KRISHNA — Concurrency Control Algorithms based on Dynamic Attributes

  • Vijay Kumar
  • James Mumper


Three new concurrency control mechanisms that utilizes the dynamic attribute of concurrent transactions for resolving conflicts are presented. Our study shows that their performance is superior to all well-known two-phase concurrency control mechanisms. There are a large number of such algorithms but we report our findings since our approach has some unique features worth exploring.


Conflict Resolution Absolute Priority Transaction Size Conflict Rate Concurrent Transaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    R. Agrawal, at. al., “The Performance of Alternative Strategies for dealing with Deadlocks in Database Management Systems”, IEEE Trans on SE„ Vol. SE-13, No. 12, Dec. 1987.Google Scholar
  2. [2]
    K.P. Eswaran, at. al., “The Notions of Consistency and Predicate Locks in database Systems”, Comm. ACM Vol. 19, No. 11, Nov. 1976.Google Scholar
  3. [3]
    P. Franaszek and J. T. Robinson, “Limitations of concurrency in transaction procesing”, ACM TODS., 10 (1), pp. 1–28, Mar. 1985.MATHCrossRefGoogle Scholar
  4. [4]
    M. Hsu and B. Zhang, “The Mean Value Approach to Performance Evaluation of Cautious Waiting”, Submitted for Publication, 1987.Google Scholar
  5. [5]
    V.Kumar, “Performance Comparison of Database Concurrency Control Mechanisms based on Two-Phase Locking, Timestamping and Mixed Approach”, Information Sciences: An International Journal, Vol. 51, No. 3, 1990.Google Scholar
  6. [6]
    V. Kumar and Meichun Hsu, “A Superior Two-Phase Locking Algorithm and Its Performance”, Information Sciences: An International Journal,(Accepted for publication).Google Scholar
  7. [7]
    D.J. Rosenkrantz at. al, “System level concurrency control for distributed database systems,” ACM Trans. Database Syst., Vol. 3, No. 2, pp. 178–198, June 1978.MathSciNetCrossRefGoogle Scholar
  8. [8]
    Shemer, J.E. and Collmeyer, A.J., “Database sharing-A study of interference, roadblocks, and deadlocks”, Proc. 1972 ACM SIGFIDET Workshop, pp. 147–163.Google Scholar
  9. [9]
    Y.C. Tay at. al., “Locking performance in centralized databases,” ACM TODS, 10 (4), pp. 415–462, Dec. 85.Google Scholar

Copyright information

© Springer-Verlag Wien 1991

Authors and Affiliations

  • Vijay Kumar
    • 1
  • James Mumper
    • 1
  1. 1.Computer Science TelecommunicationsUniversity of Missouri-Kansas CityKansas CityUSA

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