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Enhanced ant colony algorithm for cost-aware data-intensive service provision

Conference Paper


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Abstract


  • Huge collections of data have been created in recent

    years. Cloud computing has been widely accepted as the nextgeneration

    solution to addressing data-proliferation problems.

    Because of the explosion in digital data and the distributed nature

    of the cloud, as well as the increasingly large number of providers

    in the market, providing efficient cost models for composing dataintensive

    services will become central to this dynamic market. The

    location of users, service composers, service providers, and data

    providers will affect the total cost of service provision. Different

    providers will need to make decisions about how to price and pay

    for resources. Each of them wants to maximize its profit as well

    as retain its position in the marketplace. Based on our earlier

    work, this paper addresses the effect of data intensity and the

    communication cost of mass data transfer on service composition,

    and proposes a service selection algorithm based on an enhanced

    ant colony system for data-intensive service provision. In this

    paper, the data-intensive service composition problem is modeled

    as an AND/OR graph, which is not only able to deal with sequence

    relations and switch relations, but is also able to deal with parallel

    relations between services. In addition, the performance of the

    service selection algorithm is evaluated by simulations.

Publication Date


  • 2013

Citation


  • Wang, L., Shen, J. & Beydoun, G. (2013). Enhanced ant colony algorithm for cost-aware data-intensive service provision. 2013 IEEE International Congress on Big Data: BigData Congress (pp. 227-234). United States: IEEE.

Scopus Eid


  • 2-s2.0-84888062859

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2345&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1336

Has Global Citation Frequency


Start Page


  • 227

End Page


  • 234

Place Of Publication


  • United States

Abstract


  • Huge collections of data have been created in recent

    years. Cloud computing has been widely accepted as the nextgeneration

    solution to addressing data-proliferation problems.

    Because of the explosion in digital data and the distributed nature

    of the cloud, as well as the increasingly large number of providers

    in the market, providing efficient cost models for composing dataintensive

    services will become central to this dynamic market. The

    location of users, service composers, service providers, and data

    providers will affect the total cost of service provision. Different

    providers will need to make decisions about how to price and pay

    for resources. Each of them wants to maximize its profit as well

    as retain its position in the marketplace. Based on our earlier

    work, this paper addresses the effect of data intensity and the

    communication cost of mass data transfer on service composition,

    and proposes a service selection algorithm based on an enhanced

    ant colony system for data-intensive service provision. In this

    paper, the data-intensive service composition problem is modeled

    as an AND/OR graph, which is not only able to deal with sequence

    relations and switch relations, but is also able to deal with parallel

    relations between services. In addition, the performance of the

    service selection algorithm is evaluated by simulations.

Publication Date


  • 2013

Citation


  • Wang, L., Shen, J. & Beydoun, G. (2013). Enhanced ant colony algorithm for cost-aware data-intensive service provision. 2013 IEEE International Congress on Big Data: BigData Congress (pp. 227-234). United States: IEEE.

Scopus Eid


  • 2-s2.0-84888062859

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2345&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1336

Has Global Citation Frequency


Start Page


  • 227

End Page


  • 234

Place Of Publication


  • United States