Implementation of a Distributed Consensus Algorithm with OMNeT++

2009 ◽  
Vol 19 (1) ◽  
pp. 41-42
Author(s):  
Andreas Dielacher ◽  
Thomas Handl ◽  
Christian Widtmann
Keyword(s):  
Consensus Algorithm ◽  
Distributed Consensus

Second-Order Consensus Filtering Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 1621-1624
Author(s):  
Huan Xin Peng ◽  
Wen Kai Wang
Keyword(s):  
Second Order ◽  
Consensus Algorithm ◽  
Distributed Consensus ◽  
Filtering Algorithm ◽  
First Order

In order to improve the accuracy of consensus filters, in this paper, we propose a second-order consensus filtering algorithm based on the pseudo two-hop distributed consensus algorithm, we analyze the performance of the second-order consensus filtering algorithm, and prove that the second-order consensus filtering algorithm is convergent. We analyze the filtering accuracy of the second-order consensus filtering algorithm, and make a comparison for filtering accuracy between the first-order consensus filtering algorithm and the second-order consensus filtering algorithm, simultaneously, simulation is proposed, the results show the second-order consensus filtering algorithm is convergent, and the filtering accuracy is higher than that of the first-order consensus algorithm.

scholarly journals Spire: A Cooperative, Phase-Symmetric Solution to Distributed Consensus

2021 ◽  
Author(s):  
Emil Koutanov
Keyword(s):  
Protocol Design ◽  
Consensus Algorithm ◽  
Distributed Consensus ◽  
Dominant Position ◽  
Successive Iteration ◽  
Basic Model ◽  
A Value ◽  
Viable Solution ◽  
Phase Symmetry ◽  
Do So

All existing solutions to distributed consensus are organised around a Paxos-like structure wherein processes contend for exclusive leadership in one phase, and then either use their dominant position to propose a value in the next phase or elect an alternate leader. This approach may be characterised as adversarial and phase-asymmetric, requiring distinct message schemas and process behaviours for each phase. In over three decades of research, no algorithm has diverged from this basic model, alluding to it perhaps being the only viable solution to consensus. This paper presents a new consensus algorithm named Spire, characterised by a phase-symmetric, cooperative structure. Processes do not contend for leadership; instead, they collude to iteratively establish a dominant value and may do so concurrently without conflicting. Each successive iteration is structured identically to the previous, employing the same messages and invoking the same behaviour. By these characteristics, Spire buckles the trend in protocol design, proving that at least two disjoint cardinal solutions to consensus exist. The resulting phase symmetry halves the number of distinct messages and behaviours, offering a clear intuition and an approachable foundation for learning consensus and building practical systems.

scholarly journals A generalization of the graph Laplacian with application to a distributed consensus algorithm

2015 ◽  
Vol 25 (2) ◽  
pp. 353-360
Author(s):  
Guisheng Zhai
Keyword(s):  
Spectral Properties ◽  
Graph Laplacian ◽  
Positive Definite ◽  
Consensus Algorithm ◽  
Distributed Consensus ◽  
Positive Definite Matrices ◽  
Nonnegative Definite

Abstract In order to describe the interconnection among agents with multi-dimensional states, we generalize the notion of a graph Laplacian by extending the adjacency weights (or weighted interconnection coefficients) from scalars to matrices. More precisely, we use positive definite matrices to denote full multi-dimensional interconnections, while using nonnegative definite matrices to denote partial multi-dimensional interconnections. We prove that the generalized graph Laplacian inherits the spectral properties of the graph Laplacian. As an application, we use the generalized graph Laplacian to establish a distributed consensus algorithm for agents described by multi-dimensional integrators.

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

2019 ◽  
Vol 12 (4) ◽  
pp. 177 ◽  
Author(s):  
Qi Deng
Keyword(s):  
Artificial Intelligence ◽  
Fault Tolerance ◽  
Large Scale ◽  
Economic Value ◽  
Economic Models ◽  
Consensus Algorithm ◽  
Economic Policies ◽  
Byzantine Fault Tolerance ◽  
Distributed Consensus ◽  
Byzantine Fault

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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