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The Weakest Failure Detector for Eventual Consistency

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Published:21 July 2015Publication History

ABSTRACT

In its classical form, a consistent replicated service requires all replicas to witness the same evolution of the service state. Assuming a message-passing environment with a majority of correct processes, the necessary and sufficient information about failures for implementing a general state machine replication scheme ensuring consistency is captured by the Ω failure detector.

This paper shows that in such a message-passing environment, Ω is also the weakest failure detector to implement an eventually consistent replicated service, where replicas are expected to agree on the evolution of the service state only after some (a priori unknown) time.

In fact, we show that Ω is the weakest to implement eventual consistency in any message-passing environment, i.e., under any assumption on when and where failures might occur. Ensuring (strong) consistency in any environment requires, in addition to Ω, the quorum failure detector ∑. Our paper thus captures, for the first time, an exact computational difference between building a replicated state machine that ensures consistency and one that only ensures eventual consistency.

References

  1. E. A. Brewer. Towards robust distributed systems (abstract). In Proceedings of the Nineteenth Annual ACM Symposium on Principles of Distributed Computing, PODC '00, pages 7--, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. T. D. Chandra, V. Hadzilacos, and S. Toueg. The weakest failure detector for solving consensus. J. ACM, 43(4):685--722, July 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. T. D. Chandra and S. Toueg. Unreliable failure detectors for reliable distributed systems. J. ACM, 43(2):225--267, Mar. 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber. Bigtable: A distributed storage system for structured data. ACM Trans. Comput. Syst., 26(2):4:1--4:26, June 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. B. Charron-Bost and G. Tel. Approximation d'une borne inférieure répartie. Technical Report LIX/RR/94/06, Laboratoire d'Informatique LIX, École Polytechnique, Sept. 1994.Google ScholarGoogle Scholar
  6. B. F. Cooper, R. Ramakrishnan, U. Srivastava, A. Silberstein, P. Bohannon, H.-A. Jacobsen, N. Puz, D. Weaver, and R. Yerneni. Pnuts: Yahoo!'s hosted data serving platform. Proc. VLDB Endow., 1(2):1277--1288, Aug. 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S. Sivasubramanian, P. Vosshall, and W. Vogels. Dynamo: Amazon's highly available key-value store. In Proceedings of Twenty-first ACM SIGOPS Symposium on Operating Systems Principles, SOSP '07, pages 205--220, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. C. Delporte-Gallet, H. Fauconnier, and R. Guerraoui. Tight failure detection bounds on atomic object implementations. J. ACM, 57(4), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. Dolev, R. I. Kat, and E. M. Schiller. When consensus meets self-stabilization. Journal of Computer and System Sciences, 76(8):884 -- 900, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Dubois, R. Guerraoui, P. Kuznetsov, F. Petit, and P. Sens. The weakest failure detector for eventual consistency. CoRR, abs/X.X, 2015.Google ScholarGoogle Scholar
  11. A. Fekete, D. Gupta, V. Luchangco, N. Lynch, and A. Shvartsman. Eventually-serializable data services. In Proceedings of the Fifteenth Annual ACM Symposium on Principles of Distributed Computing, PODC '96, pages 300--309, New York, NY, USA, 1996. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. J. Fischer, N. A. Lynch, and M. S. Paterson. Impossibility of distributed consensus with one faulty process. J. ACM, 32(2):374--382, Apr. 1985. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. F. C. Freiling, R. Guerraoui, and P. Kuznetsov. The failure detector abstraction. ACM Comput. Surv., 43(2):9:1--9:40, Feb. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. S. Gilbert and N. Lynch. Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News, 33(2):51--59, June 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. R. Guerraoui, V. Hadzilacos, P. Kuznetsov, and S. Toueg. The weakest failure detectors to solve quittable consensus and nonblocking atomic commit. SIAM J. Comput., 41(6):1343--1379, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. R. Guerraoui and E. Ruppert. A paradox of eventual linearizability in shared memory. In Proceedings of the 2014 ACM Symposium on Principles of Distributed Computing, PODC '14, pages 40--49, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. V. Hadzilacos and S. Toueg. A modular approach to fault-tolerant broadcasts and related problems. Technical Report TR 94--1425, Department of Computer Science, Cornell University, May 1994. Google ScholarGoogle Scholar
  18. P. Jayanti and S. Toueg. Every problem has a weakest failure detector. In PODC, pages 75--84, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. F. Kuhn, Y. Moses, and R. Oshman. Coordinated consensus in dynamic networks. In Proceedings of the 30th Annual ACM Symposium on Principles of Distributed Computing (PODC), pages 1--10. ACM, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. A. Lakshman and P. Malik. Cassandra: A decentralized structured storage system. SIGOPS Oper. Syst. Rev., 44(2):35--40, Apr. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. L. Lamport. Proving the correctness of multiprocessor programs. Transactions on software engineering, 3(2):125--143, Mar. 1977. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. L. Lamport. The Part-Time parliament. ACM Transactions on Computer Systems, 16(2):133--169, May 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. L. Lamport. Lower bounds for asynchronous consensus. Distributed Computing, 19(2):104--125, 2006.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A. Mostefaoui, M. Raynal, and F. Tronel. From binary consensus to multivalued consensus in asynchronous message-passing systems. Inf. Process. Lett., 73(5--6):207--212, Mar. 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Y. Saito and M. Shapiro. Optimistic replication. ACM Comput. Surv., 37(1):42--81, Mar. 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. F. B. Schneider. Implementing fault-tolerant services using the state machine approach: A tutorial. ACM Computing Surveys, 22(4):299--319, Dec. 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. Serafini, D. Dobre, M. Majuntke, P. Bokor, and N. Suri. Eventually linearizable shared objects. In A. W. Richa and R. Guerraoui, editors, Proceedings of the 29th Annual ACM Symposium on Principles of Distributed Computing, pages 95--104. ACM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. A. Singh, P. Fonseca, P. Kuznetsov, R. Rodrigues, and P. Maniatis. Zeno: Eventually consistent byzantine-fault tolerance. In Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, NSDI'09, pages 169--184, Berkeley, CA, USA, 2009. USENIX Association. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. W. Vogels. Eventually consistent. Commun. ACM, 52(1):40--44, Jan. 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Conferences
            PODC '15: Proceedings of the 2015 ACM Symposium on Principles of Distributed Computing
            July 2015
            508 pages
            ISBN:9781450336178
            DOI:10.1145/2767386

            Copyright © 2015 ACM

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            Publication History

            • Published: 21 July 2015

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            PODC '15 Paper Acceptance Rate45of191submissions,24%Overall Acceptance Rate740of2,477submissions,30%

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