Cloud Computing

Mastelić, Toni; Oleksiak, Ariel; Claußen, Holger; Brandić, Ivona; Pierson, Jean‐Marc; Vasilakos, Athanasios V.

doi:10.1145/2656204

Cited by 227 publications

(67 citation statements)

References 146 publications

(189 reference statements)

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“…[2,3,10,17,19,24,26]) and data center consolidation (e.g. [1,5,13,14,16]) and with respect to our previous results [4] we introduce the following novelties:…”

Section: Research Contributionmentioning

confidence: 99%

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Energy-aware auto-scaling algorithms for Cassandra virtual data centers

2017

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Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra virtual data centers (VDCs). This paper address the problem of energy efficient autoscaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: Opt, LocalOpt and LocalOpt-H. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. LocalOpt consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energyaware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely BestFit and BestFit-H. at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

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“…[2,3,10,17,19,24,26]) and data center consolidation (e.g. [1,5,13,14,16]) and with respect to our previous results [4] we introduce the following novelties:…”

Section: Research Contributionmentioning

confidence: 99%

“…Many ways of reducing the power consumption in cloud systems have been proposed the literature; two interesting survey are [24] and [19]. Different approaches can be used for the sustainable operation of data centers.…”

Section: Power Consumption Modelmentioning

confidence: 99%

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

2017

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“…A Computação em Nuvem pode ser compreendida como o conjunto de aplicações entregues como um serviço pela internet, onde fisicamente servidores e aplicações estão alocados em datacenters terceirizados, provendo serviços sobre demanda [9], em regiões geograficamente distribuídas.…”

Section: Computação Em Nuvemunclassified

A Utilização Do Posicionamento De Máquinas Virtuais Para a Qualidade De Aplicações De Clientes Em Nuvem

Jesus¹,

Kamienski²

2017

SADSJ

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ResumoCom a evolução das tecnologias de virtualização, das redes e dos hipervisores de computação, o compartilhamento de recursos nas atuais infraestruturas como serviços, tornou-se mais flexível. Toda a implementação de uma nuvem, depende de desempenho e padronizações para clientes; tendo em vista que o ambiente é compartilhado, todos querem obter o melhor desempenho. As qualidades de serviços são definidas, protocolos são padronizados, e há a preocupação com a segurança entre os clientes, em que são criadas quotas de recursos e, na teoria, a plataforma está preparada para a demanda. Porém o desempenho para os clientes varia e, em pouco tempo, a degradação dos recursos torna-se uma barreira para o uso eficiente da infraestrutura da nuvem. O comprometimento da escalabilidade e da disponibilidade, acontece pela falta de uma estratégia para a heterogeneidade de hardware, variações dos fatores de rede, computação e cargas de trabalho, que são as características de um ambiente compartilhado, sendo necessária uma tecnologia capaz de satisfazer de maneira automatizada das regras propostas pelo modelo da nuvem. Estas características são estudadas neste artigo, assim como os conceitos chaves da computação em nuvem, as redes virtuais e os algoritmos de placement. Palavras-Chaves: Computação em Nuvem; Posicionamento de Máquinas Virtuais;OpenStack; Virtualização; Avaliação de Desempenho.

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“…In recent years, VM consolidation has been studied intensively (cf., Ref. [15] for a survey of this research area). The idea behind VM consolidation is to gather VM instances into few physical machines, enable other machines to be turned off, and thus save on energy.…”

Section: Related Workmentioning

confidence: 99%

A Game Theoretic Approach to Power Reduction in Distributed Storage Systems

Hasebe

Sawada²,

Kato

2016

Journal of Information Processing

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We present a game theoretic approach for power reduction in large-scale distributed storage systems. The key idea is to use a distributed hash table and migrate its virtual nodes dynamically so as to skew the workload towards a subset of physical disks while not overloading them. To realize this idea in an autonomous way, virtual nodes are regarded as selfish agents playing a game in which each node receives a payoff according to the workload of the disk on which it currently resides. We model this setting as a potential game, a kind of strategic game in which the incentive of all players to change their strategy can be represented by a single global function. Thus, any increase in the payoff of a virtual node yields a better state in terms of energy conservation. This game model consists of a pair of global and private payoff functions, derived by the Wonderful Life Utility scheme. The former function evaluates how good the current state of the system is, while the latter determines the current payoff of each node. The performance of our method is measured both by simulations and a prototype implementation. From the experiments, we observed that our method consumed 11.1%-16.4% less energy than the static configuration. In addition, although a small number of responses were heavily delayed because of overloading of some disks at peak time, our method maintained preferred overall average response time in the range 50-190 ms.

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Cloud Computing

Cited by 227 publications

References 146 publications

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

Energy-aware auto-scaling algorithms for Cassandra virtual data centers

A Utilização Do Posicionamento De Máquinas Virtuais Para a Qualidade De Aplicações De Clientes Em Nuvem

A Game Theoretic Approach to Power Reduction in Distributed Storage Systems

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