Scale-up x Scale-out: A Case Study using Nutch/Lucene

Michael, M.; Moreira, José E.; Shiloach, Doron; Wisniewski, Robert W.

doi:10.1109/ipdps.2007.370631

Cited by 108 publications

(55 citation statements)

References 6 publications

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“…It would be also interesting to identify circumstances under which one scaling type is more suitable than other based on some key criteria from the cloud consumer's perspective. Michael et al [33] carried out such an experimental case study to evaluate performance/price scale out and scale up but on dedicated servers, IBM's specific clusters, and specific to Nutch/Lucene framework for implementing search applications. However, it would be of key interest to conduct a similar study using on-demand cloud infrastructure offerings and on other application domains, e.g., e-business applications, and development framework, e.g., LAMP (Linux, Apache, MySQL, and Perl/PHP/Python).…”

Section: How To Scale? Which Scaling Strategy?mentioning

confidence: 99%

On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure

Suleiman

Sakr

Jeffery

et al. 2011

J Internet Serv Appl

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The exposure of business applications to the web has considerably increased the variability of its workload patterns and volumes as the number of users/customers often grows and shrinks at various rates and times. Such application characteristics have increasingly demanded the need for flexible yet inexpensive computing infrastructure to accommodate variable workloads. The on-demand and per-use cloud computing model, specifically that of public Cloud Infrastructure Service Offerings (CISOs), has quickly evolved and adopted by majority of hardware and software computing companies with the promise of provisioning utility-like computing resources at massive economies of scale. However, deploying business applications on public cloud infrastructure does not lead to achieving desired economics and elasticity gains, and some challenges block the way for realizing its real benefits. These challenges are due to multiple differences between CISOs and application's requirements and characteristics. This article introduces a detailed analysis and discussion of the economics and elasticity challenges of business applications to be deployed and operate on public cloud infrastructure. This includes analysis of various aspects of public CISOs, modeling and measuring CISOs' economics and elasticity, application workload patterns and its impact on achieving elasticity and economics, economics-driven elasticity decisions and policies, and SLA-driven monitoring and elasticity of cloud-based business applications. The analysis and discussion are supported with motivating scenarios for cloud-based business applications. The paper provides a multi-lenses overview that can help cloud consumers and potential business application's owners to understand, analyze, and evaluate important economics and elasticity capabilities of different CISOs and its suitability for meeting their business application's requirements.

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Section: How To Scale? Which Scaling Strategy?mentioning

confidence: 99%

On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure

Suleiman

Sakr

Jeffery

et al. 2011

J Internet Serv Appl

View full text Add to dashboard Cite

show abstract

“…Nutch is particularly well suited for scaling out with a large number of commodity hardware [32,33].…”

Section: Building An Inverted Indexmentioning

confidence: 99%

Accelerating text mining workloads in a MapReduce-based distributed GPU environment

Wittek

Daranyi

2013

Journal of Parallel and Distributed Computing

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Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data-intensive, and the ease of deployment of algorithms is an important factor in developing advanced applications, we introduce a flexible, distributed, MapReducebased text mining workflow that performs I/O-bound operations on CPUs with industry-standard tools and then runs compute-bound operations on GPUs which are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050 attached to each, and we achieve considerable speedups for random projection and self-organizing maps.

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“…A escalabilidade vertical é obtida através do incremento de mais recursos computacionais (núcleos, memória, processamento) em um servidor original, incrementando assim o poder de processamento [56] [38]. Já escalabilidade horizontal é obtida através do incremento de um grande número de servidores menores e mais baratos, fortemente interconectados em…”

Section: Introductionunclassified

“…Mineração em Grandes Massas de Dados Utilizando Hadoop MapReduce e Algoritmos Bioinspirados: Uma Revisão Sistemática formato de cluster [56] [38]. A escolha desse tipo de escalabilidade vem crescendo pois é mais acessível e possui melhor desempenho de processamento em relação à escalabilidade vertical [56].…”

Section: Introductionunclassified

Mineração em Grandes Massas de Dados Utilizando Hadoop MapReduce e Algoritmos Bio-inspirados: Uma Revisão Sistemática

Menezes

Freitas

Parpinelli

2016

RITA

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Resumo: A Área de Mineração de Dados tem sido utilizada em diversas áreasde aplicação e visa extrair conhecimento através da análise de dados. Nas últimas décadas, inúmeras bases de dados estão tendenciando a possuir grande volume, alta velocidade de crescimento e grande variedade. Esse fenômeno é conhecido como Big Data e corresponde a novos desafios para tecnologias clássicas como Sistema de Gestão de Banco de Dados Relacional pois não tem oferecido desempenho satisfatório e escalabilidade para aplicações do tipo Big Data. Ao contrário dessas tecnologias, Hadoop MapReduce é um framework que, além de provêr processamento paralelo, também fornece tolerância a falhas e fácil escalabilidade sobre um sistema de armazenamento distribuído compatível com cenário Big Data. Uma das técnicas que vem sendo utilizada no contexto Big Data são algoritmos bio-inspirados. Esses algoritmos são boas opções de solução em problemas complexos multidimensionais, multiobjetivos e de grande escala. A combinação de sistemas baseados em Hadoop MapReduce e algoritmos bio-inspirados tem se mostrado vantajoso em aplicações Big Data. Esse artigo apresenta uma revisão sistemática de trabalhos nesse contexto, visando analisar critérios como: tarefas de mineração de dados abordadas, algoritmos bio-inspirados utilizados, disponibilidade das bases utilizadas e quais características Big Data são tratadas nos trabalhos. Como resultado, esse artigo discute os critérios analisados e identifica alguns modelos de paralelização, além de sugerir uma direção para trabalhos futuros.

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Scale-up x Scale-out: A Case Study using Nutch/Lucene

Abstract: Scale-up

Cited by 108 publications

References 6 publications

On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure

On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure

Accelerating text mining workloads in a MapReduce-based distributed GPU environment

Mineração em Grandes Massas de Dados Utilizando Hadoop MapReduce e Algoritmos Bio-inspirados: Uma Revisão Sistemática

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