Skip to main content

Research on Auto-regressive Load Balancing Model Based on Multi-agents

  • Conference paper
  • 877 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 238))

Abstract

This paper proposes the auto-regressive load balancing model based on multi-agents. By the simulation experiments, we prove the load balancing mechanism can expand the server’s "capacity" and improve the system throughput. The method overcomes the shortages of imbalance and instability of the server system. Therefore, the model can improve the system utilization factor of server system, achieve load balance.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chen, T., Lin, Y.-C.: A fuzzy-neural system incorporating unequally important expert opinions for semiconductor yield forecasting. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16(1), 35–58 (2008)

    Article  MathSciNet  Google Scholar 

  2. Laszlo, M., Mukherjee, S.: A genetic algorithm that exchanges neighboring centers for k-means clustering. Pattern Recognition Letters 28, 2359–2366 (2007)

    Article  Google Scholar 

  3. Abraham, A., Das, S., Konar, A.: Document clustering using differential evolution. In: Proceedings of 2006 IEEE Congress on Evolutionary Computation, pp. 1784–1791 (2006)

    Google Scholar 

  4. Easwaran, A., Shin, I., Lee, I.: Optimal virtual cluster based scheduling. In: Euromicro Conference on Real-Time Systems, vol. 43(1), pp. 25–59 (2009)

    Google Scholar 

  5. Yan, K.Q., et al.: A hybrid load balancing policy underlying grid computing environment. Journal of Computer Standards & Interfaces, 161–173 (2007)

    Google Scholar 

  6. Minh, T.N., Thoai, N., Son, N.T., Ky, D.X.: Project oriented scheduler for cluster system. In: Modeling, Simulation and Optimization of Complex Process, pp. 393–402. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  7. Ahmad, A., Dey, L.: A feature selection technique for classifieatory analysis. Pattern Recognition Letters 26, 43–56 (2005)

    Article  Google Scholar 

  8. Shuai, D., Shuai, Q., Dong, Y.: Particle model to optimize resource allocation and task assignment. Journal of Information Systems (32), 987–995 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dong, Y., Xiao, S. (2011). Research on Auto-regressive Load Balancing Model Based on Multi-agents. In: Zhiguo, G., Luo, X., Chen, J., Wang, F.L., Lei, J. (eds) Emerging Research in Web Information Systems and Mining. WISM 2011. Communications in Computer and Information Science, vol 238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24273-1_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24273-1_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24272-4

  • Online ISBN: 978-3-642-24273-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics