A Price-Incentive Resource Auction Mechanism Balancing the Interests Between Users and Cloud Service Provider

被引:17
作者
Li, Songyuan [1 ]
Huang, Jiwei [2 ]
Cheng, Bo [1 ]
机构
[1] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[2] China Univ Petr, Beijing Key Lab Petr Data Min, Beijing 102249, Peoples R China
来源
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT | 2021年 / 18卷 / 02期
基金
北京市自然科学基金; 中国国家自然科学基金;
关键词
Cloud computing; resource management; market-based pricing; auction mechanism; ELECTRICITY PRICE; ALLOCATION;
D O I
10.1109/TNSM.2020.3036989
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
For a cloud service provider, it necessitates an emerging cloud ecosystem to consolidate the existing users and attract more potential users, further gaining its market share. Therefore, in this article, we design a price-incentive resource auction mechanism in cloud environment. In response to the cloud resource price, each user synthesizes her bidding budget and QoS requirement, and purchases cloud resources according to her resource demand in a strategic manner. The cloud service provider, meanwhile, can regulate the resource demands of users through conducting a market-based pricing strategy, against too low prices to cover the operational costs (i.e., energy costs) or too high prices resulting in user churn. In virtue of an elaborate market-based pricing strategy, the interests of users and the cloud service provider are balanced. Our price-incentive resource auction mechanism targets to stimulate maximum users willing to purchase resources and perform their applications at the cloud, on the premise of a minimum profit rate guaranteed for the cloud service provider. It is also able to provide budge balance and truthfulness guarantee, and satisfy the envy-freeness. In order to carry out the above objectives, we carefully design the user utility function reflecting the complicated user interest, and formulate our resource pricing and auction problem as a bin packing problem, which has non-polynomial computational complexity. Regarding the NP-hardness of optimization problem and the concavity of user utility, we present a computational-efficient (1 + epsilon)-approximate algorithm namely PIRA. Finally, we conduct simulations based on the real-world dataset to validate the effectiveness of our proposed approach.
引用
收藏
页码:2030 / 2045
页数:16
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