Worldwide Consensus

  • Francisco Maia
  • Miguel Matos
  • José Pereira
  • Rui Oliveira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6723)


Consensus is an abstraction of a variety of important challenges in dependable distributed systems. Thus a large body of theoretical knowledge is focused on modeling and solving consensus within different system assumptions. However, moving from theory to practice imposes compromises and design decisions that may impact the elegance, trade-offs and correctness of theoretical appealing consensus protocols.

In this paper we present the implementation and detailed analysis, in a real environment with a large number of nodes, of mutable consensus, a theoretical appealing protocol able to offer a wide range of trade-offs (called mutations) between decision latency and message complexity. The analysis sheds light on the fundamental behavior of the mutations, and leads to the identification of problems related to the real environment. Such problems are addressed without ever affecting the correctness of the theoretical proposal.


Failure Detector Consensus Problem Message Complexity Consensus Protocol Message Loss 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aguilera, M., Chen, W., Toueg, S.: On quiescent reliable communication. SIAM Journal on Computing 29 (2000)CrossRefMATHGoogle Scholar
  2. 2.
    Bavier, A., Bowman, M., Chun, B., Culler, D., Karlin, S., Muir, S., Peterson, L., Roscoe, T., Spalink, T., Wawrzoniak, M.: Operating system support for planetary-scale network services. In: Symposium on Networked Systems Design and Implementation, p. 19 (2004)Google Scholar
  3. 3.
    Chandra, T., Griesemer, R., Redstone, J.: Paxos made live: an engineering perspective. In: Symposium on Principles of Distributed Computing, pp. 398–407 (2007)Google Scholar
  4. 4.
    Chandra, T., Toueg, S.: Unreliable failure detectors for reliable distributed systems. Journal of the ACM 43, 225–267 (1996)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Eugster, P., Guerraoui, R., Kermarrec, A.-M., Massoulié, L.: From Epidemics to Distributed Computing. IEEE Computer 37(5), 60–67 (2004)CrossRefGoogle Scholar
  6. 6.
    Fischer, M., Lynch, N., Paterson, M.: Impossibility of distributed consensus with one faulty process. J. ACM 32(2), 374–382 (1985)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Guerraoui, R., Oliveira, R., Schiper, A.: Stubborn Communication Channels. Technical report, EPFL (1998)Google Scholar
  8. 8.
    Leonini, L., Riviere, E., Felber, P.: SPLAY: Distributed Systems Evaluation Made Simple. In: Symposium on Networked Systems Design and Implementation, pp. 185–198 (2009)Google Scholar
  9. 9.
    Matos, M., Pereira, J., Oliveira, R.: Self Tuning with Self Confidence. In: ”Fast Abstract”, International Conference on Dependable Systems and Networks (2008)Google Scholar
  10. 10.
    Pereira, J., Oliveira, R.: The mutable consensus protocol. In: Symposium on Reliable Distributed Systems, pp. 218–227 (2004)Google Scholar
  11. 11.
    Schiper, A.: Early consensus in an asynchronous system with a weak failure detector. Distributed Computing 10, 149–157 (1997)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Francisco Maia
    • 1
  • Miguel Matos
    • 1
  • José Pereira
    • 1
  • Rui Oliveira
    • 1
  1. 1.High-Assurance Software LaboratoryUniversity of MinhoBragaPortugal

Personalised recommendations