Semantic-based Grid Resource Discovery and its Integration with the Grid Service Broker

Somasundaram, Thamarai Selvi; Balachandar, Ram; Kandasamy, Vijayakumar; Buyya, Rajkumar; Raman, Rajagopalan; Mohanram, N.; Varun, S.

doi:10.1109/adcom.2006.4289861

Cited by 35 publications

(18 citation statements)

References 12 publications

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“…Another notable approach to the resource discovery problem is the semantic communities one (Li and Vuong, 2005;Zhu et al, 2005;Somasundaram et al, 2006). Motivation behind the semantic communities approach is that Grid communities and human communities consist of members that are engaged in sharing and communication.…”

Section: Related Workmentioning

confidence: 99%

Resource discovery in a Grid system: Directing requests to trustworthy virtual organizations based on global trust values

Karaoglanoglou

Karatza

2011

Journal of Systems and Software

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Section: Related Workmentioning

confidence: 99%

Resource discovery in a Grid system: Directing requests to trustworthy virtual organizations based on global trust values

Karaoglanoglou

Karatza

2011

Journal of Systems and Software

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“…The effectiveness and efficiency of Grid services is substantially enhanced by this approach, particularly when the Grid contains large and complex resources [31,32]. This can also be seen in research on workflow automation [33] and resource discovery [34,35]. Virtual observatories [36,37], though they do not claim to be semantic grids, satisfy Foster's grid criteria [38] and apply Semantic Web technologies.…”

Section: Knowledge Representation In Sciencementioning

confidence: 99%

A Vision and Agenda for Theory Provenance in Scientific Publishing

Wood

Larson

Gardner

2009

Database Systems for Advanced Applications

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Abstract. Primary motivations for effective data and process provenance in science are to facilitate validation and reproduction of experiments and to assist in the interpretation of data-analysis outcomes. Central to both these aims is an understanding of the ideas and hypotheses that the data supports, and how those ideas fit into the wider scientific context. Such knowledge consists of the collection of relevant previous ideas and experiments from the body of scientific knowledge, or, more specifically, how those ideas and hypotheses evolved, the steps in that evolution, and the experiments and results used to support those steps. This information we term the provenance of ideas or theory provenance. We propose an integrated approach to scientific knowledge management, combining data, process and theory provenance, providing full transparency for effective verification and review.

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“…In such systems the resource matching is performed using a simple query language which compares strings or integers known as symmetric flat attributes based on exact matching [126,128]. In this matching approach, the resource attribute-value pairs are matched with the request attributes specified as primitive constraint statements.…”

Section: Resource Discoverymentioning

confidence: 99%

“…Traditional resource discovery systems (e.g., Globus MDS [106,126,128], Condor Matchmaker [128], Portable Batch System [128]) use the exact lookup approach called matchmaking for locating resources. In such systems the resource matching is performed using a simple query language which compares strings or integers known as symmetric flat attributes based on exact matching [126,128].…”

Section: Resource Discoverymentioning

confidence: 99%

Survey on Grid Resource Allocation Mechanisms

et al. 2014

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Grid is a distributed high performance computing paradigm that offers various types of resources (like computing, storage, communication) to resource-intensive user tasks. These tasks are scheduled to allocate available Grid resources efficiently to achieve high system throughput and to satisfy A. Y. Zomaya University of Sydney, Sydney, Australia user requirements. The task scheduling problem has become more complex with the ever increasing size of Grid systems. Even though selecting an efficient resource allocation strategy for a particular task helps in obtaining a desired level of service, researchers still face difficulties in choosing a suitable technique from a plethora of existing methods in literature. In this paper, we explore and discuss existing resource allocation mechanisms for resource allocation problems employed in Grid systems. The work comprehensively surveys Gird resource allocation mechanisms for different architectures (centralized, distributed, static or dynamic). The paper also compares these resource allocation mechanisms based on their common features such as time complexity, searching mechanism, allocation strategy, optimality, operational environment and objective function they adopt for solving computing-and data-intensive applications. The comprehensive analysis of cutting-edge research in the Grid domain presented in this work provides readers with an understanding of essential concepts of resource allocation mechanisms in Grid systems and helps them identify important and outstanding issues for further investigation. It also helps readers to choose the most appropriate mechanism for a given system/application.

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Semantic-based Grid Resource Discovery and its Integration with the Grid Service Broker

Cited by 35 publications

References 12 publications

Resource discovery in a Grid system: Directing requests to trustworthy virtual organizations based on global trust values

Resource discovery in a Grid system: Directing requests to trustworthy virtual organizations based on global trust values

A Vision and Agenda for Theory Provenance in Scientific Publishing

Survey on Grid Resource Allocation Mechanisms

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