DOCSLIB.ORG

Comparative Study of Byzantine Fault Tolerant Consensus Algorithms on Permissioned Blockchains April 2020

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Comparative Study of Byzantine Fault Tolerant Consensus Algorithms on Permissioned Blockchains April 2020 Comparative Study of Byzantine Fault Tolerant Consensus Algorithms on Permissioned Blockchains April 2020 Isitan Gorkey Chakir El Moussaoui Vincent Wijdeveld EEMCS EEMCS EEMCS TU Delft TU Delft TU Delft Delft, Netherlands Delft, Netherlands Delft, Netherlands [email protected] [email protected] [email protected] Supervised by Supervised by Erik Sennema Zekeriya Erkin Miray Aysen EEMCS EEMCS EEMCS TU Delft TU Delft TU Delft Delft, Netherlands Delft, Netherlands Delft, Netherlands [email protected] [email protected] [email protected] Abstract—Blockchain is a technology that is in use increasingly. process of answering the research question consists of first Although owing its common use to the cryptocurrencies there giving an overview of the blockchain. Then the consensus is more to the blockchain technology than just the monetary algorithms used in blockchain technologies will be discussed use. One of these uses is by smaller groups of participants under the control of a central authority, for instance at a followed by the trust models between nodes, the incentive company. Such private blockchains use permissioned blockchain mechanisms, and scalability analysis. Lastly, the comparison consensus algorithms as the participants need the permission of permissioned consensus algorithms will be discussed and a of the authority to be able to join the system. This paper will conclusion will be drawn to the paper. give an overview of the blockchain technology, investigate permis- sioned and permissionless blockchain, and focus on permissioned blockchains to analyze it in terms of, e.g. trust models between II. AN OVERVIEW OF BLOCKCHAIN the nodes, incentives, number of nodes & parties involved, and Blockchain [4] is a sequence of blocks, where each block scalability regarding the number of transactions. Index Terms—Blockchain, Byzantine Fault Tolerance, Consensus keeps a list of transactions. Each block consists of a header Algorithms, Cryptocurrency, Permissioned and a body. The header contains the following information about the block which can be seen in Figure 1: I. INTRODUCTION • Block version: indicates the set of block validation rules Blockchain is a technology that is being used in increasingly to follow. more platforms [1]. The idea of blockchain was first • Merkle tree root hash: the hash value of the Merkle tree introduced by Haber and Stornetta in 1990 [2], the first root, which is recursively derived from the hash value of conceptualization of it did not happen until 2008 when the transactions in the block, as can be seen in Figure 1. Nakamoto applied blockchain as the underlying technology of • Timestamp: the time passed in seconds from January 1, Bitcoin [3]. Bitcoin made it possible to make safe monetary 1970 until the creation of the block (universal time). transactions without the need for a central authority such as a • nBits: the target threshold of a valid hash. The gener- bank, which remarked the strength of blockchain technology. ated hash needs nBits or a lower amount of 0’s at the In the following years, blockchain technology has been used beginning of the generated hash. in more fields from payment processing to online voting. • Nonce: a 4-byte field, which usually starts with 0 and increases for every hash calculation. The main research question of this paper is: How the con- • Previous block hash: a 256-bit hash value that points to sensus algorithms that are used in permissioned blockchains the previous block. compare against each other. The subquestions investigated The hash of the previous block is contained in the block to answer this question are: How do consensus algorithms header, a block has only one previous block. The first block compare against each other in the means of incentives, number of a blockchain is called the genesis block. The genesis block of nodes & parties involved, scalability regarding the number has no previous block. of transactions, and trust models between the nodes. The 1 Block N Block N + 1 Block Header Block Header Block Time Block Time nBits Nonce nBits Nonce Version Stamp Version Stamp Previous Block Merkle Tree Previous Block Merkle Tree Hash Root Hash Hash Root Hash Body Body Hash1 Hash2 Hash1 Hash2 Transaction Transaction Counter Counter Tx1 Tx2 Tx3 Tx4 Tx1 Tx2 Tx3 Tx4 Fig. 1. An example of a block structure in a blockchain The body consists of a transaction counter and a list of trans- Permissionless blockchains do, however, suffer from several actions. The maximum number of transactions that a block can drawbacks due to their fully replicated and open nature [5]: contain depends on the maximum block size and the size of 1) They have low performance in terms of the ability to the transactions that is set for the specific implementation of process a high throughput of transaction requests. For blockchain. Bitcoin, for example, has a maximum block size example, Bitcoin is currently able to process only about of 1 MB and a block is mined every 10 minutes on average. 7 transactions per second (TPS). This results in a maximum processing time of 7 transactions 2) Data stored in permissionless blockchains is publicly per second. One solution to increase the transaction throughput accessible as it is fully replicated among all peers. is to make use of larger blocks. This, however, means that if Without implementing additional measures on top of the all blocks are filled the blockchain will grow twice as fast as original protocol, permissionless blockchains suffer from before. This can make it significantly more costly to run a full confidentiality issues. bitcoin miner node in the future. Doubling the block size can 3) Due to forking, permissionless blockchains with the thus result in a higher barrier of entry for node administrators consensus protocols Proof of Work, Proof of Stake and and lower the number of nodes ran on the network. This could Proof of Authority never reach consensus finality, that cause centralisation which is undesirable as it is contradictory is, they have forks resulting in more waiting time for to the decentralized nature of the system. a block to be deemed valid as they go by one of the chains of the fork [6]. This chain could be chosen A. Permissionless Blockchain by different parameters based on implementation, such Permissionless blockchains [5] are open for any entity to as the length of the chain. When forking is possible, participate in by running a local instance of the protocol transactions committed in dropped chains of the fork using their digital signature (public key). There is no need might be revoked. for participants to publicize their real identities. The first To avoid these drawbacks, permissioned blockchains were permissionless blockchain was Bitcoin [3]. introduced. There are some advantages to the fully replicated immutable B. Permissioned Blockchain data store of the permissionless blockchains and their open Permissioned blockchains have evolved as an alternative to nature, which are [5]: permissionless blockchains. In [7] Vukolic´ stated that ”this 1) No need to have full trust in any single entity in the happened in order to address the need for running blockchain network or a third party. technology among a set of known and identifiable participants 2) Each entity in the network is identified by its digital that have to be explicitly admitted to the blockchain network”. signature. There is no need for real identities. This makes the permissionless blockchains pseudonymous. The permissioned blockchain [7] concept is best used in 3) Blockchains are fully replicated immutable data stores business applications of the blockchain technology. Even that aim to ensure integrity and non-repudiation. though the nodes do not necessarily trust each other or any 2 third parties, they are still required to identify themselves, confirmable by the other nodes and the proof should be which is usually the case within business circles. The notion infeasible to forge. of smart contracts [5] was introduced by Ethereum [8] and are 1) Proof of Work used in applications that require trustworthy and decentralized Proof of Work (PoW) was first introduced by Satoshi execution of business logic involving multiple entities. Nakamoto in the implementation of the Bitcoin network [3]. The reason permissioned blockchains provide enhancements Within a decentralized network [4], there needs to be some over their permissionless counterparts is that they facilitate node in the network that records the transactions, creates enterprise-grade use cases [5], such as: a block, and sends it to all other nodes in the network for 1) The participation in the consensus protocol is limited to them to attach it to their own blockchain. The created block a specific group of nodes that require explicit system will then be recorded by all other nodes in the network. This configuration in terms of permission from the authority, so called block creation is rewarded with a block reward software, and hardware. This results in permissioned which will be discussed in section V. The easiest and the blockchains being able to use Byzantine Fault Tolerant fairest way to select such a node is random selection. But (BFT) protocols, which generally have better throughput randomly selecting such a node is vulnerable to attacks. To and transaction latency. [9]. avoid this, a node must do a lot of work in the form of 2) Falazi et al. [5] noted that ”Permissioned blockchains are computer calculations to be selected. The idea behind this is generally better in terms of confidentiality since sensitive that a node which has done a lot of work would not be likely transactions can be isolated from public access.” to attack the network. 3) For permissioned blockchains forks and non-final deci- sions could be avoided. For example, Byzantine consen- In Bitcoin, each node of the network is calculating a hash sus algorithms reach consensus with finality [10].
Load more

Recommended publications
  • Chapter 1 from Byzantine Consensus to Blockchain Consensus
    Chapter 1 From Byzantine Consensus to Blockchain Consensus CONTENTS 1.1 Introduction ....................................................... 3 1.2 Byzantine Consensus .............................................. 6 1.2.1 On System Models ........................................ 6 1.2.2 Byzantine Consensus Definitions .......................... 7 1.2.3 FLP Impossibility ......................................... 9 1.2.4 Byzantine Consensus Patterns ............................. 12 1.2.5 Hybrid Models to Reduce Processes ....................... 13 1.2.6 Randomization ............................................ 15 1.3 Blockchains with Nakamoto Consensus ............................. 19 1.3.1 Bitcoin’s Blockchain and Consensus ....................... 19 1.3.2 Blockchain Applications ................................... 24 1.3.3 Nakamoto Consensus Variants ............................. 27 1.4 Blockchains with Byzantine Consensus ............................. 30 1.4.1 Permissioned Blockchains with Byzantine Consensus ....... 30 1.4.2 Permissionless Blockchains with Hybrid Consensus ........ 32 1.5 Conclusion ........................................................ 34 3 4 ⌅ Saunders Template 1.1 Introduction Blockchain is an exciting new technology that is making headlines worldwide. The reasons behind the success of a technology are often unclear, but in the case of block- chain it is safe to say that an important factor is that is has two killer apps, not a single one. The first killer app are cryptocurrencies, as the original blockchain is the core of Bitcoin [128], the first cryptocurrency and the one that is fostering the adoption of cryptocurrencies. The second killer app are smart contracts, first introduced in the Ethereum system [40], with their promise of computerizing legal contracts and of supporting a countless number of applications [161, 153, 90]. Moreover, the sky seems to be the limit for the applications people are imagining for blockchain. A blockchain is essentially a secure, unmodifiable, append-only, log of transac- tions.
    [Show full text]
  • A Survey of Distributed Consensus Protocols for Blockchain Networks
    1 A Survey of Distributed Consensus Protocols for Blockchain Networks Yang Xiao∗, Ning Zhang†, Wenjing Lou∗, Y. Thomas Hou∗ ∗Virginia Polytechnic Institute and State University, VA, USA †Washington University in St. Louis, MO, USA Abstract—Since the inception of Bitcoin, cryptocurrencies participants. On the other hand, blockchain is also known for and the underlying blockchain technology have attracted an providing trustworthy immutable record keeping service. The increasing interest from both academia and industry. Among block data structure adopted in a blockchain embeds the hash various core components, consensus protocol is the defining technology behind the security and performance of blockchain. of the previous block in the next block generated. The use of From incremental modifications of Nakamoto consensus protocol hash chain ensures that data written on the blockchain can not to innovative alternative consensus mechanisms, many consensus be modified. In addition, a public blockchain system supports protocols have been proposed to improve the performance of third-party auditing and some blockchain systems support a the blockchain network itself or to accommodate other specific high level of anonymity, that is, a user can transact online application needs. In this survey, we present a comprehensive review and anal- using a pseudonym without revealing his/her true identity. ysis on the state-of-the-art blockchain consensus protocols. To The security properties promised by blockchain is unprece- facilitate the discussion of our analysis, we first introduce the dented and truly inspiring. Pioneering blockchain systems such key definitions and relevant results in the classic theory of fault as Bitcoin have greatly impacted the digital payment world.
    [Show full text]
  • A MILP Model for a Byzantine Fault Tolerant Blockchain Consensus
    future internet Article A MILP Model for a Byzantine Fault Tolerant Blockchain Consensus Vitor Nazário Coelho 1,* , Rodolfo Pereira Araújo 2, Haroldo Gambini Santos 3, Wang Yong Qiang 4 and Igor Machado Coelho 5,* 1 OptBlocks, Avenida Jo ao Pinheiro, 274 Sala 201-Lourdes, Belo Horizonte-MG 30130-186, Brazil 2 Graduate Program in Computational Sciences (PPG-CComp), Universidade do Estado do Rio de Janeiro, Rua S ao Francisco Xavier, 524-Maracan a, Rio de Janeiro-RJ 20550-013, Brazil; [email protected] 3 Department of Computer Science, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, Ouro Preto-MG 35400-000, Brazil; [email protected] 4 Research & Development Department, Neo Global Development, 80, Zhengxue Rd, Shanghai 200082, China; [email protected] 5 Institute of Computing, Universidade Federal Fluminense, Av. Gal. Milton Tavares de Souza, São Domingos, Niterói-RJ 24210-310, Brazil * Correspondence: [email protected] (V.N.C.); [email protected] (I.M.C.) Received: 30 September 2020; Accepted: 26 October 2020; Published: 29 October 2020 Abstract: Mixed-integer mathematical programming has been widely used to model and solve challenging optimization problems. One interesting feature of this technique is the ability to prove the optimality of the achieved solution, for many practical scenarios where a linear programming model can be devised. This paper explores its use to model very strong Byzantine adversaries, in the context of distributed consensus systems. In particular, we apply the proposed technique to find challenging adversarial conditions on a state-of-the-art blockchain consensus: the Neo dBFT. Neo Blockchain has been using the dBFT algorithm since its foundation, but, due to the complexity of the algorithm, it is challenging to devise definitive algebraic proofs that guarantee safety/liveness of the system (and adjust for every change proposed by the community).
    [Show full text]
  • Byzantine Consensus in Asynchronous Message-Passing Systems: a Survey
    Int. J. Critical Computer-Based Systems, Vol. x, No. x, xxxx 1 Byzantine Consensus in Asynchronous Message-Passing Systems: a Survey Miguel Correia*, Giuliana Santos Veronese, Nuno Ferreira Neves and Paulo Verissimo Faculdade de Ciˆenciasda Universidade de Lisboa Campo Grande, 1749-016 Lisboa, Portugal E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract Consensus is a classical distributed systems problem with both theo- retical and practical interest. Asynchronous Byzantine consensus is cur- rently at the core of some solutions for the implementation of highly- resilient computing services. This paper surveys Byzantine consensus in message-passing distributed systems, by presenting the main theo- retical results in the area, the main classes of algorithms and by dis- cussing important issues like the performance and resilience of these algorithms. Keywords: distributed algorithms; distributed systems; consensus; Byzantine faults; arbitrary faults; asynchronous model; message pass- ing; Byzantine consensus Reference to this paper should be made as follows: Correia, M., Veronese, G. S., Neves, N. F. and Verissimo, P. (xxxx) ‘Byzantine Con- sensus in Asynchronous Message-Passing Systems: a Survey’, Int. J. Critical Computer-Based Systems, Vol. x, No. x, pp.xxx–xxx. Biographical notes: Miguel Correia is an assistant professor in the Department of Informatics at the Faculty of Sciences at the University of Lisboa, and an adjunct faculty member of the Carnegie Mellon In- formation Networking Institute. More information about his research can be found at http://www.di.fc.ul.pt/∼mpc.
    [Show full text]
  • Virginia Gold 212-626-0505 [email protected]
    Contact: Virginia Gold 212-626-0505 [email protected] ACM TURING AWARD GOES TO PIONEER WHO ADVANCED RELIABILITY AND CONSISTENCY OF COMPUTING SYSTEMS Microsoft’s Lamport Contributed to Theory and Practice of Building Distributed Computing Systems that Work as Intended NEW YORK, March 18, 2014 – ACM (Association for Computing Machinery) www.acm.org today named Leslie Lamport, a Principal Researcher at Microsoft Research, as the recipient of the 2013 ACM A.M. Turing Award for imposing clear, well-defined coherence on the seemingly chaotic behavior of distributed computing systems, in which several autonomous computers communicate with each other by passing messages. He devised important algorithms and developed formal modeling and verification protocols that improve the quality of real distributed systems. These contributions have resulted in improved correctness, performance, and reliability of computer systems. The ACM Turing Award, widely considered the “Nobel Prize in Computing,” carries a $250,000 prize, with financial support provided by Intel Corporation and Google Inc. ACM President Vint Cerf noted that “as an applied mathematician, Leslie Lamport had an extraordinary sense of how to apply mathematical tools to important practical problems. By finding useful ways to write specifications and prove correctness of realistic algorithms, assuring strong foundation for complex computing operations, he helped to move verification from an academic discipline to practical tool.” Lamport’s practical and widely used algorithms and tools have applications in security, cloud computing, embedded systems and database systems as well as mission-critical computer systems that rely on secure information sharing and interoperability to prevent failure. His notions of safety, where nothing bad happens, and liveness, where something good happens, contribute to the reliability and robustness of software and hardware engineering design.
    [Show full text]
  • Byzantine Fault Tolerance
    Byzantine Fault Tolerance CS 475, Spring 2019 Concurrent & Distributed Systems Review: Why P2P? • Spreads network/cache costs across users instead of provider • No server might mean: • Easier to deploy • Less chance of overload • Single failure won’t take down the system • Harder to attack J. Bell GMU CS 475 Spring 2019 !2 Review: Napster • The good: • Simple • Finding a file is really fast, regardless of how many clients there are - master has it all • The bad: • Server becomes a single point of failure • Server does a lot of processing • Server having all of metadata implies significant legal liabilities J. Bell GMU CS 475 Spring 2019 !3 Review: Gnutella • Gnutella’s search approach is called "flooding" • Cool: • Fully decentralized • Cost of search is distributed - no single node has to search through all of the data • Bad: • Search requires contacting many nodes! • Who can know when your search is done? • What if nodes leave while you are searching? J. Bell GMU CS 475 Spring 2019 !4 Review: BitTorrent • Goal: • Get large files out to as many users as possible, quickly • Usages: • Static bulk content (Big software updates, videos, etc) • User model is cooperative • While downloading a large file, also sharing the parts that you have • After you get the file, keep sharing for a while too • Approach relies on a “tracker” per file J. Bell GMU CS 475 Spring 2019 !5 Is our system well behaved? Byzantine Start talking about for P2P systems Partitions What we’ve done so far Crash-fail J. Bell GMU CS 475 Spring 2019 !6 Byzantine Failures in P2P May I have this totally legal, not copyrighted video please? Sure, here it is! J.
    [Show full text]
  • Distributed Computing Column 36 Distributed Computing: 2009 Edition
    Distributed Computing Column 36 Distributed Computing: 2009 Edition Idit Keidar Dept. of Electrical Engineering, Technion Haifa, 32000, Israel [email protected] It’s now the season for colorful reviews of 2009. While you can read elsewhere about the year in sports, top-40 pop hit charts, people of the year, and so on, I throw my share into the mix with a (biased) review of this year’s major distributed computing events. Awards First, let’s look at awards. This year we learned that two women were recognized with ACM and IEEE prestigious awards for their achievements in, (among other things), distributed computing. Barbara Liskov won the 2008 ACM Turing Award for a range of contributions to practical and theoretical foundations of programming language and system design, some of which are in distributed computing. The award citation mentions her impact on distributed programming, decentralized information flow, replicated storage, and modular upgrading of distributed systems. I include below a short reflection, by Rodrigo Rodrigues, on Barbara Liskov’s Turing Award and legacy. Looking ahead to 2010, it was recently announced that Nancy Lynch is the recipient of the prestigious 2010 IEEE Emanuel R. Piore Award1 for her contributions to foundations of distributed and concurrent computing. The award is given for outstanding contributions in the field of information processing in relation to computer science. Leslie Lamport has won the same award in 2004 for his seminal contributions to the theory and practice of concurrent programming and fault-tolerant computing. The 2009 Edsger W. Dijkstra Prize in Distributed Computing was awarded to Joseph Halpern and Yoram Moses for “Knowledge and Common Knowledge in a Distributed Environment”.
    [Show full text]
  • Leaderless Byzantine Fault Tolerance
    Leaderless Byzantine Fault Tolerance Tian Qin Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2020-121 http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-121.html May 29, 2020 Copyright © 2020, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Acknowledgement I would like to express my appreciation to Jian Liu and Peng Gao for their guidance, advice, and feedbacks during this research work. Leaderless Byzantine Fault Tolerance by Tian Qin Research Project Submitted to the Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, in partial satisfaction of the requirements for the degree of Master of Science, Plan II. Approval for the Report and Comprehensive Examination: Committee: Professor Dawn Song Research Advisor (Date) * * * * * * * Professor Sylvia Ratnasamy Second Reader Leaderless Byzantine Fault Tolerance ABSTRACT transaction commitment is deterministic; it is built upon a gossip In this work, we propose Leaderless Byzantine Fault Tolerance protocol to relay messages and data across the whole network, prac- (LBFT), a novel consensus algorithm that combines Snowball algo- tical Byzantine Fault Tolerance algorithm to commit transactions rithm and Practical Byzantine Fault Tolerance (pBFT) algorithm, in a deterministic manner, and Snowball to realize decentralization two existing consensus algorithms.
    [Show full text]
  • Modelling and Testing Composite Byzantine-Fault Tolerant Consensus Protocols
    Modelling and Testing Composite Byzantine-Fault Tolerant Consensus Protocols Daniel Lok Capstone Final Report for BSc (Honours) in Mathematical, Computational, and Statistical Sciences Supervised by: Dr. Ilya Sergey AY 2018/2019 i Acknowledgements This capstone would not have been possible without the support of numer- ous friends and faculty. We thank Dr. Ilya Sergey for his invaluable advice on every aspect of this project, as well as his eagerness in teaching and recommending additional sources of information. We would also like to thank Dr. Olivier Danvy for his course Functional Programming and Proving, which provided a rich foundation on which much of this capstone was built. Additionally, we would like to thank the residents and ex-residents of Elm College’s 17th floor for emotional support, and in particular, Yong Kai Yi and Cephas Tan for their helpful comments and advice on the direction, structure, and wording of this manuscript. ii YALE-NUS COLLEGE Abstract B.Sc (Hons) Modelling and Testing Composite Byzantine-Fault Tolerant Consensus Protocols by Daniel LOK Implementing distributed systems is a notoriously difficult task. Program- mers that attempt to build executable versions of published consensus pro- tocols often run into numerous bugs along the way, in part due to the often informal nature of the publications. This project provides a framework to conveniently implement and test models of distributed consensus protocols, with the intention of assisting programmers in formulating “correct” system sematics prior to actual im- plementation. This paper describes the internal workings of the framework, its API, and how to use it to model complex phenomena such as Byzantine faults, modular protocol composition, and asynchrony.
    [Show full text]
  • Enabling Secure and Resource-Efficient Blockchain Networks with VOLT
    Enabling secure and resource-efficient blockchain networks with VOLT Srinath Setty Soumya Basu? Lidong Zhou Michael L. Roberts Ramarathnam Venkatesan Microsoft Research ?Cornell University Abstract per second) at relatively low latency (e.g., a few seconds). This paper describes VOLT, a permissioned blockchain While Byzantine fault-tolerant (BFT) protocols [22, network for a group of autonomous organizations to au- 31, 36, 55] seem to solve the core consensus problem, tomate cross-organizational business processes. Specif- subtle, but fundamental, aspects render these protocols ically, VOLT ensures that a correct member—without insufficient for permissioned blockchain networks, even trusting anyone else—shares a consistent history of trans- though the underlying substrate in BFT remains relevant. actions with other members in the system. VOLT achieves These BFT protocols were primarily designed to replicate this strong security property using the concept of a self- services in a datacenter environment—to guard against verifying ledger: an append-only ledger of transactions crash failures and Byzantine behavior by a subset of the that can be checked locally by a verifier process at each replicas. In the context of permissioned blockchain net- node; the ledger also embeds succinct cryptographic mes- works, the protocol has to be carried out across trusted sages encoding views of members to enable each veri- domains with each member representing a participating fier to locally determine globally-committed transactions. organization (such as a bank), rather than a replica of a To orchestrate this concept, VOLT employs a logically replicated service typically in the same trusted domain. centralized—but untrusted—service provider. We instan- Consequently, the desirable guarantees must be defined tiate the service provider using a highly-available cloud from the perspective of each member, rather than for a service built atop a fault-tolerant cloud storage service.
    [Show full text]
  • Byzantizing Paxos by Refinement
    Byzantizing Paxos by Refinement Leslie Lamport 5 December 2010 Abstract We derive a 3f +1 process Byzantine Paxos consensus algorithm by Byzantizing a variant of the ordinary Paxos algorithm|that is, by having 2f + 1 nonfaulty processes emulate the ordinary Paxos algo- rithm despite the presence of f malicious processes. We have writ- ten a formal, machine-checked proof that the Byzantized algorithm implements the ordinary Paxos consensus algorithm under a suitable refinement mapping. Contents 1 Introduction 1 2 Consensus and Classic Paxos 2 2.1 Consensus . .2 2.2 Paxos Consensus . .2 3 Byzantizing An Algorithm 4 4 Algorithm PCon 7 5 Algorithm BPCon 8 6 Liveness and Learning About Sent Messages 10 6.1 Sending Proofs . 11 6.2 Relaying 1b Messages . 12 7 The Castro-Liskov Algorithm 13 8 The Formal Specifications and Proof 14 9 Conclusion 16 References 16 You can verb anything. Ron Ziegler (quoted by Brian Reid) 1 Introduction The Paxos algorithm [6] has become a standard tool for implementing fault- tolerant distributed systems. It uses 2f + 1 processes to tolerate the benign failure of any f of them. More recently, Castro and Liskov developed a 3f + 1 process algorithm [2] that tolerates f Byzantine (maliciously faulty) processes. Intuitively, their algorithm seems to be a Byzantine version of Paxos. Other algorithms that also seem to be Byzantine versions of Paxos have subsequently appeared [4, 11, 14]. The only previous attempt we know of to explain the relation between a Byzantine Paxos algorithm and ordinary Paxos was by Lampson [13]. He derived both from an abstract, non-distributed algorithm.
    [Show full text]
  • Gosig: Scalable Byzantine Consensus on Adversarial Wide Area Network for Blockchains
    Gosig: Scalable Byzantine Consensus on Adversarial Wide Area Network for Blockchains Peilun Li Guosai Wang Xiaoqi Chen Tsinghua University Tsinghua University Princeton University Wei Xu Tsinghua University Abstract longer have to worry about Sybil attacks [21], there are Existing Byzantine fault tolerance (BFT) protocols still some other significant challenges. face significant challenges in the consortium blockchain The blockchain is replicated to each participant, and scenario. On the one hand, we can make little assump- the key problem is how to reach consensus on these repli- tions about the reliability and security of the underlying cas. Comparing to traditional distributed transaction sys- Internet. On the other hand, the applications on consor- tems, the three biggest challenges of a blockchain are: tium blockchains demand a system as scalable as the Bit- 1) Players are from different organizations without coin but providing much higher performance, as well as mutual trust. Failures, even Byzantine failures, are com- provable safety. We present a new BFT protocol, Gosig, mon. Thus, we cannot rely on a small quorum (e.g., that combines crypto-based secret leader selection and Chubby [9] or ZooKeeper [27]) for consensus. Instead, multi-round voting in the protocol layer with implemen- we need Byzantine consensus and allow all players to tation layer optimizations such as gossip-based message participate, i.e., supporting a consensus group with thou- propagation. In particular, Gosig guarantees safety even sands of servers. in a network fully controlled by adversaries, while pro- 2) The system runs on an open Internet. The network viding provable liveness with easy-to-achieve network can drop/delay communication arbitrarily.
    [Show full text]