Performance Perspective on Private Distributed Ledger Technologies for Industrial Networks

Geyer, Fabien; Kinkelin, Holger; Leppelsack, Hendrik; Liebald, Stefan; Scholz, Dominik; Carle, Georg; Schupke, Dominic

doi:10.1109/netsys.2019.8854512

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…Content may change prior to final publication. [71] using Caliper [61] and a dedicated testbed on which network parameters, such as latency or packet loss, can be configured. In the experiments, the influence of transaction rate, chaincode, network properties, local network impairment, and block size have been separately examined and quantitatively analyzed.…”

Section: ) Hyperledger Performance Analysismentioning

confidence: 99%

“…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%

“…Otherwise, any check fail will mark the transaction as invalid, and its effects are disregarded. Geyer et al [71] modelled the Solo ordering process of HLF as an M/M B /1 queueing system. According to the previously described three phases, transactions with endorsements arrive at the orderer at different times and are queued.…”

Section: ) Queueing Models For Vote-based Consensusesmentioning

confidence: 99%

See 2 more Smart Citations

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: ) Hyperledger Performance Analysismentioning

confidence: 99%

Section: A Markov Chains For Modelling Dlt Consensusesmentioning

confidence: 99%

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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“…Reading from the ledger is the most performed action in blockchain networks which is the time between sending the reading request until the replay is received. Improving the reading latency will help to optimize the authentication time [67].…”

Section: Read Latencymentioning

confidence: 99%

“…The metric of defining how many reading operations are performed in a period of time. This metric is not really relevant for blockchain network but important for blockchain-based identity solution as most solutions relay to blockchain performance, not to the identity system needed [67].…”

Section: Read Throughputmentioning

confidence: 99%

Distributed Ledger Technology for eHealth Identity Privacy: State of The Art and Future Perspective

Bouras

Qing-hua

Zhang

et al. 2020

Sensors

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Electronic healthcare (eHealth) identity management (IdM) is a pivotal feature in the eHealth system. Distributed ledger technology (DLT) is an emerging technology that can achieve agreements of transactional data states in a decentralized way. Building identity management systems using Blockchain can enable patients to fully control their own identity and provide increased confidence in data immutability and availability. This paper presents the state of the art of decentralized identity management using Blockchain and highlights the possible opportunities for adopting the decentralized identity management approaches for future health identity systems. First, we summarize eHealth identity management scenarios. Furthermore, we investigate the existing decentralized identity management solutions and present decentralized identity models. In addition, we discuss the current decentralized identity projects and identify new challenges based on the existing solutions and the limitations when applying it to healthcare as a particular use case.

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A Performance Evaluation Method for a Class of Cross-Chain Systems

Wu,

Huang,

et al. 2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Performance Perspective on Private Distributed Ledger Technologies for Industrial Networks

Cited by 14 publications

References 13 publications

Performance Evaluation of Blockchain Systems: A Systematic Survey

Performance Evaluation of Blockchain Systems: A Systematic Survey

Distributed Ledger Technology for eHealth Identity Privacy: State of The Art and Future Perspective

A Performance Evaluation Method for a Class of Cross-Chain Systems

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