Optimizing voting-type algorithms for replicated data

  • Akhil Kumar
  • Arie Segev
Efficiency By Replicated Data
Part of the Lecture Notes in Computer Science book series (LNCS, volume 303)


The main objectives of data replication are improved availability and reduced communications cost for queries. Maintaining the various copies consistent, however, increases the communications cost incurred by updates. For a given degree of replication, the choice of a specific concurrency control algorithm can have a significant impact on the total communications cost. In this paper we present various models for analyzing and understanding the trade-offs between the potentially opposing objectives of maximum resiliency and minimum communications cost in the context of the quorum consensus class of algorithms. It is argued that an optimal vote assignment is one which meets given resiliency goals and yet incurs the least communications cost compared with all other alternative assignments. A mathematical model for vote assignment is developed, and optimal algorithms are presented. It is demonstrated that significant cost savings can be realized from these approaches.


Communication Cost Major Site Link Failure Concurrency Control Site Failure 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [BARB86]
    Barabara, D., Garcia-Molina, H., and Spauster, A., “Protocols for Dynamic Vote Reassignment”, Technical Report, Department of Computer Science, Princeton University, May 1986.Google Scholar
  2. [BERN87]
    Bernstein, P., Hadzilacos, V., and Goodman, N., “Concurrency Control and Recovery in Database Systems”, Addison Wesley Publishing Co., 1987.Google Scholar
  3. [DAVI85]
    Davidson, S. B., Garcia-Molina, H., and Skeen, D., “Consistency in Partitioned Networks”, ACM Computing Surveys 17(3), September 1985.Google Scholar
  4. [EAGE81]
    Eager, D.L., “Robust Concurrency Control in Distributed Databases”, Technical Report CSRG #135, Computer Systems Research Group, University of Toronto, October 1981.Google Scholar
  5. [EAGE83]
    Eager, D.L., and Sevcik, K.C., “Achieving Robustness in Distributed Database Systems”, ACM Trans. Database Syst. 8(3):354–381, September 1983.Google Scholar
  6. [ELAB85]
    El Abbadi, A., Skeen, D., and Cristian, F., “An Efficient, Fault-Tolerant Protocol for Replicated Data Management”, Proc. 4th ACM SIGACT-SIGMOD Symp, on Principles of Database Systems, pages 215–228. Portland, Oregon, March 1985.Google Scholar
  7. [GARC84]
    Garcia-Molina, H., and Barbara, D., “Optimizing the Reliability Provided by Voting Mechanisms”, Proc. 4th International Conference on Distributed Computing Systems, May 1984.Google Scholar
  8. [GARC85]
    Garcia-Molina, H., and Barbara, D., “How to Assign Votes in a Distributed System”, Journal of ACM, Vol. 32, No. 4, October 1985.Google Scholar
  9. [GIFF79]
    Gifford, D.K., “Weighted Voting for Replicated Data”, Proc. 7th ACM SIGOPS Symp. on operating Systems Principles, pages 150–159. Pacific Grove, CA, December 1979.Google Scholar
  10. [GRAY78]
    Gray, J., “Notes on Data Base Operating Systems,” in Operating Systems: An Advanced Course, Springer-Verlag, 1978, pp393–481.Google Scholar
  11. [HERL87]
    Herlihy, M., “Dynamic Quorum Adjustment for Partitional Data”, ACM TODS, Vol 12, No 2, June 1987.Google Scholar
  12. [JAJO87]
    Jajodia, S. and Mutchler, D., “Dynamic Voting”, Proc. 1987 ACM SIGMOD, San Francisco, CA, May 1987.Google Scholar
  13. [REED78]
    Reed, D., “Naming and Synchronization in a Decentralized Computer System,” Ph.D. Thesis, Department of Electrical Engineering and Computer Science, M.I.T., 1978.Google Scholar
  14. [SCHR86]
    Schrage, L., “LINDO”, Scientific Press, 1986.Google Scholar
  15. [THOM79]
    Thomas, R. H., “A Majority Consensus Approach to Concurrency Control,” TODS, June 1979.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • Akhil Kumar
    • 1
  • Arie Segev
    • 1
  1. 1.School of Business Administration and Lawrence Berkeley Lab's Computer Science Research Dept.University of CaliforniaBerkeley

Personalised recommendations