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An online auction mechanism for cloud computing resource allocation and pricing based on user evaluation and cost

机译:基于用户评估和成本的云计算资源分配和定价在线拍卖机制

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摘要

Auction-based virtual resource allocation is a major problem for cloud computing. However, previous studies have typically equated virtual resources with ordinary items and designs corresponding to specific auction mechanisms, which cannot truly reflect the characteristics of the virtual resource. This inability to reflect the virtual resource characteristics arises because the users have only the right to use resources without ownership, the use of virtual resources has significant impacts on the social welfare and revenue of the resource provider, and the virtual resource usage will generate operating costs. Considering the above issues, this work proposes a truthful online auction mechanism based on user evaluation and cost and applies it to the allocation and pricing of cloud computing virtual resources. Based on a pay-as-you-go model, users can submit multiple requirements at any time, but only one requirement can be satisfied, which is known as a multi-requirement, single-minded scenario. We prove that the resource providers can obtain increased social welfare and guarantee that the mechanism is truthful. For the resource allocation problem, we propose a heuristic algorithm to obtain the allocation result quickly and to maximize the social welfare of the cloud resource provider. The payment algorithm accounts for the interests of both the users and resource provider based on the dichotomy. We experimentally analyze the solution in terms of social welfare, execution time, resource utilization and users served.
机译:基于拍卖的虚拟资源分配是云计算的主要问题。但是,先前的研究通常将虚拟资源与对应于特定拍卖机制的普通物品和设计等同起来,这不能真正反映虚拟资源的特征。之所以无法反映虚拟资源的特性,是因为用户仅有权使用资源而无所有权,虚拟资源的使用会对资源提供者的社会福利和收入产生重大影响,并且虚拟资源的使用将产生运营成本。考虑到上述问题,本文提出了一种基于用户评价和成本的真实在线拍卖机制,并将其应用于云计算虚拟资源的分配和定价。基于现收现付模型,用户可以随时提交多个需求,但只能满足一个需求,这被称为多需求,一心一意的方案。我们证明资源提供者可以获得增加的社会福利,并保证该机制是真实的。对于资源分配问题,我们提出了一种启发式算法,可以快速获得分配结果,并最大限度地提高云资源提供商的社会福利。支付算法基于二分法考虑了用户和资源提供者的利益。我们从社会福利,执行时间,资源利用和所服务的用户方面对实验解决方案进行了分析。

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