Performance Modeling of the Consensus Mechanism in a Permissioned Blockchain

Krieger, Udo R.; Ziegler, Michael Herbert; Cech, Hendrik

doi:10.1007/978-3-030-21952-9_1

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Candidates are a mid-state transiting from follower to leader. The whole Raft consensus can be CTMC GI/M/1 queue [104] PoW Bitcoin mean stationary number of txs in the queue and in the block M/G/1 queue variant [105] PoW Bitcoin confirmation time and tx delay non-exhaustive queue [106] PoW NA mean number of txs and mean confirmation time of txs in the system Discrete-time GI/GI N /1 queue [107] Proof-of-Authority Ethereum system queue size and tx waiting time M/M B /1 queue [71] BFT-SMaRt HLF tx latency M/G/1 and M/M/1 queue [108] PBFT NA system delay (n,k) fork-join queue [109] vote-based consensus permissioned blockchain blocks commitment delay, block validation response time and synchronization processes among mining nodes.…”

Section: A Markov Chains For Modelling Dlt Consensusesmentioning

confidence: 99%

“…In the literature, this model is highly prevalent for performance modelling (e.g., estimating the sojourn time of jobs in the queues) of parallel and distributed systems. Recently, it has been found effective for use in studying the delay performance of the synchronization process of the vote-based permissioned blockchain systems [109]. A typical non-purging (n, k) fork-join queueing model is illustrated in Fig.…”

Section: ) Queueing Models For Vote-based Consensusesmentioning

confidence: 99%

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Performance Evaluation of Blockchain Systems: A Systematic Survey

et al. 2020

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Blockchain has been envisioned to be a disruptive technology with potential for applications in various industries. As more and more different blockchain platforms have emerged, it is essential to assess their performance in different use cases and scenarios. In this paper, we conduct a systematic survey on the blockchain performance evaluation by categorizing all reviewed solutions into two general categories, namely, empirical analysis and analytical modelling. In the empirical analysis, we comparatively review the current empirical blockchain evaluation methodologies, including benchmarking, monitoring, experimental analysis and simulation. In analytical modelling, we investigate the stochastic models applied to performance evaluation of mainstream blockchain consensus algorithms. Through contrasting, comparison and grouping different methods together, we extract important criteria that can be used for selecting the most suitable evaluation technique for optimizing the performance of blockchain systems based on their identified bottlenecks. Finally, we conclude the survey by presenting a list of possible directions for future research.

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Section: A Markov Chains For Modelling Dlt Consensusesmentioning

confidence: 99%

Section: ) Queueing Models For Vote-based Consensusesmentioning

confidence: 99%

Performance Evaluation of Blockchain Systems: A Systematic Survey

et al. 2020

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“…Wang et al (2018) provides a taxonomy for blockchains, explains common blockchain consensus methods, analyzes blockchain applications, and explores technical problems as well as recent breakthroughs in addressing those challenges. Krieger et al (2019) investigate the effects of the most important design and load factors on the time it takes for new blocks to be committed to the blockchain after successful commitment choices and approval by the fully distributed consensus mechanism. A simplified example of a fully linked P2P graph using mean-value analysis techniques is used to demonstrate the suggested analysis of a permissioned blockchain.…”

Section: Introductionmentioning

confidence: 99%

Blockchain Technologies in the Paper Industry and Performance Analysis

MIA¹,

Chowdhury²

2021

Preprint

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Blockchain is the technology which consists of a chain of blocks that store the necessary information including digital signatures with a validation process in a decentralized and distributed type of network. Immutability, transparency and auditability, make the transactions more secure and tamper proof. In Bangladesh if the paper industries use the block chain technology, then their production and transparency will be very high. Blockchain technology can effectively record every asset throughout its flow on the supply chain, contribute to tracking orders, receipts, and payments. In this paper, data is collected from a large paper industry in Bangladesh and also from online databases for comparison. A qualitative thematic analysis has been performed in accordance with the investigative processes that are developed by Creswell. We evaluated the impact of the most influential design and parameters (load) on the resulting commitment. For analyzing the performance of the model, Markovian Non-purging fork join queueing model is used. Finally, the model is studied by the real word data and to show its functionality.

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