Resource Pricing and Demand Allocation for Revenue Maximization in IaaS Clouds: A Market-Oriented Approach
Resource Pricing and Demand Allocation for Revenue Maximization in IaaS Clouds: A Market-Oriented Approach
复制标题
IaaS云收入最大化的资源定价和需求分配：面向市场的方法
DOI：
10.1109/tnsm.2021.3085519
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发表时间：
2021-09
影响因子：
5.3
通讯作者：
Songyuan Li;Jiwei Huang;Bo Cheng
中科院分区：
文献类型：
--
作者：
Songyuan Li;Jiwei Huang;Bo Cheng
随着越来越多的用户将他们的应用程序外包到云端，资源定价成为IaaS云管理的一个重要问题。综合考虑自己的招标预算和云资源的价格，每个用户根据自己的资源需求，自我激励地购买云资源，使自己的效用最大化。同时，云服务提供商（CSP）对云资源的价格进行调控，以达到一定的盈利目标。通过精心设计的资源定价策略，用户和CSP的目标是平衡的，并在一定程度上满足各自的目标。本文将深入了解以市场为导向的云定价策略。具体而言，我们提出了IaaS云中的拍卖市场，多个具有异构竞标预算和QoS需求的用户根据其资源需求订阅云资源。以收益最大化为目标的资源定价和需求分配方案还满足预算可行性、激励兼容性和无嫉妒性等基本属性。为了解决收益最大化问题的np -硬度和非凸性问题，我们设计了一种价格激励的资源拍卖机制RARM，该机制对收益最大化保持（$1+\alpha $）近似比。最后，我们基于真实数据集评估了我们的RARM机制，以证明我们提出的方法的有效性。
With more users outsourcing their applications to the cloud, resource pricing becomes an important issue for IaaS cloud management. Jointly considering her own bidding budget and the price of cloud resources, each user is self-motivated to purchase cloud resources according to her resource demand which maximizes her own utility. Meanwhile, the cloud service provider (CSP) regulates the price of cloud resources with a certain profitability objective achieved. With an elaborate resource pricing strategy, the goals from users and the CSP are balanced and respectively satisfied to some extent. This article provides an insight into the market-oriented cloud pricing strategy. In specific, we propose an auction market in the IaaS cloud, where multiple users with heterogeneous bidding budgets and QoS requirements subscribe cloud resources according to their resource demands. The resource pricing and demand allocation scheme targeting revenue maximization also satisfies essential properties including budget feasibility, incentive compatibility and envy-freeness. To attack the NP-hardness and non-convexity of revenue maximization problem, we design a price-incentive resource auction mechanism namely RARM, which preserves an ( $1+\alpha $ ) approximation ratio on revenue maximization. Finally, we evaluate our RARM mechanism based on the real-world dataset to certify the efficacy of our proposed approach.