Skip to main content
Log in

Approximation for the mean value performance of locking algorithms for distributed database systems: a partitioned database

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Concurrent access to databases must be synchronized for correct execution of transactions and preservation of data consistency. This is usually achieved through use of concurrency control algorithms, amongst which locking algorithms are the most popular both in the literature and in practice. Several analytic methods have been developed for predicting the performance of centralized database systems employing locking algorithms for concurrency control, but very few exist for distributed database systems.

This paper proposes a method to approximate the mean value of various performance parameters in distributed database systems using locking for concurrency control. The main contribution of this approach is its ability to model the interaction between resource and data contention and the resulting effect on system performance. System performance is evaluated at a point where the interaction between these two factors is in equilibrium (stable state) and both the data and resource contention equations are simultaneously satisfied.

The model involves the solution of a set of simultaneous polynomial equations whose order is dependent on several problem parameters such as the number of nodes and number of locks requested per transaction. These equations are solved by an iterative procedure to evaluate approximate values of relative throughput, utilization of servers and transaction response time. The small computational requirements of the analytical model permit sensitivity analysis on network parameters, and can thus be effectively used by system designers to evaluate choices of communication line speeds, processor capacity, database sizes, etc.

The analytic approximations have been extensively verified against simulations for networks with up to 20 nodes. The input traffic was varied from light loads (about 5% utilization of the channels and processors) to heavy loads (about 65% utilization of the processors and channels). The discrepancies between the analytic approximation and the simulation were quite small (2–8%).

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. R. Agrawal, M.J. Carey and M. Livny, Models for studying concurrency control performance alternatives and implications,ACM SIGMOD (1985) pp. 108–121.

  2. R. Agrawal, M.J. Carey and M. Livny, Concurrency control performance modeling: Alternatives and implications, ACM Trans. Database Systems 12 (1987) 609–654.

    Google Scholar 

  3. D.Z. Badal, The analysis of the effects of concurrency control on distributed database system performance,Proc. 6th Int. Conf. on Very Large Data Bases (IEEE, 1980) pp. 376–383.

  4. D.Z. Badal, Concurrency control overhead or closer look at blocking vs. nonblocking concurrency control mechanisms,Proc. 6th Berkeley Conf. on Distributed Data Management and Computer Networks (1981) pp. 85–104.

  5. F. Baskett, K.M. Chandy, R.R. Muntz and F.G. Palacios, Open, closed and mixed networks of queues with different classes of customers, J. ACM 22 (April 1975) 248–260.

    Google Scholar 

  6. M. Basu, G. Dutta, T.W. Morgan and D.P. Heyman, A comparison of the queueing network analyzer with a CSMA/CD simulation,Proc. 6th Int. Phoenix Conf. on Computers and Communication (1987) pp. 379–383.

  7. P.A. Bernstein and N. Goodman, Approaches to concurrency control in distributed database systems,National Computer Conf. (1979) pp. 813–820.

  8. P.A. Bernstein and N. Goodman, Timestamp-based algorithms for concurrency control in distributed database systems,Proc. 6th Int. Conf. on Very Large Data Bases (1980) pp. 285–300.

  9. P.A. Bernstein and N. Goodman, Concurrency control in distributed database systems, ACM Comput. Surveys 13 (June 1981) 185–221.

    Google Scholar 

  10. P.A. Bernstein and N. Goodman, A sophisticate's introduction to distributed database concurrency control,Proc. 8th Int. Conf. on Very Large Data Bases (Sept. 1982) pp. 62–76.

  11. B. Bhargava, Performance evaluation of optimistic approach to distributed database systems and its comparison to locking,Proc. 3rd Int. Conf. on Distributed Computing Systems (IEEE, 1982) pp. 508–517.

  12. S.C. Bruell, G. Balbo and P.V. Afshari, Mean value analysis of mixed multiple class BCMP networks with load dependent service stations, Perform. Eval. 4 (1984).

  13. M.J. Carey, Modeling and evaluation of database concurrency control algorithms, Ph.D. Thesis, University of California, Berkeley (1983).

    Google Scholar 

  14. M.J. Carey, An abstract model of database concurrency control algorithms,SIGMOD, vol. 13, no. 4 (ACM IEEE, 1983) pp. 97–107.

    Google Scholar 

  15. W.K. Cheng an G.G. Belford, Analysis of update synchronization schemes in distributed databases,Proc. Distributed Computing, COMPCON 80, vol. 23 (IEEE, 1980) pp. 450–455.

    Google Scholar 

  16. A. Chesnais, E. Gelenbe and I. Mitrani, On the modeling of parallel access to shared data, Commun. ACM 26 (1983) 196–202.

    Google Scholar 

  17. H. Garcia-Molina, Performance comparison of two update algorithms for distributed databases,Int. Conf. on Distributed Data Management and Computer Networks (1977) pp. 108–119.

  18. H. Garcia-Molina, A concurrency control mechanism for distributed databases which uses centralized locking controllers,Int. Conf. on Distributed Data Management and Computer Networks (1979) pp. 113–124.

  19. E. Gelenbe and K. Sevcik, Analysis of update synchronization for multiple copy databases,Proc. 3rd Berkeley Conf. on Distributed Data Management and Computer Networks (1978) pp. 69–90.

  20. N. Griffeth and J.A. Miller, Performance modeling of database recovery protocols, IEEE Trans. Software Eng. SE-11 (1985) 564–572.

    Google Scholar 

  21. K.B. Irani and H.L. Lin, Queueing network models for concurrent transaction processing in a database system,Proc. ACM SIGMOD Int. Conf. on Management of Data (June 1979) pp. 134–142.

  22. J.R. Jackson, Networks of waiting lines, Oper. Res. 5 (1957).

  23. F. Kaumon, L. Kleinrock and R.R. Muntz, Queueing analysis of the ordering issue in a distributed database concurrency control mechanism,2nd Int. Conf. on Distributed Computing Systems (IEEE, 1981) pp. 13–23.

  24. L. Kleinrock,Queueing Systems, vol. 1 and 2 (Wiley-Interscience, 1976).

  25. A.M. Law, Statistical analysis of simulation output data, Oper. Res. 31 (November 1983).

  26. W.K. Lin and J. Nolte, Performance of two phase locking,Proc. 7th Berkeley Conf. on Distributed Data Management and Computer Networks (1982) pp. 131–160.

  27. W.K. Lin and J. Nolte, Read only transactions and two phase locking,Proc. 2nd Symp. on Reliability in Distributed Software and Database Systems (IEEE, 1982) pp. 85–93.

  28. W.K. Lin and J. Nolte, Communication delay and two phase locking,Int. Conf. on Distributed Computing Systems (IEEE, 1982) pp. 502–507.

  29. W.K. Lin and J. Nolte, Basic timestamp, multiversion timestamp and two phase locking,Int. Conf. on Very Large Data Bases (1983) pp. 109–119.

  30. D.A. Menasce and T. Nakanishi, Optimistic versus pessimistic concurrency control mechanisms in database management systems, Inform. Sys. 7 (1982) 13–27.

    Google Scholar 

  31. M.T. Ozsu and B.W. Weide, Modeling of distributed database concurrency control mechanisms using an extended Petri net formalism,Proc. 3rd Int. Conf. on Distributed Computing Systems, vol. 12 (IEEE, 1982) pp. 660–664.

    Google Scholar 

  32. P. Peinl and A. Reuter, Empirical comparison of database concurrency control schemes,Proc. 9th Int. Conf. on Very Large Data Bases (IEEE, 1983) pp. 97–108.

  33. D. Potier and Ph. Leblanc, Analysis of locking policies in database management systems, Commun. ACM 23 (Oct. 1980) 584–593.

    Google Scholar 

  34. K.H. Pun and G.G. Belford, Optimal granularity and degree of multiprogramming in a distributed database system, Data Eng. (1986) 13–20.

  35. A. Raghuram, Performance analysis of concurrency control algorithms in distributed database systems, Ph.D. Dissertation, Drexel University (Jan. 1988).

  36. B. Rajaraman, Convergence properties of mean value performance estimators in distributed database systems, M.S. Thesis, Drexel University (Nov. 1988).

  37. D.P. Ries, The effect of concurrency control on the performance of a distributed management system,Int. Conf. on Distributed Data Management and Computer Networks (1979) pp. 75–112.

  38. F.S. Roberts,Applied Combinatorics (Prentice-Hall, 1984).

  39. D.J. Rosenkrantz, R.E. Stearns and P.M. Lewis, II, System level concurrency control for distributed database systems, ACM Trans. Database Systems 3 (1978) 178–198.

    Google Scholar 

  40. C.K. Sevcik, Comparison of concurrency control methods using analytic models, Info. Proc. (1983) 847–858.

  41. A. Singhal, A.P. Sheth and M.T. Liu, An analysis of the effect of the network load and topology on the performance of a concurrency control algorithm in distributed database systems, IEEE (1984) 45–54.

  42. Y.C. Tay, A mean value performance model for locking in databases, Ph.D. dissertation, Harvard University (Feb. 1984).

  43. Y.C. Tay and R. Suri, Choice and performance in locking for databases,Proc. 10th Int. Conf. on Very Large Data Bases (Aug. 1984) pp. 119–128.

  44. Y.C. Tay, R. Suri and N. Goodman, A mean value performance model for locking in databases: the no-waiting case, J. ACM 32 (July 1985) 618–651.

    Google Scholar 

  45. Y.C. Tay, N. Goodman and R. Suri, Locking performance in centralized databases, ACM Trans. Database Systems 10 (December 1985) 415–462.

    Google Scholar 

  46. A. Thomasian, An iterative solution to the queueing network model of a DBMS with dynamic locking,Proc. 13th Computer Measurement Group Conf. (Dec. 1982) pp. 252–261.

  47. A. Thomasian and I.K. Ryu, A decomposition solution to the queueing network model of the centralized DBMS with static locking,Proc. ACM SIGMETRICS Conf. on Measurement and Modeling of Computer Systems (1983) pp. 82–92.

  48. W. Whitt, The queueing network analyzer, Bell Sys. Tech. J. 62 (November, 1983) 2779–2815.

    Google Scholar 

  49. W. Whitt, Performance of the queueing network analyzer, Bell Sys. Tech. J. 62 (November, 1983) 2817–2843.

    Google Scholar 

  50. W. Whitt, Approximations for departure processes and queues in series, Naval Res. Log. Quarterly 31 (1984) 499–521.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

This work was done while the authors were at Drexel University, Philadelphia.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Raghuram, A., Morgan, T.W., Rajaraman, B. et al. Approximation for the mean value performance of locking algorithms for distributed database systems: a partitioned database. Ann Oper Res 36, 299–345 (1992). https://doi.org/10.1007/BF02094335

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02094335

Keywords

Navigation