Advertisement

Dynamic multiprocessor scheduling for supporting real-time constraints

  • Shin-Mu Tseng
  • Y. H. Chin
  • Wei-Pang Yang
Session 2
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1345)

Abstract

A real-time transaction carries the constraint that it must be completed before its assigned deadline. For some real-time applications, a successfully completed transaction may contribute a value to the system to reflect its profit. Satisfying both constraints of maximizing the totally obtained profits and minimizing the number of missed transactions simultaneously under various system conditions is a challenge. In this paper, we present a dynamic scheduling policy named Dynamic Processor Allocation (DPA) for scheduling value-based transactions in a multiprocessor real-time database system. The DPA policy allocates the processors to both of high-value transactions and urgent transactions dynamically by utilizing the statistical information of the system. Through simulation experiments, DPA is shown to deliver good performance in both maximizing the totally obtained profits and minimizing the number of missed transactions under various system environments. Hence, it resolves the drawbacks of the existing scheduling policies which can deliver good performance only at normal loads or at high loads.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    R. Abbott and H. Garcia-Molina, “Scheduling real-time transactions,” ACM SIGMOD Record, vol. 17, no. 1, pp. 71–81, March 1988.Google Scholar
  2. 2.
    R. Abbott and H. Garcia-Molina, “Scheduling real-time transactions: A performance evaluation,” Proc. 14th Int'l Conf. Very Large Databases, pp. 1–12, Los Angeles, Aug. 1988.Google Scholar
  3. 3.
    R. Abbott and H. Garcia-Molina, “Scheduling real-time transactions: A performance evaluation,” ACM Trans. Database Systems, vol. 17, no. 3, pp. 513–560, Sep. 1992.CrossRefGoogle Scholar
  4. 4.
    D. Agrawal, A. E. Abbadi, and R. Jeffers, “Ordered shared locks for real-time databases,” VLDB J., vol. 4, no. 1, pp. 87–126, 1995.CrossRefGoogle Scholar
  5. 5.
    S. Biyabani, J. Stankovic, and K. Ramamritham, “The integration of deadline and criticalness in hard real-time scheduling,” Proc. 9th IEEE Real-Time Systems Symp., Huntsville, AL, Dec. 1988.Google Scholar
  6. 6.
    A. Buchmann, D. McCarthy, M. Hsu, and U. Dayal, “Time-critical database scheduling: A framework for integrating real-time scheduling and concurrency control,” Proc. 5th Int'l Conf. Data Engi., Los Angels, Apr. 1989.Google Scholar
  7. 7.
    P. A. Fishwick, “SimPack: C-based simulation tool package version 2,” University of Florida, 1992.Google Scholar
  8. 8.
    J. R. Haritsa, M. J. Carey, and M. Livny, “Dynamic real-time optimistic concurrency control,” Proc. 11th IEEE Real-Time Systems Symp., pp. 94–103, Orlando, Florida, Dec. 1990.Google Scholar
  9. 9.
    J. R. Haritsa, M. J. Carey, and M. Livny, “Value-based scheduling in real-time database systems,” VLDB J., vol. 2, no. 2, pp. 117–152, 1993.CrossRefGoogle Scholar
  10. 10.
    D. Hong, T. Johnson, and S. Chakravarthy, “Real-time transaction scheduling: a cost conscious approach,” Proc. ACM Int'l Conf. Management Data, pp. 197–206, Washington, DC, May, 1993.Google Scholar
  11. 11.
    J. Huang, J. Stankovic, D. Towsley, and K. Ramaritham, “Experimental evaluation of real-time transaction processing,” Proc. 10th IEEE Real-Time Systems Symp., pp. 144–153, Santa Monica, CA, Dec. 1989.Google Scholar
  12. 12.
    J. Huang and J. Stankovic, “Concurrency control in real-time database systems: optimistic scheme vs. two-phase locking,” COINS Technical Report 90-66, University of Massachusetts, Amherst, MA, 1990.Google Scholar
  13. 13.
    E. Jensen, C. Locke, and H. Tokuda, “A time-driven scheduling model for real-time operating systems,” Proc. 6th IEEE Real-Time Systems Symp., pp. 112–122, Dec. 1985.Google Scholar
  14. 14.
    Y. Lin and S. H. Son, “Concurrency control in real-time database systems by dynamic adjustment of serialization order,” Proc. 11th IEEE Real-Time Systems Symp., Orlando, Florida, Dec. 1990.Google Scholar
  15. 15.
    J. Lee and S. H. Son, “Using dynamic adjustment of serialization order for real-time database systems,” Proc. 14th IEEE Real-Time Systems Symp., pp. 66–75, Raleigh-Durham, N.C., Dec. 1993.Google Scholar
  16. 16.
    D. Menasce and T. Nakanishi, “Optimistic vs. pessimistic concurrency control mechanisms in database management systems,” Information Systems, vol. 7, no. 1, pp. 13–27, 1982.CrossRefGoogle Scholar
  17. 17.
    R. Sargent, “Statistical analysis of simulation output data,” Proc. 4th Ann. Symp. Simulation Computer Systems, pp. 39–50, Aug. 1976.Google Scholar
  18. 18.
    S. M. Tseng and Y. H. Chin, “Prescheduling policy for real-time concurrency control: a performance evaluation,” Journal of Systems Integration, vol. 3, no. 3, pp. 23–42, 1993.CrossRefGoogle Scholar
  19. 19.
    S. M. Tseng, Y. H. Chin, and W. P. Yang, “Scheduling real-time transactions with dynamic values: a performance evaluation,” Proc. 2nd Int'l Workshop Real-Time Computing Systems and Applications, pp. 60–67, Tokyo, Japan, Oct. 1995.Google Scholar
  20. 20.
    S. M. Tseng, Y. H. Chin, and W. P. Yang, “An adaptive value-based scheduling policy for multiprocessor real-time database systems,” Proc.8 th Int'l Conf. Database and Expert Systems Applications, pp. 254–259, France, Sep. 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Shin-Mu Tseng
    • 1
  • Y. H. Chin
    • 2
  • Wei-Pang Yang
    • 1
  1. 1.Institute of Computer and Information ScienceNational Chiao Tung UniversityHsinchuTaiwan, R.O.C.
  2. 2.Institute of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan, R.O.C.

Personalised recommendations