<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Cloud computing has become a new platform for personal computing. However, while designing the strategy of data placement, there still lacks the consideration of systematic diversity of distributed transaction costs. This paper proposes the use of genetic algorithms to address the data placement problem in cloud computing. This strategy has adequately considered the correlation between data slices to minimize the total cost of distributed transactions. Compared to other methods, genetic algorithms have proven to comprehensively consider the correlation between the data slices in cloud computing, therefore greatly reducing the amount and cost of distributed transactions.</p>
</abstract>

Cloud computing has become a new platform for personal computing. However, while designing the strategy of data placement, there still lacks the consideration of systematic diversity of distributed transaction costs. This paper proposes the use of genetic algorithms to address the data placement problem in cloud computing. This strategy has adequately considered the correlation between data slices to minimize the total cost of distributed transactions. Compared to other methods, genetic algorithms have proven to comprehensively consider the correlation between the data slices in cloud computing, therefore greatly reducing the amount and cost of distributed transactions.

The Design and Evaluation of a Strategy of Data Placement in Cloud Computing Platform

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Cloud computing has become a new platform for personal computing. However, while designing the strategy of data placement, there still lacks the consideration...

The Design and Evaluation of a Strategy of Data Placement in Cloud Computing Platform

Published Online: Mar 01, 2014

Page range: 13 - 30

Received: Oct 10, 2013

Accepted: Feb 02, 2014

DOI: https://doi.org/10.21307/ijssis-2017-643

© 2014 Wei Guo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.