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ARPP: Ant Colony Algorithm Based Resource Performance Prediction Model

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Methods and Tools of Parallel Programming Multicomputers (MTPP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6083))

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Abstract

Resource performance prediction is the basis of dynamic load balance in distributed computing. A model for resource performance prediction named ARPP is introduced and carried out. ARPP model monitors key parameters of resources and estimates the directions using ant algorithm. The implement and analysis of ARPP is based on GridSim simulator and the process of astronomical image mosaicking application. The experiment result shows the efficiency of the model and the determination of optimized parameters.

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References

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Author information

Authors and Affiliations

  1. School of Computer Science & Technology, Tianjin University, Tianjin, 300072, China

    Ce Yu, Kelang Xiong, Jizhou Sun & Jian Xiao

  2. Network Center, Hebei University of Technology, Tianjin, 300130, China

    Yanyan Huang

Authors
  1. Ce Yu
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  2. Kelang Xiong
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  3. Jizhou Sun
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  4. Yanyan Huang
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  5. Jian Xiao
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, Chung Hua University, 300, Hsinchu, Taiwan, China

    Ching-Hsien Hsu

  2. Supercomputer Software Department, Institute of Computational Mathematics and Mathematical Geophysics, Pr. Lavrentieva, ICM&MG RAS, 630090, Novosibirsk, Russia

    Victor Malyshkin

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© 2010 Springer-Verlag Berlin Heidelberg

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Yu, C., Xiong, K., Sun, J., Huang, Y., Xiao, J. (2010). ARPP: Ant Colony Algorithm Based Resource Performance Prediction Model. In: Hsu, CH., Malyshkin, V. (eds) Methods and Tools of Parallel Programming Multicomputers. MTPP 2010. Lecture Notes in Computer Science, vol 6083. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14822-4_19

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  • DOI: https://doi.org/10.1007/978-3-642-14822-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14821-7

  • Online ISBN: 978-3-642-14822-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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