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Multi-objective Optimization for Data Placement Strategy in Cloud Computing

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Part of the Communications in Computer and Information Science book series (CCIS,volume 308)


In cloud computing, the data of processing and the data of transfering is charged at for the service of the provider. So, it is important to reduce the cost and to improve the performance for the consumer of the cloud computing. At present, the existing optimization algorithms only focus on one aspect , such as reducing the move of data, the processing time, the transferring time, the processing cost or the transferring cost. This paper makes a model for the multi-objective data placement and uses a particle swarm optimization algorithm to optimize the time and cost in cloud computing. The mode applied processors interaction graph to map the data of the task and the data center. The simulation experimental result manifests that the proposed method is more effective in time and cost.


  • Cloud Computing
  • Particle Swarm Optimization
  • Multi-Objective Optimization
  • Data Placement

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© 2012 Springer-Verlag Berlin Heidelberg

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Guo, L., He, Z., Zhao, S., Zhang, N., Wang, J., Jiang, C. (2012). Multi-objective Optimization for Data Placement Strategy in Cloud Computing. In: Liu, C., Wang, L., Yang, A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 308. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34040-6

  • Online ISBN: 978-3-642-34041-3

  • eBook Packages: Computer ScienceComputer Science (R0)