Advertisement

Journal of Systems Integration

, Volume 3, Issue 1, pp 23–42 | Cite as

Prescheduling policy for real-time concurrency control: A performance evaluation

  • S. M. Tseng
  • Y. H. Chin
Article

Abstract

A new priority management policy, aprescheduling policy, is proposed. This policy can be applied on any conventional concurrency control protocol to schedule a real-time transaction. Costly preemption is avoided by the prescheduling policy, and parsing dataset of a transaction is not needed. Three widely used conventional concurrency control protocols (dynamic two-phase locking, basic timestamp ordering, and optimistic) are incorporated with the prescheduling policy to form three real-time concurrency control protocols. Performance of the three protocols is evaluated from three different viewpoints: database management systems, protocols, and transaction. From a database management system viewpoint, we show the prescheduling policy can improve the performance of protocols by raising thevalid ratio and reducingrestart counts. In general, two-phase locking with the prescheduling policy performs the best in most cases and yields the best choice for concurrency control in a real-time application. Deciding factors that affect performance of each protocol are identified from protocol viewpoint. Some suggestions are given for writing a timely transaction from the aspect of transaction viewpoint.

Key Words

Real-time valid ratio concurrency control prescheduling performance evaluation 

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: a performance evaluation,” inProceedings 14th Int. Conf. on Very Large Data Base, Los Angeles, CA, pp. 1–12, 1988.Google Scholar
  2. 2.
    R. Abbott and H. Garcia-Molina, “Scheduling real-time transactions with disk resident data,” inProceedings 15th Int. Conf. on Very Large Data Base, Amsterdam, pp. 385–396, 1989.Google Scholar
  3. 3.
    R. Agrawal, M. J. Carey, and M. Livny, “Concurrency control performance modeling: alternatives and implications,”ACM Trans. Database Syst., vol. 12, no. 4, pp. 609–654, 1987.CrossRefGoogle Scholar
  4. 4.
    P. A. Bernstein, D. W. Shipman, and J. B. Rothnie, Jr., “Concurrency control in a system for distributed database (SDD-1),”ACM Trans. Database Syst., vol. 5, no. 1, pp. 18–51, 1980.CrossRefGoogle Scholar
  5. 5.
    P. Bernstein and N. Goodman, “Timestamp-based algorithms for concurrency control in distributed database systems,” inProceedings 6th Int. Conf. on Very Large Data Base, Montreal, Canada, pp. 285–300, October 1980.Google Scholar
  6. 6.
    A. P. Buchmann, D. R. McCarthy, M. Hsu, and V. Dayal, “Time-critical database scheduling: A framework for integrating real-time scheduling and concurrency control,” inProc. of IEEE Real-Time Systems Symposium, Santa Molic, CA, pp. 470–480, 1989.Google Scholar
  7. 7.
    M. J. Carey and M. R. Stonebraker, “The performance of concurrency control algorithms for database management systems,” inProceedings 10th Int. Conf. on Very Large Data Base, Singapore, pp. 107–117, 1984.Google Scholar
  8. 8.
    K. Chrysanthis and K. Ramamritham, “ACTA: A framework for specifying and reasoning about transaction structure and behavior,” inProceedings of the 1990 ACM SIGMOD International Conference on Management of Data, Atlantic City, NJ, pp. 194–203, 1990.Google Scholar
  9. 9.
    J. Gray, “Notes on database operating systems,” inOperating Systems: An Advanced Course, Springer-Verlag, 1979.Google Scholar
  10. 10.
    E. D. Jensen, C. D. Locke, and H. Tokuda, “A time-driven model for real-time operating systems,” inProc. IEEE Real-Time Systems Symposium, San Jose, CA, pp. 112–122, 1986.Google Scholar
  11. 11.
    A. Kumar and M. Stonebraker, “Performance evaluation of an operating system transactions manager,” inProceedings 13th Int. Conf. on Very Large Data Base, Brighton, England, pp. 473–481, 1987.Google Scholar
  12. 12.
    H. T. Kung and J. T. Robinson, “On optimistic methods for concurrency control,”ACM Trans. Database Syst., vol. 6, no. 2, pp. 213–226, 1981.CrossRefGoogle Scholar
  13. 13.
    C. U. Orji, L. Lilien, and J. Hyziak, “A performance analysis of an optimistic and a basic timestamp-ordering concurrency control algorithms for centralized database systems,” inProc. of IEEE International Conference on Data Engineering, Los Angeles, CA, pp. 64–71, 1988.Google Scholar
  14. 14.
    R. Sargent, “Statistic analysis of simulation output data,” inProceedings of the 4th Annual Symposium on the Simulation of Computer Systems, pp. 39–50, August 1976.Google Scholar
  15. 15.
    L. Sha, R. Rajkumar, and J. P. Lehoczky, “Concurrency control for distributed real-time databases,”SIGMOD RECORD, vol. 17, no. 1, pp. 82–98, 1988.Google Scholar
  16. 16.
    A. D. Stoyenko, V. C. Hamacher, and R. C. Holt, “Analyzing hard-real-time programs for guaranteed schedulability,”IEEE Trans. Software Engineering, vol. 17, no. 8, pp. 737–750, July 1991.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • S. M. Tseng
    • 1
  • Y. H. Chin
    • 1
  1. 1.Institute of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

Personalised recommendations