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Cost and time aware ant colony algorithm for data replica in alpha magnetic spectrometer experiment

Conference Paper


Download full-text (Open Access)

Abstract


  • Huge collections of data have been created in

    recent years. Cloud computing provides a way to enable massive

    amounts of data to work together as data-intensive services.

    Considering Big Data and the cloud together, which is a practical

    and economical way to deal with Big Data, will accelerate the

    availability and acceptability of analysis of the data. Providing an

    efficient mechanism for optimized data-intensive services will become

    critical to meet the expected growth in demand. Because the

    competition is an extremely important factor in the marketplace,

    the cost model for data-intensive service provision is the key to

    provide a sustainable service market. As data play the dominant

    role in execution of data-intensive service composition, the cost

    and access response time of data sets influence the quality of the

    service that requires the data sets. In this paper, a data replica

    selection optimization algorithm based on an ant colony system is

    proposed. The performance of the data replica selection algorithm

    is evaluated by simulations. The background application of the

    work is the Alpha Magnetic Spectrometer experiment, which

    involves large amounts of data being transferred, organized

    and stored. It is critical and challenging to be cost and time

    aware to manage the data and services in this intensive research

    environment.

Authors


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

Publication Date


  • 2013

Citation


  • Wang, L., Luo, J., Shen, J. & Dong, F. (2013). Cost and time aware ant colony algorithm for data replica in alpha magnetic spectrometer experiment. IEEE 2nd International Congress on Big Data (pp. 247-254). United States: IEEE.

Scopus Eid


  • 2-s2.0-84885980691

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2107&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1098

Has Global Citation Frequency


Start Page


  • 247

End Page


  • 254

Place Of Publication


  • United States

Abstract


  • Huge collections of data have been created in

    recent years. Cloud computing provides a way to enable massive

    amounts of data to work together as data-intensive services.

    Considering Big Data and the cloud together, which is a practical

    and economical way to deal with Big Data, will accelerate the

    availability and acceptability of analysis of the data. Providing an

    efficient mechanism for optimized data-intensive services will become

    critical to meet the expected growth in demand. Because the

    competition is an extremely important factor in the marketplace,

    the cost model for data-intensive service provision is the key to

    provide a sustainable service market. As data play the dominant

    role in execution of data-intensive service composition, the cost

    and access response time of data sets influence the quality of the

    service that requires the data sets. In this paper, a data replica

    selection optimization algorithm based on an ant colony system is

    proposed. The performance of the data replica selection algorithm

    is evaluated by simulations. The background application of the

    work is the Alpha Magnetic Spectrometer experiment, which

    involves large amounts of data being transferred, organized

    and stored. It is critical and challenging to be cost and time

    aware to manage the data and services in this intensive research

    environment.

Authors


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

Publication Date


  • 2013

Citation


  • Wang, L., Luo, J., Shen, J. & Dong, F. (2013). Cost and time aware ant colony algorithm for data replica in alpha magnetic spectrometer experiment. IEEE 2nd International Congress on Big Data (pp. 247-254). United States: IEEE.

Scopus Eid


  • 2-s2.0-84885980691

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2107&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1098

Has Global Citation Frequency


Start Page


  • 247

End Page


  • 254

Place Of Publication


  • United States