Survey: Grid Computing and Semantic Web
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Grid Computing is a platform for coordinated resource sharing and problem solving on a global scale among virtual organizations. Grid uses Grid Services to access and use a set of Grid resources. Subsequently, these Grid Services need to be discovered, selected and invoked quickly and efficiently to satisfy the needs of a demanding environment such as Grid. In this paper, we introduce how the Grid Computing takes advantage of the benefits of Semantic Web to manipulate Grid Services obtaining better searches, results and performance.Keywords:
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Web Grid computing is a new research field and Grid environment has more complex features than general network, In order to implement efficient Grid computing many problems must be solved.Then how to manage Web Services resources in Grid environment is one of the key factors that determine whether the Grid computing can get a high performance. This article thoroughly has analyzed the present situation of Web grid resource management,at the same time,summarized the deficiency in the present of Web grid resource management technology,proposed a Web Services management model based on Agent domain.
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We present an agent-based computational grid (ACG), which applies the concept of computational grid to agents. The ACG system is to implement a uniform higher-level management of the computing resources and services on the grid, and provide users with a consistent and transparent interface for accessing such services. All entities in the grid environment including computing resources and services can be represented as agents. Each entity is registered with a grid service manager. An ACG grid service can be a service agent that provides the actual grid service to the other grid member. Grid members communicate with each other by communication space that is an implementation of tuple space. The design and implementation of four grid protocols for grid service discovery are given. Finally, some conclusions are given.
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Grid technology aims to help eliminate isolated island of hardware and software resources,so that users can achieve the sharing and managing of many kinds of resources and easily provide services on the entire grid.Currently,the great challenge from the requirements of many popular grid applications is how to share and manage data resources in grid architecture.This paper introduces the architecture design to realize publishing data resources efficiently in grid environment,and then applies this idea in the implementation of data publishing system for scientific data grid.
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Grid is an emerging infrastructure which enables effective coordinate access to various distributed computing resources in order to serve the needs of collaborative research and work across the world. Grid resource management is always a key subject in the grid computing. We first analyze the resource management in the grid computing environment, then according to the load imbalance question in the ant colony optimization algorithm, propose an improved algorithm that suits to be used in the grid environment.
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Well organized and easy usable Grid management system is very important for executing various Grid applications and managing Grid computing environment.Moreover, information system which can support Grid management system by providing various Grid environment related information is also one of the most interesting issue in the Grid middleware system area.Effective cooperation between Grid management system and information system can make a novel Grid middleware system.Especially, service oriented architecture based Grid management system is flexible and extensible for providing various type of Grid services.Also, information system based on data mining process which comprises various different kinds of domains such as users, resources and applications can make Grid management system more precise and efficient.In this paper, we propose semantic Grid middleware system which is a combination of Grid management system and semantic information system.
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Grid computing environment generally refers to computing environment and programming environment.It is grid set of tools and technology allowing users to access resources and the application of in a simple way. Grid computing environment can be classified by the technology which is used,but the key of the choosing method and making sure classification is to make the system has better self-definition,durability and easy-to-use features,and do not have a negative impact on overall performance of the system.The main characteristics of computing environment are on the processing to the distributed basic components,programming model,security,data management,tools expansion,choice and using of other advanced tools,as well as user interfaces.The key technology to construct the computing environment is: Grid Portal Development Kit,Java commercialization Grid Toolbox,notification/event services.Grid computing environment has several computing models,including MPI,Legion,and XCAT,which all support the packaging of metadata and objects.
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computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on largescale resource sharing, innovative applications, and, in some cases, highperformance orientation. In this article, we discuss the structure and the application of Grid. Firstly, the Five Layered Grid Architecture is reviewed, which composed by five layers named as fabric, connectivity, resource, collective and application.Grid can be defined as flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources—what called as virtual organizations. In such settings, we encounter unique authentication, authorization, resource access, resource discovery, and other challenges. It is this class of problem that is addressed by Grid technologies. Secondly, another Grid structure, OGSA(Open Grid Services Architecture) is discussed. Built on concepts and technologies from the Grid and Web services communities, this architecture defines an uniformly exposed service semantics (the Grid service); defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provides location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities. Finally, we discuss the applications of Grid technology. These applications involve distributed supercomputing, distributed instrumentation system, and intensive data computing. Some famous examples are also given out for demonstration.
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This chapter contains sections titled: Introduction The OGSA Approach Data Grid Services Issues Summary Acknowledgements References
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