Como Criar um Blog??

Como esse é o primeiro Post do nosso blog, resolvi fazer um Tutorial: Como Criar um Blog??

Muitas Pessoas querem começar um blog para Ganhar Dinheiro mas não sabem por onde começar, ai vai uma dica: Tenha Muita Paciência e goste do tema que você irá desenvolver no seu blog.

Vamos Começar:

• Acesse o Site: www.blogspot.com;
• Clique em: CRIAR UM BLOG;

• Digite seu endereço de email;
• Digite Novamente;
• Digite uma Senha; (que você não vá esqueçer)
• Digite Novamente;
• Digite seu Nome de Tela (Geralmente seu próprio Nome)
• Digite o Código de Verificação;
• Selecione: Aceito os Termos de Uso;
• Continuar!!

• Crie um Nome Para seu Blog;
• Digite Seu Endereço do Blog [URL] - Ex: blogger-extreme.blogspot.com
• Verifique se a URL que você escolheu está disponível, cliquando em: Verificar Disponibilidade;
• Clique em Continuar!!

• Escolhe um Modelo (Aparência, Layout, Template) para seu blog;
• Continuar!!

• O Seu Blog está Pronto!!!
• Começar a Usar o Blog!!

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Google Cluster Data

Posted by Joseph L. Hellerstein, Manager of Google Performance Analytics

Google faces a large number of technical challenges in the evolution of its applications and infrastructure. In particular, as we increase the size of our compute clusters and scale the work that they process, many issues arise in how to schedule the diversity of work that runs on Google systems.

We have distilled these challenges into the following research topics that we feel are interesting to the academic community and important to Google:

• Workload characterizations: How can we characterize Google workloads in a way that readily generates synthetic work that is representative of production workloads so that we can run stand alone benchmarks?
• Predictive models of workload characteristics: What is normal and what is abnormal workload? Are there "signals" that can indicate problems in a time-frame that is possible for automated and/or manual responses?
• New algorithms for machine assignment: How can we assign tasks to machines so that we make best use of machine resources, avoid excess resource contention on machines, and manage power efficiently?
• Scalable management of cell work: How should we design the future cell management system to efficiently visualize work in cells, to aid in problem determination, and to provide automation of management tasks?

To aid researchers in addressing these questions in a realistic manner, we will provide data from Google production systems. The initial focus of these data will be workload characterization. Details of the data can be found here. The data are structured as follows:

• Time (int) - time in seconds since the start of data collection
• JobID (int) - Unique identifier of the job to which this task belongs
• TaskID (int) - Unique identifier of the executing task
• Job Type (0, 1, 2, 3) - class of job (a categorization of work)
• Normalized Task Cores (float) - normalized value of the average number of cores used by the task
• Normalized Task Memory (float) - normalized value of the average memory consumed by the task

We solicit your feedback in terms of: (a) the quality and content of the data we are providing; (b) technical approaches and/or results related to the topics above; and (c) other research topics that you feel Google should be addressing in the area of Cloud Computing (along with details of the data required to address these topics).

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Announcing our Q4 Research Awards

Posted by Maggie Johnson, Director of Education & University Relations and Jeff Walz, Head of University Relations

We do a significant amount of in-house research at Google, but we also maintain strong ties with academic institutions globally, pursuing innovative research in core areas relevant to our mission. One way in which we support academic institutions is the Google Research Awards program, aimed at identifying and supporting world-class, full-time faculty pursuing research in areas of mutual interest.

Our University Relations team and core area committees just completed the latest round of research awards, and we're excited to announce them today. We had a record number of submissions, resulting in 76 awards across 17 different areas. Over $4 million was awarded — the most we have ever funded in a round.

The areas that received the highest level of funding for this round were systems and infrastructure, machine learning, multimedia, human computer interaction, and security. These five areas represent important areas of collaboration with university researchers. We're also excited to be developing more connections internationally. In this round, over 20 percent of the funding was awarded to universities outside the U.S.

Some exciting examples from this round of awards:

Ondrej Chum, Czech Technical University, Large Scale Visual Link Discovery. This project addresses automatic discovery of visual links between image parts in huge image collections. Visual links associate parts of images that share even a relatively small, but distinctive, visual information.

Bernd Gartner, ETH Zurich, Linear Time Kernel Methods and Matrix Factorizations. This project aims to derive faster approximation algorithms for kernel methods as well as matrix approximation problems and leverage these two promising paradigms for better performance on large scale data.

Dawson Engler, Stanford University, High Coverage, Deep Checking of Linux Device Drivers using KLEE + Under-constrained Execution Symbolic execution. This project extends the recently built KLEE, a tool that automatically generates test cases that execute most statements in real programs, so that it allows automatic, deep checking of Linux device drivers.

Jeffrey G. Gray, University of Alabama at Birmingham, Improving the Education and Career Opportunities of the Physically Disabled through Speech-Aware Development Environments. This project will investigate the science and engineering of tool construction to allow those with restricted limb mobility to access integrated development environments (IDEs), which will support programming by voice.

Xiaohui (Helen) Gu, North Carolina State University, Predictive Elastic Load Management for Cloud Computing Infrastructures. This project proposes to use fine-grained resource signatures with signal processing techniques to improve resource utilization by reducing the number of physical hosts required to run all applications.

Jason Hong and John Zimmerman, Carnegie Mellon University, Context-Aware Mobile Mash-ups. This project seeks to build tools for non-programmers to create location and context-aware mashups of data for mobile devices that can present time- and place-approriate information.

S V N Vishwanathan, Purdue University, Training Binary Classifiers using the Quantum Adiabatic Algorithm. The goal of this project is to harness the power of quantum algorithms in machine learning. The advantage of the new quantum methods will materialize even more once new adiabatic quantum processors become available.

Emmett Witchel and Vitaly Shmatikov, University of Texas at Austin, Private and Secure MapReduce. This project proposes to build a practical system for large-scale distributed computation that provides rigorous privacy and security guarantees to the individual data owners whose information has been used in the computation.

Click here to see a full list of this round’s award recipients. More information on our research award program can be found on our website.