No Windows baixe o programa UNetbootin (http://unetbootin.sourceforge.net/).
Ele é um executável direto, ou seja, não precisa de instalação.
Baixe a ISO de alguma distribuição LINUX. Eu baixei a do UBUNTU 11.04.
Selecione a opção de utilizar uma ISO (DISKIMAGE) e informe o caminho.
Selecione o pendrive, que deve ter pelo menos 1GB e formatado.
Dê OK e espere.
Após o término, se você quiser instalar o LINUX ou só testar, reinicialize seu computador e altere a opção de boot no setup para que o primeiro dispositivo seja o pendrive.
Assim, se tudo der certo, aparecerá uma tela com opções para executar o LINUX a partir do pendrive ou para instalar.
É o blog do Emanuel!!!
Aqui serão postados assuntos diversos: viagens, informática e tecnologia de uma forma geral.
Como sou professor e trabalho com Computação em Nuvens (Cloud Computing), muitas vezes os posts serão sobre assuntos relacionados ao mundo acadêmico, como congressos. Mas tem de tudo um pouco!
sábado, 17 de dezembro de 2011
Instalando o JAVA no UBUNTU
Uso o UBUNTU 11.04.
Em uma tela do terminal siga os passos abaixo.
Baixar o java: jdk-7u2-linux-i586.tar.gz
Descompactar em alguma pasta: tar -zxvf jdk-7u2-linux-i586.tar.gz
É criada a pasta jdk1.7.0_02
Descubra o caminho completo do diretório do java: pwd
No meu caso aparece: /home/emanuel/jdk1.7.0_02
Ajustando as variáveis:
editar o /etc/bash.bashrc
sudo /etc/bash.bashrc
Adiciona no final:
PATH=$PATH:/home/emanuel/jdk1.7.0_02/bin
export
JAVA_HOME=/home/emanuel/jdk1.7.0_02
export JAVA_HOME
Em uma tela do terminal siga os passos abaixo.
Baixar o java: jdk-7u2-linux-i586.tar.gz
Descompactar em alguma pasta: tar -zxvf jdk-7u2-linux-i586.tar.gz
É criada a pasta jdk1.7.0_02
Descubra o caminho completo do diretório do java: pwd
No meu caso aparece: /home/emanuel/jdk1.7.0_02
Ajustando as variáveis:
editar o /etc/bash.bashrc
sudo /etc/bash.bashrc
Adiciona no final:
PATH=$PATH:/home/emanuel/jdk1.7.0_02/bin
export
JAVA_HOME=/home/emanuel/jdk1.7.0_02
export JAVA_HOME
Saia do terminal (exit) e abra um novo. Para testar as variáveis, os diretórios devem aparecer quando você digitar $PATH e $JAVA_HOME
Para testar o java, basta digitar qualquer comando do java, como java ou javac, e as opções devem surgir na tela.
terça-feira, 15 de novembro de 2011
Instâncias da Amazon EC2 (no dia 15/11/2011)
A Amazon EC2 em 15/11/2011 possui as seguintes instâncias disponíveis:
Fonte: http://aws.amazon.com/ec2/instance-types/
Fonte: http://aws.amazon.com/ec2/instance-types/
Standard Instances
Instances of this family are well suited for most applications.
Small Instance – default
*
1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small
Large Instance
7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large
Extra Large Instance
15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge
Micro Instances
Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.
Micro Instance
613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro
High-Memory Instances>
Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.
High-Memory Extra Large Instance
17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge
High-Memory Double Extra Large Instance
34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge
High-Memory Quadruple Extra Large Instance
68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge
High-CPU Instances
Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.
High-CPU Medium Instance
1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium
High-CPU Extra Large Instance
7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge
Cluster Compute Instances
Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.
Cluster Compute Quadruple Extra Large Instance
23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge
Cluster Compute Eight Extra Large Instance
62 GB of memory
88 EC2 Compute Units (eight-core 2 x Intel Xeon)
3370 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc2.8xlarge
88 EC2 Compute Units (eight-core 2 x Intel Xeon)
3370 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc2.8xlarge
Cluster GPU Instances
Instances of this family provide general-purpose graphics processing units (GPUs) with proportionally high CPU and increased network performance for applications benefitting from highly parallelized processing, including HPC, rendering and media processing applications. While Cluster Compute Instances provide the ability to create clusters of instances connected by a low latency, high throughput network, Cluster GPU Instances provide an additional option for applications that can benefit from the efficiency gains of the parallel computing power of GPUs over what can be achieved with traditional processors. Learn more about use of this instance type for HPC applications.
Cluster GPU Quadruple Extra Large Instance
22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge
domingo, 18 de setembro de 2011
Revisões de Literatura
Estou pagando uma disciplina de Análise de Desempenho e acabei de começar umas revisões de literatura. Os artigos selecionados foram:
Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
Alexandru Iosup, Simon Ostermann, M. Nezih Yigitbasi, Radu Prodan, Thomas Fahringer, and Dick H.J. Epema
Abstract—Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining
expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds
serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their
owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel
production environments. However, the current commercial clouds have been built to support web and small database workloads,
which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may
introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the
performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing
workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to
achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing
services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the
performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific
computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be
useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer
and demand.
Index Terms—Distributed systems, distributed applications, performance evaluation, metrics/measurement, performance measures
The Impact of Virtualization on Network Performance of Amazon EC2 Data Center
Guohui Wang T. S. Eugene Ng
Abstract —Cloud computing services allow users to lease computing resources from large scale data centers operated by service
providers. Using cloud services, users can deploy a wide variety of applications dynamically and on-demand. Most cloud service
providers use machine virtualization to provide flexible and cost effective resource sharing. However, few studies have investigated
the impact of machine virtualization in the cloud on networking
performance. In this paper, we present a measurement study to characterize the impact of virtualization on the networking performance of the
Amazon Elastic Cloud Computing (EC2) data center. We measure the processor sharing, packet delay, TCP/UDP throughput and
packet loss among Amazon EC2 virtual machines. Our results show that even though the data center network is lightly utilized,
virtualization can still cause significant throughput instability and abnormal delay variations. We discuss the implications of our
findings on several classes of applications.
Index Terms —Measurement, cloud service, virtualization, networking performance
Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
Alexandru Iosup, Simon Ostermann, M. Nezih Yigitbasi, Radu Prodan, Thomas Fahringer, and Dick H.J. Epema
Abstract—Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining
expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds
serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their
owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel
production environments. However, the current commercial clouds have been built to support web and small database workloads,
which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may
introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the
performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing
workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to
achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing
services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the
performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific
computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be
useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer
and demand.
Index Terms—Distributed systems, distributed applications, performance evaluation, metrics/measurement, performance measures
The Impact of Virtualization on Network Performance of Amazon EC2 Data Center
Guohui Wang T. S. Eugene Ng
Abstract —Cloud computing services allow users to lease computing resources from large scale data centers operated by service
providers. Using cloud services, users can deploy a wide variety of applications dynamically and on-demand. Most cloud service
providers use machine virtualization to provide flexible and cost effective resource sharing. However, few studies have investigated
the impact of machine virtualization in the cloud on networking
performance. In this paper, we present a measurement study to characterize the impact of virtualization on the networking performance of the
Amazon Elastic Cloud Computing (EC2) data center. We measure the processor sharing, packet delay, TCP/UDP throughput and
packet loss among Amazon EC2 virtual machines. Our results show that even though the data center network is lightly utilized,
virtualization can still cause significant throughput instability and abnormal delay variations. We discuss the implications of our
findings on several classes of applications.
Index Terms —Measurement, cloud service, virtualization, networking performance
Weka 3: Data Mining Software in Java
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
http://www.cs.waikato.ac.nz/ml/weka/
http://www.cs.waikato.ac.nz/ml/weka/
Maximizando a tela do UBUNTU no VirtualBox
Um problema que sempre tenho é como maximizar a tela do UBUNTU quando utilizando o VirtualBox (pra ficar com a tela toda preenchida, sem os ícones lá de cima).
Pesquisando pela internet achei isso, que resolveu o meu problema:
1 ) No Virtual Box, quando estiver com uma máquina virtual iniciada, clica-se no menu "Dispositivos", no item "Instalar adicionais de convidados".
2 ) Já no UBUNTU, abra um terminal e digite: /media/VBOXADDITIONS_4.1.2_73507, ou algo parecido em /media.
3 ) sudo ./VBoxLinuxAdditions-x86.run
Pesquisando pela internet achei isso, que resolveu o meu problema:
1 ) No Virtual Box, quando estiver com uma máquina virtual iniciada, clica-se no menu "Dispositivos", no item "Instalar adicionais de convidados".
2 ) Já no UBUNTU, abra um terminal e digite: /media/VBOXADDITIONS_4.1.2_73507, ou algo parecido em /media.
3 ) sudo ./VBoxLinuxAdditions-x86.run
sexta-feira, 16 de setembro de 2011
MINIPLOP 2011 - Requisitos de Qualidade: Um Padrão para Identificação de Requisitos Não Funcionais Conflitantes
Em 24/09/2011 (domingo mesmo!) estarei em São Paulo no MINIPLOP 2011 (http://www.miniplop.ita.br/TheIntroduction.htm) apresentando o artigo "Requisitos de Qualidade: Um Padrão para Identificação de Requisitos Não Funcionais Conflitantes".
Resumo. O desenvolvimento de software é uma atividade que trabalha diversos aspectos da Engenharia de Software. Um deles é a elicitação de requisitos e sua escrita em uma notação de fácil entendimento para os desenvolvedores de software. Requisitos Não Funcionais são aqueles que estão associados aos aspectos qualitativos de um software, como desempenho, segurança e usabilidade. Normalmente a identificação dos requisitos não funcionais é realizada de maneira ad hoc, e desenvolvidas tardiamente. Neste artigo é proposto um padrão para a identificação e seleção de requisitos não funcionais para aplicações comerciais nos estágios iniciais de seu desenvolvimento, que não estejam conflitantes entre si, e que estejam mais aderentes às necessidades dos clientes.
Resumo. O desenvolvimento de software é uma atividade que trabalha diversos aspectos da Engenharia de Software. Um deles é a elicitação de requisitos e sua escrita em uma notação de fácil entendimento para os desenvolvedores de software. Requisitos Não Funcionais são aqueles que estão associados aos aspectos qualitativos de um software, como desempenho, segurança e usabilidade. Normalmente a identificação dos requisitos não funcionais é realizada de maneira ad hoc, e desenvolvidas tardiamente. Neste artigo é proposto um padrão para a identificação e seleção de requisitos não funcionais para aplicações comerciais nos estágios iniciais de seu desenvolvimento, que não estejam conflitantes entre si, e que estejam mais aderentes às necessidades dos clientes.
Palavras-chave: Requisitos Não Funcionais, Padrões de Software, Qualidade de Software, Engenharia de Requisitos.
Assinar:
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