A combinatorial double auction resource allocation model in cloud computing

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• 作者：Parnia Samimi^a ; ^{parniasamimi62@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Youness Teimouri^b ; ^{youness126@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Muriati Mukhtar^a ; ^{mm@ftsm.ukm.my" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Economic model ; Resource allocation ; Combinatorial double auction ; Cloud computing ; Grid computing ; CloudSim
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：20 August 2016
• 年：2016
• 卷：357
• 期：Complete
• 页码：201-216
• 全文大小：1182 K

文摘

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.