Advertisement

Optimizing Real-Time Ordered-Data Broadcasts in Pervasive Environments Using Evolution Strategy

  • Rinku Dewri
  • Darrell Whitley
  • Indrajit Ray
  • Indrakshi Ray
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

We consider the problem of real-time data broadcast scheduling in pervasive systems with soft deadlines and constraints on the order in which data items should be broadcast to be useful. The broadcast schedule needs to be generated to provide a certain level of quality of service. Thus, the real-time scheduler has to effectively trade-off between its running time and the quality of schedules generated. We use an evolution strategy to solve the problem. The variants tested includes (1 + λ)-ES, (1,λ)-ES, and a (2 + 1)-ES with a modified Syswerda recombination operator, as well as a genetic algorithm.

Keywords

Data broadcasting Scheduling Evolution strategy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ravindran, B., Jensen, E.D., Li, P.: On Recent Advances in Time/Utility Function Real-Time Scheduling and Resource Management. In: 8th IEEE ISORC, pp. 55–60 (2005)Google Scholar
  2. 2.
    Jensen, E., Locke, C., Tokuda, H.: A Time Driven Scheduling Model for Real-Time Operating Systems. In: 6th IEEE RTSS, pp. 112–122 (1985)Google Scholar
  3. 3.
    Buttazzo, G., Spuri, M., Sensini, F.: Value vs. Deadline Scheduling in Overload Conditions. In: 16th IEEE RTSS, pp. 90–99 (1995)Google Scholar
  4. 4.
    Wu, H., Balli, U., Ravindran, B., Jensen, E.D.: Utility Accrual Real-Time Scheduling Under Variable Cost Functions. In: 11th IEEE RTCSA, pp. 213–219 (2005)Google Scholar
  5. 5.
    Chehadeh, Y., Hurson, A., Kavehrad, M.: Object Organization on a Single Broadcast Channel in the Mobile Computing Environment. Multimedia Tools and Applications 9(1), 69–94 (1999)CrossRefGoogle Scholar
  6. 6.
    Hurson, A., Chehadeh, Y., Hannan, J.: Object Organization on Parallel Broadcast Channels in a Global Information Sharing Environment. In: 19th IEEE IPCCC, pp. 347–353 (2000)Google Scholar
  7. 7.
    Huang, J.L., Chen, M.S.: Dependent Data Broadcasting for Unordered Queries in a Multiple Channel Mobile Environment. IEEE Transactions on Knowledge and Data Engineering 16(9), 1143–1156 (2004)CrossRefGoogle Scholar
  8. 8.
    Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systemenach Prinzipien der biologischen Evolution. PhD thesis, Technical University of Berlin (1970)Google Scholar
  9. 9.
    Syswerda, G.: Schedule Optimization Using Genetic Algorithms. In: Davis, L. (ed.) The Genetic Algorithms Handbook (1990)Google Scholar
  10. 10.
    Breslau, L., Cao, P., Fan, L., Phillips, G., Shenker, S.: Web Caching and Zipf-Like Distributions: Evidence and Implications. In: IEEE INFOCOM, pp. 126–134 (1999)Google Scholar
  11. 11.
    Hameed, S., Vaidya, N.: Efficient Algorithms for Scheduling Data Broadcast. Wireless Networks 5(3), 183–193 (1999)CrossRefGoogle Scholar
  12. 12.
    Lee, V.C., Wu, X., Ng, J.K.Y.: Scheduling Real-Time Requests in On-Demand Data Broadcast Environments. Real-Time Systems 34(2), 83–99 (2006)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rinku Dewri
    • 1
  • Darrell Whitley
    • 1
  • Indrajit Ray
    • 1
  • Indrakshi Ray
    • 1
  1. 1.Colorado State UniversityFort CollinsUSA

Personalised recommendations