Dữ liệu gird

The Grid is the computing and data management infrastructure that will provide the electronic underpinning for a global society in business, government, research, science and entertainment [1–5]. Grids, illustrated in Figure 1.1, integrate networking, communication, computation and information to provide a virtual platform for computation and data management in the same way that the Internet integrates resources to form a virtual platform for information.

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Over the past several years there have been a number of projects aimed at building ‘production’ Grids. These Grids are intended to provide identified user communities with a rich, stable, and standard distributed computing environment. By ‘standard’ and ‘Grids’, we specifically mean Grids based on the common practice and standards coming out of the Global Grid Forum (GGF) (www.gridforum.org).

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Recent developments in high-speed networking enables collective use of globally distributed computing resources as a huge single problem-solving environment, also known as the Grid. The Grid not only presents a new, more difficult degree of inherent challenges in distributed computing such as heterogeneity, security, and instability, but will also require the constituent software substrates to be seamlessly interoperable across the network

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The term ‘the Grid’ was coined in the mid-1990s to denote a proposed distributed computing infrastructure for advanced science and engineering [1]. Considerable progress has since been made on the construction of such an infrastructure (e.g., [2–5]), but the term ‘Grid’ has also been conflated, at least in

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Computational biology is undergoing a revolution from a traditionally compute-intensive science conducted by individuals and small research groups to a high-throughput, datadriven science conducted by teams working in both academia and industry. It is this new biology as a data-driven science in the era of Grid Computing that is the subject of this chapter.

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The last decade has seen a substantial change in the way we perceive and use computing resources and services. A decade ago, it was normal to expect one’s computing needs to be serviced by localised computing platforms and infrastructures. This situation has changed; the change has been caused by, among other factors, the take-up of commodity computer and network components, the result of faster and more capable hardware and increasingly sophisticated software.

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This short chapter summarizes the current status of Grid Computational and Programming environments. It puts the corresponding section of this book in context and integrates a survey of a set of 28 chapters gathered together by the Grid Computing Environment (GCE) group of the Global Grid Forum, which is being published in 2002 as a special issue of Concurrency and Computation: Practice and Experience. Several of the chapters here are extensions or reprints of those papers.

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A Grid can be defined as a layer of networked services that allow users single sign-on access to a distributed collection of compute, data, and application resources. The Grid services allow the entire collection to be seen as a seamless information processing system that the user can access from any location. Unfortunately, for application developers, this Grid vision has been a rather elusive goal. The problem is that while there are several good frameworks for Grid architectures (Globus [1] and Legion/Avaki [18]), the task of application development and deployment has not become easier....

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In 1994, we outlined our vision for wide-area distributed computing [1]: For over thirty years science fiction writers have spun yarns featuring worldwide networks of interconnected computers that behave as a single entity. Until recently such science fiction fantasies have been just that. Technological changes are now occurring which may expand computational power in the same way that the invention of desktop calculators and personal computers did

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Recent developments in high-performance networks, computers, information servers, and display technologies make it feasible to design network-enabled tools that incorporate remote compute and information resources into local computational environments and collaborative environments that link people, computers, and databases into collaborative sessions.

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For over four years, the largest computing systems in the world have been based on ‘distributed computing’, the assembly of large numbers of PCs over the Internet. These ‘Grid’ systems sustain multiple teraflops continuously by aggregating hundreds of thousands to millions of machines, and demonstrate the utility of such resources for solving a surprisingly wide range of large-scale computational problems in data mining, molecular interaction, financial modeling, and so on.

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The goal of autonomic computing is the reduction of complexity in the management of large computing systems. The evolution of computing systems faces a continuous growth in the number of degrees of freedom the system must manage in order to be efficient. Two major factors contribute to the increase in the number of degrees of freedom: Historically, computing elements, such as CPU, memory, disks, network and so on, have nonuniform advancement. The disparity between the capabilities/speeds of various elements opens up a number of different strategies for a task depending upon the environment..

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Data Grids address computational and data intensive applications that combine very large datasets and a wide geographical distribution of users and resources [1, 2]. In addition to computing resource scheduling, Data Grids address the problems of storage and data management, network-intensive data transfers and data access optimization, while maintaining high reliability and availability of the data (see References [2, 3] and references therein).

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The emergence of Grid computing as the prototype of a next-generation cyber infrastructure for science has excited high expectations for its potential as an accelerator of discovery, but it has also raised questions about whether and how the broad population of research professionals, who must be the foundation of such productivity, can be motivated to adopt this new and more complex way of working.

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Computational Grids [1, 2] are large collections of resources such as computers, networks, on-line instruments, or storage archives, and they are becoming popular platforms for running large-scale, resource-intensive applications. Many challenges exist in providing the necessary mechanisms for accessing, discovering, monitoring, and aggregating Grid resources. Consequently, a tremendous effort has been made to develop middleware technology to establish a Grid software infrastructure (GSI) [2–4].

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Built upon a foundation of Simple Object Access Protocol (SOAP), Web Services Description Language (WSDL) and Universal Description Discovery and Integration (UDDI) technologies, Web services have become a widely accepted industry standard in the last few years [1, 2]. Because of their platform independence, universal compatibility, and network accessibility, Web services will be at the heart of the next generation of distributed systems. As more vendors offer SOAP tools and services, the advantages of using SOAP and Web services as an integration point will become even more pronounced.

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In line with the usual chemistry seminar speaker who cannot resist changing the advertised title of a talk as the first, action of the talk, we will first, if not actually extend the title, indicate the vast scope of combinatorial chemistry. ‘Combinatorial Chemistry’ includes not only the synthesis of new molecules and materials, but also the associated purification, formulation, ‘parallel experiments’ and ‘high-throughput screening’ covering all areas of chemical discovery.

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Until recently, application developers could often assume a target environment that was (to a useful extent) homogeneous, reliable, secure, and centrally managed. Increasingly, however, computing is concerned with collaboration, data sharing, and other new modes of interaction that involve distributed resources. The result is an increased focus on the interconnection of systems both within and across enterprises, whether in the form of intelligent networks, switching devices,

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This chapter examines how databases can be integrated into the Grid [1]. Almost all early Grid applications are file-based, and so, to date, there has been relatively little effort applied to integrating databases into the Grid. However, if the Grid is to support a wider range of applications, both scientific and otherwise, then database integration into the Grid will become important. For example, many applications in the life and earth sciences and many business applications are heavily dependent on databases.

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There are no crisp definitions of Grids [1, 2] and Peer-to-Peer (P2P) Networks [3] that allow us to unambiguously discuss their differences and similarities and what it means to integrate them. However, these two concepts conjure up stereotype images that can be compared. Taking ‘extreme’ cases, Grids are exemplified by the infrastructure used to allow seamless access to supercomputers and their datasets. P2P technology is exemplified by Napster and Gnutella, which can enable ad hoc communities of low-end clients to advertise and access the files on the communal computers....

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