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M-ary commitment protocol with partially ordered domain

  • Iwao Shimojo
  • Takayuki Tachikawa
  • Makoto Takizawa
Data Interchange
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1308)

Abstract

Distributed applications are realized by the cooperation of multiple processes. A group of the processes have to make consensus to do the cooperation. The processes exchange the values with the other processes to make consensus. The processes are referred to as consent if each process takes one value which satisfies a consensus condition. A dominant relation among the values is defined to show what values the processes can take after taking a value in the consensus protocol. Each process decides what value to be taken after taking one value by using the dominant relation. In this paper, we discuss how to make consensus in a group of multiple processes by using the dominant relation.

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References

  1. 1.
    Barborak, M., Malek, M., and Dahbura, A.: The Consensus Problem in FaultTolerant Computing, ACM Computing Surveys, Vol.25, No.2, pp.182–184, 198-199 (1993).Google Scholar
  2. 2.
    Bernstein, P. A., Hadzilacos, V., and Goodman, N.: Concurrency Control and Recovery in Database Systems, Addison-Wesley, pp.222–261(1987).Google Scholar
  3. 3.
    Birman, K. P., Schiper, A., and Stephenson, P.: Lightweight Causal and Atomic Group Multicast, ACM Trans. on Computer Systems, Vol.9, No.3, pp.272–314 (1991).Google Scholar
  4. 4.
    Ellis, C. A., Gibbs, S. J., and Rein, G. L.: Groupware, Comm. ACM, Vol.34, No.1, pp.38–58 (1991).Google Scholar
  5. 5.
    Fischer, J. M., Lynch, A. N., and Paterson, S. M.: Impossibility of Distributed Consensus with One Faulty Process, Journal of ACM, Vol.32, No.2, pp.374–382 (1985).Google Scholar
  6. 6.
    Gray, J.: Notes on Database Operating Systems, An Advanced Course, Lecture Notes in Computer Science, No.60, pp.393–481(1978).Google Scholar
  7. 7.
    Lamport, L.: Time, Clocks, and the Ordering of Events in a Distributed System, Comm. ACM, Vol.21, No.7, pp.558–565 (1978).Google Scholar
  8. 8.
    Lamport, L. and Shostak, R.: The Byzantine Generals Problem, ACM Trans. Programming Languages and Systems, Vo1.4, No3, pp.382–401 (1982).Google Scholar
  9. 9.
    Nakamura, A. and Takizawa, M.: Causally Ordering Broadcast Protocol, Proc. of IEEE ICDCS-14, pp.48–55(1994).Google Scholar
  10. 10.
    Ozsu, M. T. and Valduriez, P.: Principle of Distributed Database Systems, Prentice-Hall(1990).Google Scholar
  11. 11.
    Skeen, D. and Stonebraker, M.: A Formal Model of Crash Recovery in a Distributed System, IEEE Computer Society Press, Vol.SE-9, No.3, pp.219–228 (1983).Google Scholar
  12. 12.
    Tachikawa, T. and Takizawa, M.: Selective Total Ordering Broadcast Protocol, Proc. of IEEE ICNP-94, pp.212–219(1994).Google Scholar
  13. 13.
    Turek, J. and Shasha, D.: The Many Faces of Consensus in Distributed Systems, Distributed Computing Systems, IEEE Computer Society Press, pp.83–91(1994).Google Scholar
  14. 14.
    Yahata, C., Sakai, J., and Takizawa, M.: “Generalization of Consensus Protocols,” Proc. of the 9th IEEE Int'l Conf. on Information Networking (ICOIN-9), pp.419–424(1994).Google Scholar
  15. 15.
    Yahata, C. and Takizawa, M.: General Protocol for Consensus in Distributed Systems, Proc. of DEXA(Lecture Notes in Computer Science, No. 978, Springer-verlag), pp.227–236(1995).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Iwao Shimojo
    • 1
  • Takayuki Tachikawa
    • 1
  • Makoto Takizawa
    • 1
  1. 1.Dept. of Computers and Systems EngineeringTokyo Denki UniversitySaitamaJapan

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