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Multi-objective ant colony system for data-intensive service provision

Conference Paper


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Abstract


  • Data-intensive services have become one of the

    most challenging applications in cloud computing. The classical

    service composition problem will face new challenges as the

    services and correspondent data grow. A typical environment

    is the large scale scientific project AMS, which we are

    processing huge amount of data streams. In this paper, we

    will resolve service composition problem by considering the

    multi-objective data-intensive features. We propose to apply

    ant colony optimization algorithms and implemented them

    with simulated workflows in different scenarios. To evaluate

    the proposed algorithm, we compared it with a multi-objective

    genetic algorithm with respect to five performance metrics

Authors


  •   Wang, Lijuan (external author)
  •   Shen, Jun
  •   Luo, Junzhou (external author)

Publication Date


  • 2014

Citation


  • Wang, L., Shen, J. & Luo, J. (2014). Multi-objective ant colony system for data-intensive service provision. International Conference on Advanced Cloud and Big Data (pp. 45-52). United States: Institute of Electrical and Electronics Engineers.

Scopus Eid


  • 2-s2.0-84959451598

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4946&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3927

Has Global Citation Frequency


Start Page


  • 45

End Page


  • 52

Place Of Publication


  • United States

Abstract


  • Data-intensive services have become one of the

    most challenging applications in cloud computing. The classical

    service composition problem will face new challenges as the

    services and correspondent data grow. A typical environment

    is the large scale scientific project AMS, which we are

    processing huge amount of data streams. In this paper, we

    will resolve service composition problem by considering the

    multi-objective data-intensive features. We propose to apply

    ant colony optimization algorithms and implemented them

    with simulated workflows in different scenarios. To evaluate

    the proposed algorithm, we compared it with a multi-objective

    genetic algorithm with respect to five performance metrics

Authors


  •   Wang, Lijuan (external author)
  •   Shen, Jun
  •   Luo, Junzhou (external author)

Publication Date


  • 2014

Citation


  • Wang, L., Shen, J. & Luo, J. (2014). Multi-objective ant colony system for data-intensive service provision. International Conference on Advanced Cloud and Big Data (pp. 45-52). United States: Institute of Electrical and Electronics Engineers.

Scopus Eid


  • 2-s2.0-84959451598

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4946&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3927

Has Global Citation Frequency


Start Page


  • 45

End Page


  • 52

Place Of Publication


  • United States