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Blockchain and Cryptocurrencies - a Classification and Comparison of Architecture Drivers

  • Martin Garriga (Corresponding author)
  • , Stefano Dalla Palma
  • , Maximiliano Arias
  • , Alan De Renzis
  • , Remo Pareschi
  • , Damian Andrew Tamburri

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

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Samenvatting

Blockchain is a decentralized transaction and data management solution, the technological leap behind the success of Bitcoin and other cryptocurrencies. As the variety of existing blockchains and distributed ledgers continues to increase, adopters should focus on selecting the solution that best fits their needs and the requirements of their decentralized applications, rather than developing yet another blockchain from scratch. In this article we present a conceptual framework to aid software architects, developers, and decision makers to adopt the right blockchain technology. The framework exposes the interrelation between technological decisions and architectural features, capturing the knowledge from existing academic literature, industrial products, technical forums/blogs, and experts' feedback. We empirically show the applicability of our framework by dissecting the platforms behind Bitcoin and other top 10 cryptocurrencies, aided by a focus group with researchers and industry practitioners. Then, we leverage the framework together with key notions of the architectural tradeoff analysis method to analyze four real-world blockchain case studies from industry and academia. Results shown that applying our framework leads to a deeper understanding of the architectural tradeoffs, allowing to assess technologies more objectively and select the one that best fit developers' needs, ultimately cutting costs, reducing time-to-market and accelerating return on investment.

Originele taal-2Engels
Artikelnummere5992
Aantal pagina's21
TijdschriftConcurrency and Computation : Practice & Experience
Volume33
Nummer van het tijdschrift8
DOI's
StatusGepubliceerd - 25 apr 2021

Financiering

The authors would like to thank to the anonymous reviewers for their valuable feedback. Also, to Fidtech's team and ArtID's team for their helpful insights. This research is partially supported by the European Commission grant no. 825480 (H2020), SODALITE, and by the grant no. 825040 (H2020), RADON; finally, by grant ANPCyT PICT‐2017‐1725.

FinanciersFinanciernummer
European Union’s Horizon Europe research and innovation programme
European Commission825480, H2020
National Agency for Research and Development PICT‐2017‐1725

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