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Article: Customer Perceived Value- And Risk-Aware Multiserver Configuration for Profit Maximization

TitleCustomer Perceived Value- And Risk-Aware Multiserver Configuration for Profit Maximization
Authors
KeywordsCloud computing
customer perceived value
dynamic pricing
multiserver configuration
profit maximization
risk
Issue Date2020
Citation
IEEE Transactions on Parallel and Distributed Systems, 2020, v. 31, n. 5, p. 1074-1088 How to Cite?
AbstractAlong with the wide deployment of infrastructures and the rapid development of virtualization techniques in cloud computing, more and more enterprises begin to adopt cloud services, inspiring the emergence of various cloud service providers. The goal of cloud service providers is to pursue profit maximization. To achieve this goal, cloud service providers need to have a good understanding of the economics of cloud computing. However, the existing pricing strategies rarely consider the interaction between user requests for services and the cloud service provider and hence cannot accurately reflect the supply and demand law of the cloud service market. In addition, few previous pricing strategies take into account the risk involved in the pricing contract. In this article, we first propose a dynamic pricing strategy that is developed based on the customer perceived value (CPV) and is able to accurately capture the real situation of supply and demand in marketing. The strategy is utilized to estimate the user's demand for cloud services. We then design a profit maximization scheme that is developed based on the CPV-aware dynamic pricing strategy and considers the risk in the pricing contract. The scheme is utilized to derive the optimal multiserver configuration for maximizing the profit. Extensive simulations are carried out to verify the proposed customer perceived value and risk-aware profit maximization scheme. As compared to two state of the art benchmarking methods, the proposed scheme gains 31.6 and 30.8 percent more profit on average, respectively.
Persistent Identifierhttp://hdl.handle.net/10722/336409
ISSN
2023 Impact Factor: 5.6
2023 SCImago Journal Rankings: 2.340
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Tian-
dc.contributor.authorZhou, Junlong-
dc.contributor.authorZhang, Gongxuan-
dc.contributor.authorWei, Tongquan-
dc.contributor.authorHu, Shiyan-
dc.date.accessioned2024-01-15T08:26:38Z-
dc.date.available2024-01-15T08:26:38Z-
dc.date.issued2020-
dc.identifier.citationIEEE Transactions on Parallel and Distributed Systems, 2020, v. 31, n. 5, p. 1074-1088-
dc.identifier.issn1045-9219-
dc.identifier.urihttp://hdl.handle.net/10722/336409-
dc.description.abstractAlong with the wide deployment of infrastructures and the rapid development of virtualization techniques in cloud computing, more and more enterprises begin to adopt cloud services, inspiring the emergence of various cloud service providers. The goal of cloud service providers is to pursue profit maximization. To achieve this goal, cloud service providers need to have a good understanding of the economics of cloud computing. However, the existing pricing strategies rarely consider the interaction between user requests for services and the cloud service provider and hence cannot accurately reflect the supply and demand law of the cloud service market. In addition, few previous pricing strategies take into account the risk involved in the pricing contract. In this article, we first propose a dynamic pricing strategy that is developed based on the customer perceived value (CPV) and is able to accurately capture the real situation of supply and demand in marketing. The strategy is utilized to estimate the user's demand for cloud services. We then design a profit maximization scheme that is developed based on the CPV-aware dynamic pricing strategy and considers the risk in the pricing contract. The scheme is utilized to derive the optimal multiserver configuration for maximizing the profit. Extensive simulations are carried out to verify the proposed customer perceived value and risk-aware profit maximization scheme. As compared to two state of the art benchmarking methods, the proposed scheme gains 31.6 and 30.8 percent more profit on average, respectively.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Parallel and Distributed Systems-
dc.subjectCloud computing-
dc.subjectcustomer perceived value-
dc.subjectdynamic pricing-
dc.subjectmultiserver configuration-
dc.subjectprofit maximization-
dc.subjectrisk-
dc.titleCustomer Perceived Value- And Risk-Aware Multiserver Configuration for Profit Maximization-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TPDS.2019.2960024-
dc.identifier.scopuseid_2-s2.0-85078476727-
dc.identifier.volume31-
dc.identifier.issue5-
dc.identifier.spage1074-
dc.identifier.epage1088-
dc.identifier.eissn1558-2183-
dc.identifier.isiWOS:000526526100006-

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