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Determining bucket structures in pricing plans for private user cloud computing storage services: A Monte Carlo simulation study

机译:确定私有用户云计算存储服务定价计划中的存储桶结构:蒙特卡洛模拟研究

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Purpose - Providers of cloud computing storage services (CCSS) charge offers in several unit bundles for a lump sum per bundle. This non-linear pricing approach is known as a bucket-pricing plan (BPP). If a customer exploits the purchased bucket, he/she can opt for the next higher bucket or refrain from further CCSS use. CCSS suppliers are faced with an optimization problem concerning the number of buckets as well as their lower and upper storage volume boundaries. The purpose of this paper is to develop a model, which supports CCSS suppliers in deriving a BPP-structure and which maximizes their profit in varying market constellations. Design/methodology/approach The authors develop a multi-period model of tariff choice decisions of private customers of CCSS. The model is applied in Monte Carlo simulations to determine profit-maximal tariff structures as a function of different market characteristics such as median demand saturation, demand heterogeneity, average price per storage unit and bucket ceiling allocation (identical size of each bucket within the frame set by the lower and upper overall boundary, varying sizes of the buckets offered, so that the interval between two ceilings consecutively increases for subsequent buckets) and type of a customer's utility function. Findings - The simulation analysis suggests that demand heterogeneity and average price per unit are the most influential factors for CCSS tariff structure optimization. Price plans with more than two buckets tend to generate higher profits than simple schemes with two buckets only if demand heterogeneity is low and the average price per storage unit is high and/or median saturation level of customers is low. Originality/value - Despite the popularity of BPP among providers of CCSS for consumers, there is a lack of scholarly modeling work on the profit implications of the number of buckets entailed in a scheme and the size/ceilings of the various buckets on offer. The model suggested in this paper is a first step toward narrowing this research gap.
机译:目的-云计算存储服务(CCSS)的提供者以几个单位捆绑包的价格提供服务,每个捆绑包一次付清。这种非线性定价方法称为存储桶定价计划(BPP)。如果客户利用购买的存储桶,则他/她可以选择下一个更高的存储桶,或避免进一步使用CCSS。 CCSS供应商面临有关存储桶数量及其上下存储空间边界的优化问题。本文的目的是开发一个模型,该模型支持CCSS供应商推导BPP结构,并在不同的市场格局中最大化其利润。设计/方法/方法作者开发了CCSS私人客户关税选择决策的多阶段模型。该模型在蒙特卡洛模拟中应用,根据不同的市场特征(例如中位数需求饱和度,需求异质性,每个存储单元的平均价格和存储桶上限分配(框架集内每个存储桶的相同大小))确定利润最大的关税结构通过上下整体边界,提供的铲斗尺寸会有所不同,以便随后两个铲斗的两个天花板之间的间隔连续增大)和客户的实用功能类型。研究结果-模拟分析表明,需求异质性和每单位平均价格是CCSS关税结构优化的最主要影响因素。仅在需求异质性低且每个存储单元的平均价格高和/或客户的中位饱和度低的情况下,具有两个以上存储桶的价格计划往往会比具有两个存储桶的简单计划产生更高的利润。原创性/价值-尽管BPP在CCSS的提供者中为消费者所欢迎,但是仍然缺乏关于方案中所包含的桶数以及所提供的各个桶的大小/上限的利润影响的学术建模工作。本文提出的模型是缩小研究差距的第一步。

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