Ian Foster – författare

A guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples.

The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples.

The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security.

The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors.

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations.

Features:

New to the Second Edition:

This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations.

Features:

New to the Second Edition:

This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

Parallel Computing is a compelling vision of how computation can seamlessly scale from a single processor to virtually limitless computing power. Unfortunately, the scaling of application performance has not matched peak speed, and the programming burden for these machines remains heavy. The applications must be programmed to exploit parallelism in the most efficient way possible. Today, the responsibility for achieving the vision of scalable parallelism remains in the hands of the application developer.

This book represents the collected knowledge and experience of over 60 leading parallel computing researchers. They offer students, scientists and engineers a complete sourcebook with solid coverage of parallel computing hardware, programming considerations, algorithms, software and enabling technologies, as well as several parallel application case studies. The Sourcebook of Parallel Computing offers extensive tutorials and detailed documentation of the advanced strategies produced by research over the last two decades application case studies. The Sourcebook of Parallel Computing offers extensive tutorials and detailed documentation of the advanced strategies produced by research over the last two decades* Provides a solid background in parallel computing technologies* Examines the technologies available and teaches students and practitioners how to select and apply them* Presents case studies in a range of application areas including Chemistry, Image Processing, Data Mining, Ocean Modeling and Earthquake Simulation* Considers the future development of parallel computing technologies and the kinds of applications they will support

"The Grid is an emerging infrastructure that will fundamentally change the way we think about-and use-computing. The word Grid is used by analogy with the electric power grid, which provides pervasive access to electricity and has had a dramatic impact on human capabilities and society. Many believe that by allowing all components of our information technology infrastructure-computational capabilities, databases, sensors, and people-to be shared flexibly as true collaborative tools the Grid will have a similar transforming effect, allowing new classes of applications to emerge." --From the Preface

In 1998, Ian Foster and Carl Kesselman introduced a whole new concept in computing with the first edition of this book. Today there is a broader and deeper understanding of the nature of the opportunities offered by Grid computing and the technologies needed to realize those opportunities. In Grid 2, the editors reveal the revolutionary impact of large-scale resource sharing and virtualization within science and industry, the intimate relationships between organization and resource sharing structures and the new technologies required to enable secure, reliable, and efficient resource sharing on large scale.

Foster and Kesselman have once again assembled a team of experts to present an up-to-date view of Grids that reports on real experiences and explains the available technologies and new technologies emerging from labs, companies and standards bodies. Grid 2, like its predecessor, serves as a manifesto, design blueprint, user guide and research agenda for future Grid systems.