Selection of the Graphics Card to be used in Ethereum Mining with Linear BWM-TOPSIS
DOI:
https://doi.org/10.5281/zenodo.5136822Аннотация
Blockchain technology is becoming more and more important and new usage areas are emerging every day. However, the most fundamental one of these usage areas is cryptocurrencies, which led to the emergence of blockchain technology. Cryptocurrency transfers are made possible with mining. Although there are many cryptocurrencies available today, a lot of them use Ethereum-based blockchain technology. The choice of the most optimal graphics card (GPU; Graphics Processing Unit) in cryptocurrency mining is very important for the efficiency and profitability of the mining operations to be performed. Since this decision problem depends on more than one criterion, it should be handled using Multiple-Criteria Decision-Making Methods (MCDM). Accordingly, the study focused on the mining of Ethereum-based cryptocurrencies and the selection of the optimal GPU to be used in mining with linear BWM-TOPSIS. As a result of the study, a model is presented in which miners can choose the most efficient GPU for them and the optimal GPU as of January 2020 has been determined.
Загрузки
Опубликован
Как цитировать
Выпуск
Раздел
Лицензия
The Author(s) must make formal transfer of copyright for each article prior to publication in the International Journal of Contemporary Economics and Administrative Sciences. Such transfer enables the Journal to defend itself against plagiarism and other forms of copyright infringement. Your cooperation is appreciated. You agree that copyright of your article to be published in the International Journal of Contemporary Economics and Administrative Sciences is hereby transferred, throughout the World and for the full term and all extensions and renewals thereof, to International Journal of Contemporary Economics and Administrative Sciences.
The Author(s) reserve(s): (a) the trademark rights and patent rights, if any, and (b) the right to use all or part of the information contained in this article in future, non-commercial works of the Author's own, or, if the article is a "work-for-hire" and made within the scope of the Author's employment, the employer may use all or part of the information contained in this article for intra-company use, provided the usual acknowledgements are given regarding copyright notice and reference to the original publication.
The Author(s) warrant(s) that the article is Author's original work, and has not been published before. If excerpts from copyrighted works are included, the Author will obtain written permission from the copyright owners and shall credit the sources in the article. The author also warrants that the article contains no libelous or unlawful statements, and does not infringe on the rights of others. If the article was prepared jointly with other Author(s), the Author agrees to inform the co-Author(s) of the terms of the copyright transfer and to sign on their behalf; or in the case of a "work-for-hire" the employer or an authorized representative of the employer.
The journal is registered with the ISSN : 1925-4423.
IJCEAS is licensed under a Creative Commons Attribution 4.0 International License.
This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.
