Skip to main content

An Efficient Workload Redistribution Algorithm in Grid Computing Systems Using Genetic Information

  • Conference paper
  • 1069 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 310))

Abstract

There are various machines in grid system which was performed programs for multiple applications. In grid systems, some machines have a low utilization. But other machines have high utilization. For applications that are grid enabled, the grid can offer a resource balancing effect by scheduling grid jobs on machines with low utilization. A scheduling policy is used to manage many resources efficiently. When jobs communicate with each other, the internet, with storage resources, an advanced scheduler could schedule them to minimize communication traffic or minimize the distance of the communications for turnaround time improvement. Therefore, we propose a workload redistribution algorithm to improve the performance of grid scheduler using genetic information.

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   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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. Jacob, B., Brown, M., Fukui, K., Trivedi, N.: Introduction to Grid Computing. IBM Redbooks (2005)

    Google Scholar 

  2. Li, Y., Lan, Z.: A Survey of Load Balancing in Grid Computing. In: Zhang, J., He, J.-H., Fu, Y. (eds.) CIS 2004. LNCS, vol. 3314, pp. 280–285. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Grefenstette, J.: Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. on SMC SMC-16, 122–128 (1986)

    Google Scholar 

  4. Eager, D.L., Lazowska, E.D., Zahorjan, J.: Adaptive Load Sharing in Homogeneous Distributed Systems. IEEE Trans. on Software Engineering 12(5), 662–675 (1986)

    Google Scholar 

  5. Shivaratri, N.G., Krueger, P., Singhal, M.: Load Distributing for Locally Distributed Systems. IEEE Computer 25(12), 33–44 (1992)

    Article  Google Scholar 

  6. Kunz, T.: The Influence of Different Workload Descriptions on a Heuristic Load Balancing Scheme. IEEE Trans. on Software Engineering 17(7), 725–730 (1991)

    Article  Google Scholar 

  7. Thomas, G.R.: Ten Reasons to Use Divisible Load Theory. IEEE Computer 36(5), 63–68 (2003)

    Article  Google Scholar 

  8. International Association for High Throughput Computing, http://www.cs.wisc.edu/condor

  9. Grid Forum in Korea, http://www.gridforumforea.org

  10. http://www.globus.org

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chun, BT., Lee, SH. (2012). An Efficient Workload Redistribution Algorithm in Grid Computing Systems Using Genetic Information. In: Lee, G., Howard, D., Ślęzak, D., Hong, Y.S. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Communications in Computer and Information Science, vol 310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32692-9_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32692-9_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32691-2

  • Online ISBN: 978-3-642-32692-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics