Fault-scalable Byzantine fault-tolerant services

Abd-El-Malek, Michael; Ganger, Gregory R.; Goodson, Garth R.; Reiter, Michael K.; Wylie, Jay J.

doi:10.1145/1095809.1095817

Cited by 135 publications

(19 citation statements)

References 38 publications

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“…Singh et al [21] provided the simulation framework BFT-Sim to evaluate BFT protocols. It builds upon the high-level declarative language P2 to implement three different BFT protocols [2], [10], [22] as well as ns-2, to explore various network conditions.…”

Section: B Bft Modelsmentioning

confidence: 99%

Bernoulli Meets PBFT: Modeling BFT Protocols in the Presence of Dynamic Failures

Nischwitz

Esche

Tschorsch

2021

Annals of Computer Science and Information Systems

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The publication of the pivotal state machine replication protocol PBFT laid the foundation for a body of BFT protocols. We introduce a probabilistic model for evaluating BFT protocols in the presence of dynamic link and crash failures. The model is derived from the communication pattern, facilitating an adaptation to other protocols. The state of replicas is captured and used to derive the success probability of the protocol execution. To this end, we examine the influence of link and crash failure rates as well as the number of replicas. A comparison in protocol behavior of PBFT, Zyzzyva and SBFT is performed.

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Section: B Bft Modelsmentioning

confidence: 99%

Bernoulli Meets PBFT: Modeling BFT Protocols in the Presence of Dynamic Failures

Nischwitz

Esche

Tschorsch

2021

Annals of Computer Science and Information Systems

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“…Byzantine agreement has been the subject of extensive research and has seen a recent renewal of interest due to its application in blockchains [9]. To tolerate 𝑓 faults, Byzantine agreement algorithms typically require 3𝑓 +1 replicas [11,17,36], and some even require 5𝑓 +1 replicas [1,50]. This bound can be lowered, for example, to 2𝑓 + 1 if synchrony and digital signatures are assumed [2].…”

Section: Related Workmentioning

confidence: 99%

Byzantine Eventual Consistency and the Fundamental Limits of Peer-to-Peer Databases

Kleppmann¹,

Howard²

2020

Preprint

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Sybil attacks, in which a large number of adversary-controlled nodes join a network, are a concern for many peer-to-peer database systems, necessitating expensive countermeasures such as proofof-work. However, there is a category of database applications that are, by design, immune to Sybil attacks because they can tolerate arbitrary numbers of Byzantine-faulty nodes. In this paper, we characterize this category of applications using a consistency model we call Byzantine Eventual Consistency (BEC). We introduce an algorithm that guarantees BEC based on Byzantine causal broadcast, prove its correctness, and demonstrate near-optimal performance in a prototype implementation.

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“…Column matrix W is used in the heuristic mode only, with the same constraints as matrix V. The operator "•" represents Hadamard multiplication, and the operator " BFT mode for the task at hand. The flavors of BFT algorithms for the DABFT to choose from include, but not limited to, DBFT and PBFT (Practical BFT), as well as Q/U (Abd-El- Malek et al 2005), HQ (Cowling et al 2006), Zyzzyva (Kotla et al 2009), Quorum (Guerraoui et al 2010), Chain (Guerraoui et al 2010), Ring (Guerraoui et al 2011), and RBFT (Redundant BFT) (Aublin et al 2013), etc. Figure 3 shows the consensus process for several mainstream BFT algorithms.…”

Section: Consensus Building Processmentioning

confidence: 99%

Blockchain Economical Models, Delegated Proof of Economic Value and Delegated Adaptive Byzantine Fault Tolerance and their implementation in Artificial Intelligence BlockCloud

Deng

2019

JRFM

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The Artificial Intelligence BlockCloud (AIBC) is an artificial intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer (the latter three are the collective “upper-layers”). The AIBC layers have distinguished responsibilities and thus performance and robustness requirements. The upper layers need to follow a set of economic policies strictly and run on a deterministic and robust protocol. While the fundamental layer needs to follow a protocol with high throughput without sacrificing robustness. As such, the AIBC implements a two-consensus scheme to enforce economic policies and achieve performance and robustness: Delegated Proof of Economic Value (DPoEV) incentive consensus on the upper layers, and Delegated Adaptive Byzantine Fault Tolerance (DABFT) distributed consensus on the fundamental layer. The DPoEV uses the knowledge map algorithm to accurately assess the economic value of digital assets. The DABFT uses deep learning techniques to predict and select the most suitable BFT algorithm in order to enforce the DPoEV, as well as to achieve the best balance of performance, robustness, and security. The DPoEV-DABFT dual-consensus architecture, by design, makes the AIBC attack-proof against risks such as double-spending, short-range and 51% attacks; it has a built-in dynamic sharding feature that allows scalability and eliminates the single-shard takeover. Our contribution is four-fold: that we develop a set of innovative economic models governing the monetary, trading and supply-demand policies in the AIBC; that we establish an upper-layer DPoEV incentive consensus algorithm that implements the economic policies; that we provide a fundamental layer DABFT distributed consensus algorithm that executes the DPoEV with adaptability; and that we prove the economic models can be effectively enforced by AIBC’s DPoEV-DABFT dual-consensus architecture.

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Fault-scalable Byzantine fault-tolerant services

Cited by 135 publications

References 38 publications

Bernoulli Meets PBFT: Modeling BFT Protocols in the Presence of Dynamic Failures

Bernoulli Meets PBFT: Modeling BFT Protocols in the Presence of Dynamic Failures

Byzantine Eventual Consistency and the Fundamental Limits of Peer-to-Peer Databases

Blockchain Economical Models, Delegated Proof of Economic Value and Delegated Adaptive Byzantine Fault Tolerance and their implementation in Artificial Intelligence BlockCloud

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