This book originates from the First International Workshop on Scientific Applications of Grid Computing, SAG 2004, held in Beijing, China, in September 2004. Besides 8 thoroughly revised reviewed full workshop papers selected from initially 29 submissions, 10 invited papers from leading researchers complete coverage of the relevant topics and make this book a representative survey of current research activities in the field of grid computing applications. The papers are organized in topical sections on data-based applications; bioinformatics applications; application architectures, frameworks, and models; accounting and market-based architecture; and resource and information management on the grid.

Unter "Grid Computing" versteht man die gleichzeitige Nutzung vieler Computer in einem Netzwerk für die Lösung eines einzelnen Problems. Grundsätzliche Aspekte und anwendungsbezogene Details zu diesem Gebiet finden Sie in diesem Band. - Grid Computing ist ein viel versprechender Trend, denn man kann damit (1) vorhandene Computer-Ressourcen kosteneffizient nutzen, (2) Probleme lösen, für die enorme Rechenleistungen erforderlich sind, und (3) Synergieeffekte erzielen, auch im globalen Maßstab - Ansatz ist in Forschung und Industrie (IBM, Sun, HP und andere) zunehmend populär (aktuelles Beispiel: Genomforschung) - Buch deckt Motivationen zur Einführung von Grids ebenso ab wie technologische Grundlagen und ausgewählte Beispiele für moderne Anwendungen

Presents advances for grid and P2P paradigms, middleware, programming models, communication libraries, as well as their application to the resolution of real-life problems. This book is suitable for academics, scientists, software developers and engineers interested in the grid and P2P paradigms.

Many applications follow the distributed computing paradigm, in which parts of the application are executed on different network-interconnected computers. The extension of these applications in terms of number of users or size has led to an unprecedented increase in the scale of the infrastructure that supports them. Large-Scale Distributed Computing and Applications: Models and Trends offers a coherent and realistic image of today's research results in large scale distributed systems, explains state-of-the-art technological solutions for the main issues regarding large scale distributed systems, and presents the benefits of using large scale distributed systems and the development process of scientific and commercial distributed applications.

Past and current research in computer performance analysis has focused primarily on dedicated parallel machines. However, future applications in the area of high-performance computing will not only use individual parallel systems but a large set of networked resources. This scenario of computational and data Grids is attracting a great deal of attention from both computer and computational scientists. In addition to the inherent complexity of parallel machines, the sharing and transparency of the available resources introduces new challenges on performance analysis, techniques, and systems. In order to meet those challenges, a multi-disciplinary approach to the multi-faceted problems of performance is required. New degrees of freedom will come into play with a direct impact on the performance of Grid computing, including wide-area network performance, quality-of-service (QoS), heterogeneity, and middleware systems, to mention only a few.

Researchers in the ?eld of life sciences rely increasingly on information te- nology to extract and manage relevant knowledge. The complex computational and data management needs of life science research make Grid technologies an attractive support solution. However, many important issues must be addressed before the Life Science Grid becomes commonplace. The 1st International Life Science Grid Workshop (LSGRID 2004) was held in Kanazawa Japan, May 31–June 1, 2004. This workshop focused on life s- ence applications of grid systems especially for bionetwork research and systems biology which require heterogeneous data integration from genome to phenome, mathematical modeling and simulation from molecular to population levels, and high-performance computing including parallel processing, special hardware and grid computing. Fruitful discussions took place through 18 oral presentations, including a keynote address and ?ve invited talks, and 16 poster and demonstration p- sentations in the ?elds of grid infrastructure for life sciences, systems biology, massive data processing, databases and data grids, grid portals and pipelines for functional annotation, parallel and distributed applications, and life science grid projects. The workshop emphasized the practical aspects of grid techno- gies in terms of improving grid-enabled data/information/knowledge sharing, high-performance computing, and collaborative projects. There was agreement among the participants that the advancement of grid technologies for life science research requires further concerted actions and promotion of grid applications. We therefore concluded the workshop with the announcement of LSGRID 2005.

The amount of data in everyday life has been exploding. This data increase has been especially significant in scientific fields, where substantial amounts of data must be captured, communicated, aggregated, stored, and analyzed. Cloud Computing with e-Science Applications explains how cloud computing can improve data management in data-heavy fields such as bioinformatics, earth science, and computer science. The book begins with an overview of cloud models supplied by the National Institute of Standards and Technology (NIST), and then: Discusses the challenges imposed by big data on scientific data infrastructures, including security and trust issues Covers vulnerabilities such as data theft or loss, privacy concerns, infected applications, threats in virtualization, and cross-virtual machine attack Describes the implementation of workflows in clouds, proposing an architecture composed of two layers—platform and application Details infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and software-as-a-service (SaaS) solutions based on public, private, and hybrid cloud computing models Demonstrates how cloud computing aids in resource control, vertical and horizontal scalability, interoperability, and adaptive scheduling Featuring significant contributions from research centers, universities, and industries worldwide, Cloud Computing with e-Science Applications presents innovative cloud migration methodologies applicable to a variety of fields where large data sets are produced. The book provides the scientific community with an essential reference for moving applications to the cloud.

This book presents research from many of the major projects involved in the emerging global grid infrastructure. With a particular focus on the practical advantages and applications of grid computing – including real case studies – the book provides an in-depth study of grid technology for a wide range of different needs. Topics: examines a remote instrumentation infrastructure, and a methodology to support e-science applications on e-infrastructures; describes the GEMS storage system, and pipeline workflows for optimizing end-to-end performance in wide-area networks; investigates semantic grid system architecture, social grid agents, and monitoring platforms designed for large-scale distributed systems; explores job control using service-level agreements; introduces the Composable Services Architecture for dynamic service provisioning, and the semantically driven communication middleware platform, Phoenix; discusses the PhyloGrid application, and a numerical simulation performed using grid computing.

This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, on September 4 and 5, 2020. The book starts by addressing the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis have been included with upfront details. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber physical system and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.