دانلود مقاله ISI انگلیسی شماره 74044
ترجمه فارسی عنوان مقاله

مدل تخصیص منابع مزایده دو برابر ترکیبی در رایانش ابری

عنوان انگلیسی
A combinatorial double auction resource allocation model in cloud computing
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
74044 2016 16 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Information Sciences, Volume 357, 20 August 2016, Pages 201–216

ترجمه کلمات کلیدی
مدل اقتصادی؛ تخصیص منابع؛ مزایده دو برابر ترکیبی؛ رایانش ابری؛ محاسبات شبکه؛ CloudSim
کلمات کلیدی انگلیسی
Economic model; Resource allocation; Combinatorial double auction; Cloud computing; Grid computing; CloudSim

چکیده انگلیسی

Users and providers have different requirements and objectives in an investment market. Users will pay the lowest price possible with certain guaranteed levels of service at a minimum and providers would follow the strategy of achieving the highest return on their investment. Designing an optimal market-based resource allocation that considers the benefits for both the users and providers is a fundamental criterion of resource management in distributed systems, especially in cloud computing services. Most of the current market-based resource allocation models are biased in favor of the provider over the buyer in an unregulated trading environment. In this study, the problem was addressed by proposing a new market model called the Combinatorial Double Auction Resource Allocation (CDARA), which is applicable in cloud computing environments. The CDARA was prototyped and simulated using CloudSim, a Java-based simulator for simulating cloud computing environments, to evaluate its efficiency from an economic perspective. The results proved that the combinatorial double auction-based resource allocation model is an appropriate market-based model for cloud computing because it allows double-sided competition and bidding on an unrestricted number of items, which causes it to be economically efficient. Furthermore, the proposed model is incentive-compatible, which motivates the participants to reveal their true valuation during bidding.