Research on Consensus Efficiency Based on Practical Byzantine Fault Tolerance

Byzantine Fault Tolerance (BFT) will be increasingly important to provide reliability in distributed systems and various Byzantine fault-tolerant algorithms have been proposed to improve efficiency. However, the practicality of BFT is still limited by its cost of consensus. Practical Byzantine Fault Tolerance (PBFT) is one of the best algorithms used in reality. Confronted with a distributed system with numerous nodes, PBFT is not satisfying. In order to improve the consensus efficiency, a new algorithm named Group-Hierarchy (GH) based on PBFT is proposed in this article. GH divides all replicas in a distributed system into several groups and every group has a primary. Replicas in a group reach an agreement respectively, which is called Local Consensus. And primaries of all the groups also reach a consensus based on the Local Consensus, which is called Global Consensus. After the Local Consensus and Global Consensus, replicas could reply to client. GH correlates the message complexity to the number of replicas and the number of groups in a distributed system in theory. The message complexity of GH is lower than PBFT in theory and the performed tests show that GH can execute a request even several times faster than PBFT.

[1]  C. Sunitha,et al.  A Study on Byzantine Fault Tolerance Methods in Distributed Networks , 2016 .

[2]  John Lane,et al.  Byzantine replication under attack , 2008, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN).

[3]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[4]  Liuba Shrira,et al.  HQ replication: a hybrid quorum protocol for byzantine fault tolerance , 2006, OSDI '06.

[5]  Leslie Lamport,et al.  The part-time parliament , 1998, TOCS.

[6]  Jie Fan,et al.  Research on the Technologies of Byzantine System: Research on the Technologies of Byzantine System , 2014 .

[7]  John Lane,et al.  Customizable Fault Tolerance forWide-Area Replication , 2007, 2007 26th IEEE International Symposium on Reliable Distributed Systems (SRDS 2007).

[8]  Hagit Attiya,et al.  Distributed Computing: Fundamentals, Simulations and Advanced Topics , 1998 .

[9]  Michael K. Reiter,et al.  Fault-scalable Byzantine fault-tolerant services , 2005, SOSP '05.

[10]  Ramakrishna Kotla,et al.  Zyzzyva: speculative byzantine fault tolerance , 2007, TOCS.

[11]  Sébastien Tixeuil,et al.  Limiting Byzantine Influence in Multihop Asynchronous Networks , 2012, 2012 IEEE 32nd International Conference on Distributed Computing Systems.

[12]  Miguel Oom Temudo de Castro,et al.  Practical Byzantine fault tolerance , 1999, OSDI '99.

[13]  Andrew S. Tanenbaum,et al.  Distributed systems: Principles and Paradigms , 2001 .

[14]  Henry M. Levy,et al.  A comparison of message passing and shared memory architectures for data parallel programs , 1994, ISCA '94.

[15]  Fred B. Schneider,et al.  Implementing fault-tolerant services using the state machine approach: a tutorial , 1990, CSUR.

[16]  David Mazières,et al.  Beyond One-Third Faulty Replicas in Byzantine Fault Tolerant Systems , 2007, NSDI.

[17]  LamportLeslie.  Time, clocks, and the ordering of events in a distributed system , 1978 .

[18]  Sébastien Tixeuil,et al.  On Byzantine Broadcast in Loosely Connected Networks , 2012, DISC.

[19]  Jason Flinn,et al.  Tolerating Latency in Replicated State Machines Through Client Speculation , 2009, NSDI.

[20]  Jonathan Kirsch,et al.  Scaling Byzantine Fault-Tolerant Replication toWide Area Networks , 2006, International Conference on Dependable Systems and Networks (DSN'06).

[21]  Mema Roussopoulos,et al.  On the Practicality of Practical Byzantine Fault Tolerance , 2011, Middleware.