Pricing cloud IaaS services based on a hedonic price index

Mitropoulou, Persefoni; Filiopoulou, Evangelia; Michalakelis, Christos; Νικολαϊδου, Μάρα

doi:10.1007/s00607-016-0493-x

Cited by 17 publications

(11 citation statements)

References 23 publications

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“…Nevertheless, they initiated the hedonic model for further study of cloud prices. Mitropoulou et al [41] [45] provide a hedonic price index for cloud price comparison purposes among 23 CSPs.…”

Section: Hedonic Model For Cloud Pricementioning

confidence: 99%

Hedonic Pricing of Cloud Computing Services

Toosi

Buyya

et al. 2021

IEEE Trans. Cloud Comput.

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Cloud service providers (CSP) and cloud consumers often demand to forecast the cloud price in order to optimize their business strategy. However, pricing of cloud services is a challenging task due to its services complexity and dynamic nature of the ever-changing environment. Moreover, the cloud pricing based on consumers' willingness to pay (W2P) becomes even more challenging due to the subjectiveness of consumers' experiences and implicit values of some non-marketable prices, such as burstable CPU, dedicated server, and cloud data center global footprints. Unfortunately, many existing pricing models often cannot support value-based pricing. In this paper, we propose a novel solution based on value-based pricing, which does not only consider how much does the service cost (or intrinsic values) to a CSP but also how much customer is willing to pay (or extrinsic values) for the service. We demonstrate that the cloud extrinsic values would not only become one of the competitive advantages for CSPs to lead the cloud market but also increase the profit margin. Our approach is often referred to as a hedonic pricing model. We show that our model can capture the value of non-marketable price. This value is about 43.4% on average above the baseline, which is often ignored by many traditional cloud pricing models. We also show that Average Annual Growth Rate (AAGR) of Amazon Web Services' (AWS) is about -20.0% per annum between 2008 and 2017, ceteris paribus. In comparison with Moore's law (-50% per annum), it is at a far slower pace. We argue this value is Moore's law equivalent in the cloud. The primary goal of this research is to provide a less biased pricing model for cloud decision makers to develop their optimizing investment strategy.

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Section: Hedonic Model For Cloud Pricementioning

confidence: 99%

Hedonic Pricing of Cloud Computing Services

Toosi

Buyya

et al. 2021

IEEE Trans. Cloud Comput.

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“…Special attention must be also paid to works related to the derivation of metrics that assess different features of IT solutions, particularly to those dimensions used by our methodology as design criteria, that is, cost, reliability, and computing capacity. To begin with, two recent contributions lie at the baseline on which our approach stands: Mazrekaj, Shabani, and Sejdiu (2016), which thoroughly reviews pricing models from different providers and overviews existing related works; and Mitropoulou, Filiopoulou, Michalakelis, and Nikolaidou (2016), where a hedonic multidimensional price index is proposed for IaaS infrastructure pricing based on different characteristics of the deployed services and other factors alike. Similarly, the community has also focused on the trade-off between redundancy/scheduling or planning/reliability of IaaS systems (Gupta, Goyal, Kumar, & Aggarwal, 2013;Li et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

A heuristic approach to the multicriteria design of IaaS cloud infrastructures for Big Data applications

et al. 2018

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The rapid growth of new computing paradigms such as Cloud Computing and Big Data has unleashed great opportunities for companies to shift their business model towards a fully digital strategy. A major obstacle in this matter is the requirement of highly specialized ICT infrastructures that are expensive and difficult to manage. It is at this point that the IaaS (infrastructure as a service) model offers an efficient and cost‐affordable solution to supply companies with their required computing resources. In the Big Data context, it is often a hard task to design an optimal IaaS solution that meets user requirements. In this context, we propose a methodology to optimize the definition of IaaS cloud models for hosting Big Data platforms, following a threefold criterion: cost, reliability, and computing capacity. Specifically, the proposed methodology hinges on evolutionary heuristics in order to find IaaS configurations in the cloud that optimally balance such objectives. We also define measures to quantify the aforementioned metrics over a Big Data platform hosted within an IaaS cloud model. The proposed method is validated by using real information from three IaaS providers and three Big Data platforms. The obtained results provide an insightful input for system managers when initially designing cloud infrastructures for Big Data applications.

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“…The linear regression as a price modeling tool was also used in the researches of El Kihal et al (2012) and Mitropoulou et al (2016Mitropoulou et al ( , 2017. The rst uses it to analyze the prices of only one provider, hindering a Cross-Provider comparison, that is, a comparison capable of analyzing more than one provider in the same proposal (Hernández et al;.…”

Section: Related Workmentioning

confidence: 99%

A proposal of Infrastructure-as-a-Service providers pricing model using linear regression

Belusso

Sawicki

Basto‐Fernandes

et al. 2018

RBCA

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The increasing demand for companies to reduce the IT infrastructure (on-premise) are driving the adoption of a type of cloud computing category known as Infrastructure-as-a-Service (IaaS) to provide virtualized computing resources over the Internet. However, the choice of an instance of virtual machine whose con guration is able to meet the demands of the company is a complex task, especially concerning the price charged by providers. The lack of transparency of the mechanism of de nition of the prices adopted by providers makes di cult the decision-making process, considering the in uence of several factors on the nal price of the instances, among them the geographical location of the data center. In view of this problem, this work presents a new proposal of price modeling of instances using multiple linear regression model, including the geographical location of the data center as one of variables of the model. To verify the accuracy of the regression model proposed, the calculated prices were compared to real prices charged by IaaS providers Amazon EC2, Google Cloud Platform e Microsoft Azure. ResumoA crescente demanda por parte das empresas para reduzir a infraestrutura de TI local está impulsionando a adoção de um tipo de categoria de computação em nuvem conhecida como Infraestrutura-como-um-Serviço (IaaS) para fornecer recursos de computação virtualizados pela Internet. No entanto, a escolha de uma instância de máquina virtual cuja con guração seja capaz de atender às demandas da empresa é uma tarefa complexa, especialmente no que se refere ao preço cobrado pelos provedores. A falta de transparência do mecanismo de de nição dos preços adotados pelos provedores torna difícil o processo de tomada de decisão, considerando a in uência de vários fatores sobre o preço nal das instâncias, dentre eles a localização geográ ca do data center. Diante deste problema, este trabalho apresenta uma nova proposta de modelagem de preços de instâncias usando o modelo de regressão linear múltipla, incluindo a localização geográ ca do data center como uma das variáveis do modelo. Para veri car a precisão do modelo de regressão proposto, os preços calculados foram comparados aos preços reais cobrados pelos provedores de IaaS Amazon EC2, Google Cloud Platform e Microsoft Azure. Palavras-Chave:Computação em Nuvem; Infraestrutura-como-um-Serviço; Integração de Aplicações Empresariais; Modelo de Preços; Regressão Linear 44 Belusso et al. / Revista Brasileira de Computação Aplicada (2018), v.10, n.2, pp.44-53 | 45

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Pricing cloud IaaS services based on a hedonic price index

Cited by 17 publications

References 23 publications

Hedonic Pricing of Cloud Computing Services

Hedonic Pricing of Cloud Computing Services

A heuristic approach to the multicriteria design of IaaS cloud infrastructures for Big Data applications

A proposal of Infrastructure-as-a-Service providers pricing model using linear regression

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