A Cost Model for IaaS Clouds Based on Virtual Machine Energy Consumption

Hinz, Mauro; Koslovski, Guilherme; Miers, Charles Christian; Pilla, Laércio Lima; Pillon, Maurı́cio A.

doi:10.1007/s10723-018-9440-8

Cited by 17 publications

(19 citation statements)

References 39 publications

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“…Table summarizes the services that can be individually used (or combined) to provide the domain name system, load balancing, QoS, virtual networking, security management, firewall, network address translation (NAT), VMs, and containers . Table also corroborates that different providers can offer similar services varying the fee approaches, levels of performance, and QoS indicator, as well as supporting technologies . Internally on IaaS cloud providers, the resource orchestration is performed by mechanisms optimized to manage computing, storage, and networking resources according to the services offered and objectives of the provider.…”

Section: Motivation and Problem Definitionmentioning

confidence: 58%

Cloud broker proposal based on multicriteria decision‐making and virtual infrastructure migration

et al. 2019

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Summary The adoption of infrastructure as a Service (IaaS) is a reality for academic, industrial, and governmental institutions. Cloud tenants request dynamically provisioned virtual infrastructures (VIs) tailored to their application requirements, detailing not only the virtual compute/storage resources but also the network components, topology, and services. The creation of a large number of cloud providers came along with the widespread use of VIs. The selection of an appropriate provider is a challenging task due to the diversity of the IaaS market and formally is a multicriteria analysis (NP‐hard). Notwithstanding the provider selection complexity, the mobility of VI‐hosted applications is limited due to the optimization anchors introduced by providers. Although the existing IaaS cloud brokers can indicate a hosting provider, they lack on conceptual and technical skills to migrate a VI and all its internal components between providers. This work enhances the state‐of‐the‐art on IaaS cloud brokerage by proposing virtual infrastructure multicriteria allocation and migration–based broker (VIMAM), which performs a multicriteria analysis of providers and VI migration. VIMAM is driven by an analytic hierarchy process (AHP) to select an IaaS provider, offering a set of predefined weighting schemas to represent distinct tenant perspectives. Moreover, to migrate a VI, VIMAM takes into account the virtual machines, containers, switches, and other topology elements. In addition to discussing the AHP ranking weights and frequency of providers selection, the experimental analysis details the implementation of an OpenStack and Docker–based prototype for VI migration.

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Section: Motivation and Problem Definitionmentioning

confidence: 58%

Cloud broker proposal based on multicriteria decision‐making and virtual infrastructure migration

et al. 2019

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“…Hinz et al develop a cost model reflecting the usage of the processing power [11]. Aldossary et al introduce a cloud system architecture and evaluate an energy-aware model that enables a fair attribution of a PM's energy consumption to homogeneous and heterogeneous VMs based on their utilization and size, which reflect the physical resource usage by each VM [12].…”

Section: Related Workmentioning

confidence: 99%

A Cost Estimation Model for Cloud Services and Applying to PC Laboratory Platforms

Cho

Bahn²

2020

Processes

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IaaS (Infrastructure as a Service) is a well-known computing service, which provides infrastructures over the cloud without owning real hardware resources. This is attractive as resources can be scaled up and down instantly according to the user's computing demands. Customers of such services would like to adjust the utilization policy promptly by considering the charge of the service, but an instantaneous response is not possible as it takes several hours or even a couple of days for cloud service providers to inform the billing information. In this article, we present an instant cost estimation model for estimating the cost of public cloud resources. Specifically, our model estimates the cost of IaaS by monitoring the usage of resources on behalf of virtual machine instances. As this is performed by generating a user-side metering daemon, it is very precise and thus similar to the resource usage evaluated by the cloud service provider. To validate our model, we run PC laboratory services for 50 students in two classes by making use of a public cloud during a semester. Experimental results show that the accuracy of our model is over 99.3% in comparison with the actual charge of the public cloud.

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“…The analysis of the Spot model showed that the same instances can be acquired on average by o ers close to 30% of the value paid when compared to the On-demand model, and with a greater availability, generally above of 90%. Hinz et al (2016) present a charging model called Virtual Power, which analyzes the individual quantity of energy consumed by each virtual machine, more speci cally the electric energy consumed by the processors, and compare it to the charging model of Amazon EC2. According to them, the cost models applied by the main IaaS providers do not consider the percentual of use, but only the quantity of processors allocated by each virtual machine, what does not represent the resources consumption of a data center.…”

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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A Cost Model for IaaS Clouds Based on Virtual Machine Energy Consumption

Cited by 17 publications

References 39 publications

Cloud broker proposal based on multicriteria decision‐making and virtual infrastructure migration

Cloud broker proposal based on multicriteria decision‐making and virtual infrastructure migration

A Cost Estimation Model for Cloud Services and Applying to PC Laboratory Platforms

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

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