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Prediction of the Consolidation Delay in Blockchain-based Applications

Lookup NU author(s): Emeritus Professor Isi Mitrani

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by ACM, 2021.

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Abstract

In the last years, blockchains have become a popular technology to store immutabledata validated in a peer-to-peer way. Software systems can take advantage of blockchains to publicly store data (organised in transactions) which is immutable by design. The most important consensus algorithm in public blockchains is the proof-of-work in which miners invest a huge computational power to consolidate new data in a ledger. Miners receive incentives for their work, i.e., a fee decided and paid for each transaction. Rational miners aim to maximise the profit generated by the mining activity, and thus choose the transactions offering the highest fee per byte for their consolidation. In this paper, we propose a queueing model to study the relation between the fee offered by a transaction and its expected consolidation time, i.e., the time required to be added to the blockchain by the miners. The solution of the queueing model, although approximate, is computationally and numerically efficient and software systems can use it online to analyse the trade-off between costs and response times. Indeed, a static configuration of the model would not account for the high variations in the blockchain workload and fees offered by other users.The model takes into account the dropping of transactions caused by timeouts or finite capacity transaction pools. We validate our results with data extracted from the Bitcoin blockchain and with discrete event simulations.


Publication metadata

Author(s): Balsamo S, Marin A, Mitrani I, Rebagliati N

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Performance Engineering (ICPE 2021)

Year of Conference: 2021

Pages: 81-92

Online publication date: 19/04/2021

Acceptance date: 17/04/2021

Date deposited: 31/05/2021

Publisher: ACM

URL: https://doi.org/10.1145/3427921.3450249

DOI: 10.1145/3427921.3450249

Library holdings: Search Newcastle University Library for this item

ISBN: 9781450381949


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