engine

KW - Optimal deployment

KW - Cloud computing

KW - Workflow execution

UR - http://2014.cloudcom.org/

UR - http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7031670

U2 - 10.1109/CloudCom.2014.30

DO - 10.1109/CloudCom.2014.30

M3 - Conference contribution

SN - 9781479940936

SP - 811

EP - 816

BT - 6th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2014)

PB - IEEE

ER -

Optimal deployment of geographically distributed workflow engines on the Cloud

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

RS Industry Fellowship: RS Industry Fellowship

Working Together in ICT: Working Together: Constraint Programming and Cloud Computing

Cite this

Optimal deployment of geographically distributed workflow engines on the Cloud

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Projects

RS Industry Fellowship: RS Industry Fellowship

Working Together in ICT: Working Together: Constraint Programming and Cloud Computing

Cite this