Document Open Access Logo

State Machine Replication Is More Expensive Than Consensus

Authors Karolos Antoniadis, Rachid Guerraoui, Dahlia Malkhi, Dragos-Adrian Seredinschi



PDF
Thumbnail PDF

File

LIPIcs.DISC.2018.7.pdf
  • Filesize: 1.12 MB
  • 18 pages

Document Identifiers

Author Details

Karolos Antoniadis
  • EPFL, Lausanne, Switzerland
Rachid Guerraoui
  • EPFL, Lausanne, Switzerland
Dahlia Malkhi
  • VMware Research, Palo Alto, USA
Dragos-Adrian Seredinschi
  • EPFL, Lausanne, Switzerland

Cite AsGet BibTex

Karolos Antoniadis, Rachid Guerraoui, Dahlia Malkhi, and Dragos-Adrian Seredinschi. State Machine Replication Is More Expensive Than Consensus. In 32nd International Symposium on Distributed Computing (DISC 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 121, pp. 7:1-7:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.DISC.2018.7

Abstract

Consensus and State Machine Replication (SMR) are generally considered to be equivalent problems. In certain system models, indeed, the two problems are computationally equivalent: any solution to the former problem leads to a solution to the latter, and vice versa. In this paper, we study the relation between consensus and SMR from a complexity perspective. We find that, surprisingly, completing an SMR command can be more expensive than solving a consensus instance. Specifically, given a synchronous system model where every instance of consensus always terminates in constant time, completing an SMR command does not necessarily terminate in constant time. This result naturally extends to partially synchronous models. Besides theoretical interest, our result also corresponds to practical phenomena we identify empirically. We experiment with two well-known SMR implementations (Multi-Paxos and Raft) and show that, indeed, SMR is more expensive than consensus in practice. One important implication of our result is that - even under synchrony conditions - no SMR algorithm can ensure bounded response times.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Distributed algorithms
Keywords
  • Consensus
  • State machine replication
  • Synchronous model

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Amazon EC2. http://aws.amazon.com/ec2/. [Online; accessed 9-May-2018].
  2. etcd. https://github.com/coreos/etcd. [Online; accessed 9-May-2018].
  3. LibPaxos3. https://bitbucket.org/sciascid/libpaxos. [Online; accessed 9-May-2018].
  4. Karolos Antoniadis, Rachid Guerraoui, Dahlia Malkhi, and Dragos-Adrian Seredinschi. State Machine Replication is More Expensive Than Consensus. Technical Report 256238, EPFL, 2018. URL: https://infoscience.epfl.ch/record/256238.
  5. B. Arun, S. Peluso, R. Palmieri, G. Losa, and B. Ravindran. Speeding up Consensus by Chasing Fast Decisions. In DSN, 2017. Google Scholar
  6. Peter Bailis and Kyle Kingsbury. The network is reliable. ACM Queue, 12(7):20, 2014. Google Scholar
  7. Alysson Bessani, Marcel Santos, João Felix, Nuno Neves, and Miguel Correia. On the efficiency of durable state machine replication. In ATC, 2013. Google Scholar
  8. Alysson Bessani, João Sousa, and Eduardo EP Alchieri. State machine replication for the masses with bft-smart. In DSN, 2014. Google Scholar
  9. Martin Biely, Peter Robinson, Ulrich Schmid, Manfred Schwarz, and Kyrill Winkler. Gracefully degrading consensus and k-set agreement in directed dynamic networks. Theoretical Computer Science, 726:41-77, 2018. Google Scholar
  10. Christian Cachin, Rachid Guerraoui, and Luìs Rodrigues. Introduction to Reliable and Secure Distributed Programming. Springer, 2011. Google Scholar
  11. Lásaro Jonas Camargos, Rodrigo Malta Schmidt, and Fernando Pedone. Multicoordinated agreement protocols for higher availability. In Network Computing and Applications, 2008. Google Scholar
  12. Daniel Cason, Parisa J Marandi, Luiz E Buzato, and Fernando Pedone. Chasing the tail of atomic broadcast protocols. In SRDS, 2015. Google Scholar
  13. Tushar D. Chandra, Robert Griesemer, and Joshua Redstone. Paxos made live: An engineering perspective. In PODC, 2007. Google Scholar
  14. Tushar D. Chandra and Sam Toueg. Unreliable failure detectors for reliable distributed systems. Journal of the ACM (JACM), 43(2):225-267, 1996. Google Scholar
  15. Bernadette Charron-Bost and André Schiper. The heard-of model: computing in distributed systems with benign faults. Distributed Computing, 22(1):49-71, Apr 2009. Google Scholar
  16. James C. Corbett, Jeffrey Dean, Michael Epstein, Andrew Fikes, Christopher Frost, J. J. Furman, Sanjay Ghemawat, Andrey Gubarev, Christopher Heiser, Peter Hochschild, Wilson Hsieh, Sebastian Kanthak, Eugene Kogan, Hongyi Li, Alexander Lloyd, Sergey Melnik, David Mwaura, David Nagle, Sean Quinlan, Rajesh Rao, Lindsay Rolig, Yasushi Saito, Michal Szymaniak, Christopher Taylor, Ruth Wang, and Dale Woodford. Spanner: Google’s globally distributed database. ACM TOCS, 31(3):8:1-8:22, 2013. Google Scholar
  17. Jeffrey Dean and Luiz André Barroso. The tail at scale. Communications of the ACM, 56(2):74-80, 2013. Google Scholar
  18. Tzilla Elrad and Nissim Francez. Decomposition of distributed programs into communication-closed layers. Science of Computer Programming, 2(3):155-173, 1982. Google Scholar
  19. Michael J Fischer, Nancy A Lynch, and Michael S Paterson. Impossibility of distributed consensus with one faulty process. Journal of the ACM (JACM), 32(2):374-382, 1985. Google Scholar
  20. Eli Gafni. Round-by-round fault detectors (extended abstract): Unifying synchrony and asynchrony. In PODC, 1998. Google Scholar
  21. Álvaro García-Pérez, Alexey Gotsman, Yuri Meshman, and Ilya Sergey. Paxos consensus, deconstructed and abstracted (extended version). CoRR, abs/1802.05969, 2018. Google Scholar
  22. Chryssis Georgiou, Seth Gilbert, Rachid Guerraoui, and Dariusz R. Kowalski. On the complexity of asynchronous gossip. In PODC, 2008. Google Scholar
  23. Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The Google File System. In SOSP, 2003. Google Scholar
  24. Rachid Guerraoui, Matej Pavlovic, and Dragos-Adrian Seredinschi. Incremental consistency guarantees for replicated objects. In OSDI, 2016. Google Scholar
  25. Heidi Howard and Jon Crowcroft. Coracle: Evaluating Consensus at the Internet Edge. In SIGCOMM, 2015. Google Scholar
  26. Heidi Howard, Dahlia Malkhi, and Alexander Spiegelman. Flexible Paxos: Quorum Intersection Revisited. In OPODIS, 2016. Google Scholar
  27. Patrick Hunt, Mahadev Konar, Flavio Paiva Junqueira, and Benjamin Reed. Zookeeper: Wait-free coordination for internet-scale systems. In USENIX ATC, 2010. Google Scholar
  28. Idit Keidar and Sergio Rajsbaum. On the cost of fault-tolerant consensus when there are no faults: Preliminary version. SIGACT News, 32(2):45-63, 2001. Google Scholar
  29. M. Kleppmann. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. O'Reilly Media, 2017. Google Scholar
  30. Tim Kraska, Gene Pang, Michael J Franklin, Samuel Madden, and Alan Fekete. MDCC: Multi-data center consistency. In EuroSys, 2013. Google Scholar
  31. Leslie Lamport. Time, Clocks, and the Ordering of Events in a Distributed System. Commun. ACM, 21(7):558-565, 1978. Google Scholar
  32. Leslie Lamport. The part-time parliament. ACM TOCS, 16(2):133-169, 1998. Google Scholar
  33. Leslie Lamport. Paxos made simple. ACM Sigact News, 32(4):18-25, 2001. Google Scholar
  34. Leslie Lamport. Lower bounds for asynchronous consensus. In Future Directions in Distributed Computing, pages 22-23. Springer, 2003. Google Scholar
  35. Leslie Lamport. Fast paxos. Distributed Computing, 19(2):79-103, 2006. Google Scholar
  36. Leslie Lamport, Dahlia Malkhi, and Lidong Zhou. Stoppable Paxos. TechReport, Microsoft Research, 2008. Google Scholar
  37. Leslie Lamport and Mike Massa. Cheap paxos. In DSN, 2004. Google Scholar
  38. Barbara Liskov, Sanjay Ghemawat, Robert Gruber, Paul Johnson, Liuba Shrira, and Michael Williams. Replication in the Harp File System. In SOSP, 1991. Google Scholar
  39. John MacCormick, Nick Murphy, Marc Najork, Chandu Thekkath, and Lidong Zhou. Boxwood: Abstractions as the foundation for storage infrastructure. In OSDI, 2004. Google Scholar
  40. Iulian Moraru, David G. Andersen, and Michael Kaminsky. There is More Consensus in Egalitarian Parliaments. In SOSP, 2013. Google Scholar
  41. Yoram Moses and Sergio Rajsbaum. A layered analysis of consensus. SIAM Journal on Computing, 31(4):989-1021, 2002. Google Scholar
  42. A. Mostefaoui and M. Raynal. Low cost consensus-based atomic broadcast. In Proceedings. 2000 Pacific Rim International Symposium on Dependable Computing, 2000. Google Scholar
  43. Diego Ongaro and John Ousterhout. In search of an understandable consensus algorithm. In ATC, 2014. Google Scholar
  44. Nicola Santoro and Peter Widmayer. Time is not a healer. In STACS, 1989. Google Scholar
  45. Nicola Santoro and Peter Widmayer. Agreement in synchronous networks with ubiquitous faults. Theoretical Computer Science, 384(2):232-249, 2007. Google Scholar
  46. Nuno Santos and André Schiper. Tuning paxos for high-throughput with batching and pipelining. In International Conference on Distributed Computing and Networking, pages 153-167. Springer, 2012. Google Scholar
  47. Ulrich Schmid, Bettina Weiss, and Idit Keidar. Impossibility results and lower bounds for consensus under link failures. SIAM Journal on Computing, 38(5):1912-1951, 2009. Google Scholar
  48. Ulrich Schmid, Bettina Weiss, and John Rushby. Formally verified byzantine agreement in presence of link faults. In ICDCS, 2002. Google Scholar
  49. Fred B. Schneider. Implementing fault-tolerant services using the state machine approach: A tutorial. ACM Comput. Surv., 22(4):299-319, 1990. Google Scholar
  50. Emil Sit, Andreas Haeberlen, Frank Dabek, Byung-Gon Chun, Hakim Weatherspoon, Robert Morris, M Frans Kaashoek, and John Kubiatowicz. Proactive Replication for Data Durability. In IPTPS, 2006. Google Scholar
  51. Robert H Thomas. A majority consensus approach to concurrency control for multiple copy databases. ACM TODS, 4(2):180-209, 1979. Google Scholar
  52. Gustavo M. D. Vieira, Islene C. Garcia, and Luiz Eduardo Buzato. Seamless paxos coordinators. CoRR, abs/1710.07845, 2017. URL: http://arxiv.org/abs/1710.07845.
  53. Matt Welsh, David Culler, and Eric Brewer. Seda: An architecture for well-conditioned, scalable internet services. In SOSP, 2001. Google Scholar
  54. Benjamin Wester, James A Cowling, Edmund B Nightingale, Peter M Chen, Jason Flinn, and Barbara Liskov. Tolerating latency in replicated state machines through client speculation. In NSDI, 2009. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail