Availability Evaluation of Digital Library Cloud Services

Araújo, Julian; Maciel, Paulo; Torquato, Matheus; Callou, Gustavo; Andrade, Ermeson

doi:10.1109/dsn.2014.65

Cited by 27 publications

(9 citation statements)

References 18 publications

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“…In series configurations, if one component fails, the overall system will fail too. However, in parallel configuration, if only one component is working, the system will be operational [4].…”

Section: Reliability Block Diagrammentioning

confidence: 99%

How to Improve Cloud Services Availability? Investigating the Impact of Power and It Subsystems Failures

Rosendo

Leoni

Gomes

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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The cloud data center is a complex system composed of power, cooling, and IT subsystems. The power subsystem is crucial to feed the IT equipment. Power disruptions may result in service unavailability. This paper analyzes the impact of the power subsystem failures on IT services regarding different architecture configurations based on TIA-942 standard such as non-redundant, redundant, concurrently maintainable, and fault tolerant. We model both subsystems, power and IT, through Stochastic Petri Net (SPN). The availability results show that a fault tolerant power and IT configuration reduces the downtime from 54.1 to 34.5 hours/year when compared to a nonredundant architecture. The sensibility analysis results show that the failure and repair rates of the server component in a fault tolerant system present the highest impact on overall data center availability.

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Section: Reliability Block Diagrammentioning

confidence: 99%

How to Improve Cloud Services Availability? Investigating the Impact of Power and It Subsystems Failures

Rosendo

Leoni

Gomes

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…A Figura 2 representa o modelo SPN deste sistema modelado a partir de um conjunto composto por um módulo principal (PRI) e um módulo de backup (BKP), que inclui quatro lugares: PRI ON, PRI OFF, BKP ON, BKP OFF. Esses lugares representam os estados operacionais e os estados de falhas dos principais módulos respectivamente [25]. O módulo de backup é inicialmente desativado, e somente após a falha do módulo principal é que o módulo de backup é acionado.…”

Section: Redes De Petriunclassified

Stochastic Models for Optimizing Availability, Cost and Sustainability of Data Center Power Architectures through Genetic Algorithm

Austregésilo

Callou

2019

RITA

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In recent years, the growth of information technology has required higher reliability, accessibility, collaboration, availability, and a reduction of costs on data centers due to factors such as social network, cloud computing, and e-commerce. These systems require redundant mechanisms on the data center infrastrucutre to achieve high availability, which may increase the electric energy consumption, impacting in both the sustainability and cost. This work proposes a multi-objective optimization approach, based on Genetic Algorithms, to optimize cost, sustainability and availability of data center power infrastructures. The main goal is to maximize availability and minimize cost and exergy consumed (adopted to estimate the environmental impacts). In order to compute such metrics, this work adopts the energy flow model (EFM), reliability block diagrams (RBD) and stochastic petri nets (SPN). Two case studies are conducted to show the applicability of the proposed strategy: (i) takes into account 5 typical data center architectures that were optimized to conduct the validation process of the proposed strategy; (ii) uses the optimization strategy in two architectures classified by ANSI / TIA-942 (TIER I and II). In both case studies, significant improvements were achieved in the results, which were very close to the optimum one that was obtained by a brute force algorithm that analyzes all the possibilities and returns the optimal solution. It is worth mentioning that the time used to obtain the results using the genetic algorithm approach was significantly lower (6,763,260 times), in comparison with the strategy which combines all the possible combinations to obtain the optimal result. Resumo: Nos últimos anos, o crescimento da tecnologia da informação exigiu maior confiabilidade, acessibilidade, colaboração, disponibilidade e redução de custos nos data centers devido a fatores como redes sociais, computação em nuvem e comércio eletrônico. Esses sistemas exigem mecanismos redundantes na infraestrutura do data center para alcançar uma alta disponibilidade, o que pode aumentar o consumo de energia elétrica, impactando tanto na sustentabilidade quanto no custo. Este trabalho propõe uma abordagem de otimização multi-objetivo, baseada em Algoritmos Genéticos, para otimizar custos, sustentabilidade e disponibilidade de infraestruturas de energia em data centers. O principal objetivo é maximizar a disponibilidade e minimizar o custo e a exergia consumida (adotada para estimar os impactos ambientais). Para computar tais métricas, este trabalho adota o modelo de fluxo de energia (EFM), diagramas de blocos de confiabilidade (RBD) e redes de petri estocásticas (SPN). Dois estudos de caso são conduzidos: (i) leva em consideração 5 arquiteturas típicas de data centers para mostrar a aplicabilidade e validação da estratégia proposta; (ii) utiliza a estratégia de otimização em duas arquiteturas classificadas pela norma ANSI/TIA-942 (TIER I e II). Em ambos estudos de caso, observou-se uma melhora significativa nos resultados que fic...

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“…5). Those values were obtained from [2,8,20], and are used to compute dependability-related metrics for the sub-models, and then for the whole system. Table 3 shows the parameter values used for the submodel (Eq.…”

Section: Evaluation Of the Base Cloud Environmentmentioning

confidence: 99%

“…Table 3 shows the parameter values used for the submodel (Eq. 6), based on [2,8,20]. Table 4 shows the input parameters for the cold-standby SPN model based on [2,8,20].…”

Section: Evaluation Of the Base Cloud Environmentmentioning

confidence: 99%

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Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

et al. 2018

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Cloud computing is a paradigm that provides services through the Internet. The paradigm has been influenced by previously available technologies (for example cluster, peer-to-peer, and grid computing) and has now been adopted by almost all large organizations. Companies such as Google, Amazon, Microsoft and Facebook have made significant investments in cloud computing, and now provide services with high levels of dependability. The efficient and accurate assessment of cloud-based infrastructure is fundamental in guaranteeing both business continuity and uninterrupted public services, as much as is possible. This paper presents an approach for selecting cloud computing infrastructures, in terms of dependability and cost that best suits both company and customer needs. We use stochastic models to calculate dependability-related metrics for different cloud infrastructures. We then use a Multiple-Criteria Decision-Making (MCDM) method to rank the best cloud infrastructures, taking customer service constraints such as reliability, downtime, and cost into consideration. A case study demonstrates the practicability and usefulness of the proposed approach.

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Availability Evaluation of Digital Library Cloud Services

Cited by 27 publications

References 18 publications

How to Improve Cloud Services Availability? Investigating the Impact of Power and It Subsystems Failures

How to Improve Cloud Services Availability? Investigating the Impact of Power and It Subsystems Failures

Stochastic Models for Optimizing Availability, Cost and Sustainability of Data Center Power Architectures through Genetic Algorithm

Decision making in cloud environments: an approach based on multiple-criteria decision analysis and stochastic models

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