Article

Virtual Resources Allocation for Workflow-Based Applications Distribution on a Cloud Infrastructure

Authors:

Tram Truong Huu,

Johan MontagnatAuthors Info & Claims

CCGRID '10: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

Pages 612 - 617

https://doi.org/10.1109/CCGRID.2010.23

Published: 17 May 2010 Publication History

Publisher Site Get Access

Abstract

Cloud computing infrastructures are providing resources on demand for tackling the needs of large-scale distributed applications. Determining the amount of resources to allocate for a given computation is a difficult problem though. This paper introduces and compares four automated resource allocation strategies relying on the expertise that can be captured in workflow-based applications. The evaluation of these strategies was carried out on the Aladdin/Grid'5000 testbed using a real application from the area of medical image analysis. Experimental results show that optimized allocation can help finding a trade-off between amount of resources consumed and applications make span.

References

[1]

Jim Blythe, Sonal Jain, Ewa Deelman, Yolanda Gil, Karan Vahi, Anirban Mandal, and Ken Kennedy. Task Scheduling Strategies for Workflow-based Applications in Grids. In International Symposium on Cluster Computing and the Grid (CCGrid'05), pages 759-767, 2005.

Digital Library

Google Scholar

[2]

Tracy D. Braun, Howard Jay Siegel, Noah Beck, Lasislau L. Bölöni, Muthucumara Maheswaran, Albert I. Reuther, James P. Robertson, Mitchell D. Theys, Bin Yao, Debra Hensgen, and Richard F. Freund. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing (JPDC), 61(6):810-837, 2001.

Digital Library

Google Scholar

[3]

Minh Quan Dang and Jorn Altmann. Resource allocation algorithm for light communication grid-based workflows within an SLA context. International Journal of Parallel, Emergent and Distributed Systems, 24(1):31-48, 2009.

Digital Library

Google Scholar

[4]

Minh Quan Dang and D. Frank Hsu. Mapping Heavy Communication Grid-Based Workflows Onto Grid Resources Within an SLA Context Using Metaheuristics. International Journal of High Performance Computing Applications (IJHPCA), 22(3):330-346, 2008.

Digital Library

Google Scholar

[5]

Tristan Glatard, Johan Montagnat, David Emsellem, and Diane Lingrand. A Service-Oriented Architecture enabling dynamic services grouping for optimizing distributed workflows execution. Future Generation Computer Systems (FGCS), 24(7):720-730, July 2008.

Digital Library

Google Scholar

[6]

Tristan Glatard, Johan Montagnat, Diane Lingrand, and Xavier Pennec. Flexible and efficient workflow deployement of data-intensive applications on grids with MOTEUR. Int. Journal of High Performance Computing and Applications (IJHPCA), 22(3):347-360, August 2008.

Digital Library

Google Scholar

[7]

Tristan Glatard, Xavier Pennec, and Johan Montagnat. Performance evaluation of grid-enabled registration algorithms using bronze-standards. In Medical Image Computing and Computer-Assisted Intervention (MICCAI'06) , October 2006.

Digital Library

Google Scholar

[8]

Wei Guo, Weiqiang Sun, Weisheng Hu, and Yaohui Jin. Resource Allocation Strategies for Data-Intensive Workflow-Based Applications in Optical Grids. In 10th IEEE Singapore International Conference on Communication systems (IEEE ICCS 2006), pages 1-5, October 2006.

Google Scholar

[9]

Guilherme Koslovski, Tram Truong Huu, Johan Montagnat, and Pascale Vicat-Blanc Primet. Executing distributed applications on virtualized infrastructures specified with the VXDL language and managed by the HIPerNET framework. In First International Conference on Cloud Computing (CLOUDCOMP 2009), Munich, Germany, October 2009.

Google Scholar

[10]

Anirban Mandal, Ken Kennedy, Charles Koelbel, Gabriel Marin, John Mellor-Crummey, Bo Liu, and Lennart Johnsson. Scheduling strategies for mapping application workflows onto the grid. In 14th IEEE International Symposium on High Performance Distributed Computing (HPDC'05), pages 125-134, Washington, DC, USA, 2005. IEEE Computer Society.

Digital Library

Google Scholar

[11]

Lavanya Ramakrishnan, Daniel Nurmi, Anirban Mandal, Charles Koelbel, Dennis Gannon, T.M. Huang, Yang-Seok Kee, Graziano Obertelli, Kiran Thyagaraja, Rich Wolski, Asim YarKhan, and Dmitri Zagorodnov. VGrADS: Enabling e-Science Workflows on Grids and Clouds with Fault Tolerance. In International Conference for High Performance Computing, Networking, Storage and Analysis (SC09), November 2009.

Digital Library

Google Scholar

[12]

Pinar Senkul and Ismail H. Toroslu. An architecture for workflow scheduling under resource allocation constraints. Information Systems, 30(5):399-422, 2005.

Digital Library

Google Scholar

[13]

Zhijiao Xiao, Huiyou Chang, and Yang Yi. Optimization of Workflow Resources Allocation with Cost Constraint, pages 647-656. Springer Berlin / Heidelberg, August 2007.

Digital Library

Google Scholar

[14]

Henan Zhao and Rizos Sakellariou. Scheduling Multiple DAGs onto Heterogeneous Systems. In 15th Heterogeneous Computing Workshop (HCW 2006), Rhodes Island, Greece, April 2006.

Digital Library

Google Scholar

Cited By

View all

Pietri ISakellariou R(2016)Mapping Virtual Machines onto Physical Machines in Cloud ComputingACM Computing Surveys10.1145/298357549:3(1-30)Online publication date: 12-Oct-2016
https://dl.acm.org/doi/10.1145/2983575
Zhang ZLi Y(2015)User utility oriented queuing model for resource allocation in cloud environmentJournal of Electrical and Computer Engineering10.1155/2015/2464202015(4-4)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1155/2015/246420
Babu KKumar DVeluru SNapoli CSalapura VFranke HHou R(2015)Optimal allocation of virtual resources using genetic algorithm in cloud environmentsProceedings of the 12th ACM International Conference on Computing Frontiers10.1145/2742854.2744722(1-6)Online publication date: 6-May-2015
https://dl.acm.org/doi/10.1145/2742854.2744722
Show More Cited By

Virtual Resources Allocation for Workflow-Based Applications Distribution on a Cloud Infrastructure

Recommendations

Joint Elastic Cloud and Virtual Network Framework for Application Performance-cost Optimization

Cloud computing infrastructures are providing resources on demand for tackling the needs of large-scale distributed applications. To adapt to the diversity of cloud infrastructures and usage, new operation tools and models are needed. Estimating the ...
FOCALB: Fog Computing Architecture of Load Balancing for Scientific Workflow Applications
Abstract
Fog computing has a broad scope in real-time applications. It appears in the middle of Internet of Things (IoT) users and the cloud layer. The main applications of fog computing are to decrease latency and improve resource utilization for the end-...
Modelling and solving grid resource allocation problem with network resources for workflow applications

A problem of allocating resources of a grid to workflow applications is considered. The problem consists, generally, in allocating distributed grid resources to tasks of a workflow in such a way that the resource demands of each task are satisfied. Grid ...

Comments

Information & Contributors

Information

Published In

CCGRID '10: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

May 2010

863 pages

ISBN:9780769540399

Publisher

IEEE Computer Society

United States

Publication History

Published: 17 May 2010

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
109
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Pietri ISakellariou R(2016)Mapping Virtual Machines onto Physical Machines in Cloud ComputingACM Computing Surveys10.1145/298357549:3(1-30)Online publication date: 12-Oct-2016
https://dl.acm.org/doi/10.1145/2983575
Zhang ZLi Y(2015)User utility oriented queuing model for resource allocation in cloud environmentJournal of Electrical and Computer Engineering10.1155/2015/2464202015(4-4)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1155/2015/246420
Babu KKumar DVeluru SNapoli CSalapura VFranke HHou R(2015)Optimal allocation of virtual resources using genetic algorithm in cloud environmentsProceedings of the 12th ACM International Conference on Computing Frontiers10.1145/2742854.2744722(1-6)Online publication date: 6-May-2015
https://dl.acm.org/doi/10.1145/2742854.2744722
Rygielski PTomczak J(2011)Context change detection for resource allocation in service-oriented systemsProceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II10.5555/2041341.2041409(591-600)Online publication date: 12-Sep-2011
https://dl.acm.org/doi/10.5555/2041341.2041409

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Joint Elastic Cloud and Virtual Network Framework for Application Performance-cost Optimization

FOCALB: Fog Computing Architecture of Load Balancing for Scientific Workflow Applications

Modelling and solving grid resource allocation problem with network resources for workflow applications

Comments

Published In

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Recommendations

Joint Elastic Cloud and Virtual Network Framework for Application Performance-cost Optimization

FOCALB: Fog Computing Architecture of Load Balancing for Scientific Workflow Applications

Modelling and solving grid resource allocation problem with network resources for workflow applications

Comments

Information

Published In

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations