Scalable update propagation in epidemic replicated databases
Many distributed databases use an epidemic approach to manage replicated data. In this approach, user operations are executed on a single replica. Asynchronously, a separate activity performs periodic pair-wise comparison of data item copies to detect and bring up to date obsolete copies. The overhead due to comparison of data copies grows linearly with the number of data items in the database, which limits the scalability of the system.
We propose an epidemic protocol whose overhead is linear in the number of data items being copied during update propagation. Since this number is typically much smaller than the total number of data items in the database, our protocol promises significant reduction of overhead.
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- 1.D. Agrawal and A. Malpani. Efficient dissemination of information in computer networks. the Computer Journal, 6(34), pp. 534–541, 1991.Google Scholar
- 2.P. A. Bernstein, V. Hadzilacos, and N. Goodman. Concurrency Control and Recovery in Database Systems. Addison-Wesley, Reading, Mass., 1987.Google Scholar
- 3.K. Birman, A. Schiper, and P. Stephenson. Lightweight causal and atomic group multicast. ACM Trans. on Comp. Sys. Vol. 9, No. 3, pp. 272–314, August 1991.Google Scholar
- 4.A. Deniers, D. Greene, C. Hauser, W. Irish, J. Larson, S. Shenker, H. Sturgis, D. Swinehart, and D. Terry. Epidemic algorithms for replicated database maintenance. In Proc. of the 6th Symp. on Principles of Distr. Computing, pp. 1–12, 1987.Google Scholar
- 5.R. G. Guy, J. S. Heidemann, W. Mak, T. W. Page, G. J. Popek, G. J. Rothmeier. Implementation of the Ficus replicated file system. In Proc. of Usenix Summer Conf., pp. 63–71, 1990.Google Scholar
- 6.C. Fidge. Timestamps in message-passing systems that preserve the partial ordering. In Proc. of the 11th Australian Computer Science Conf., pp. 56–66, 1988.Google Scholar
- 7.A. Heddaya, M. Hsu, and W. Weihl. Two phase gossip: managing distributed event histories. Information Sciences, 49, pp. 35–57, 1989.Google Scholar
- 8.L. Kawell Jr., S. Beckhardt, T. Halvorsen, R. Ozzie, and I. Greif. Replicated document management in a group communication system. Presented at the 2d Conf. on Computer-Supported Cooperative Work. September 1988.Google Scholar
- 9.R. Ladin, B. Liskov, L. Shrira, and S. Ghemawat. Providing high availability using lazy replication. ACM Trans. on Computer Systems, 4(10), pp. 360–391, November 1992.Google Scholar
- 10.Oracle 7 Distributed Database Technology and Symmetric Replication. Oracle White Paper, April 1995.Google Scholar
- 11.D. S. Parker, G. J. Popek, G. Rudisin, A. Stoughton, B. J. Walker, E. Walton, J. M. Chow, D. Edwards, S. Kiser, and C. Kline. Detection of mutual inconsistency in distributed systems. IEEE Trans. on Software Eng. 9(3), pp. 240–246, May 1983.Google Scholar
- 12.G. Popek, B. Walker, J. Chow, D. Edwards, C. Kline, G. Rudisin, and G. Thiel. LOCUS: A network transparent, high reliability distributed system. In Proc. 8th Symp. on Operating Systems Principles, pp. 169–177, 1981.Google Scholar
- 13.M. Rabinovich, N. Gehani, and A. Kononov. Scalable update propagation in epidemic replicated databases. AT&T Bell Labs Technical Memorandum 112580-951213-11TM, December 1995.Google Scholar
- 14.D. Terry, A. Demers, K. Peterson, M. Spreitzer, M. Theimer, and B. Welch. Session guarantees for weakly consistent replicated data. In Proc. of the Int. Conf. on Parallel and Distributed Information Systems, 1994.Google Scholar
- 15.G. T. Wuu and A. J. Bernstein. Efficient solution to the replicated log and dictionary problems. In Proc. of the 3d ACM Symp. on Principles of Distr. Computing, pp. 233–242, 1984.Google Scholar