S-MDS: Semantic Monitoring and Discovery System for the Grid

Said, Mirza Pahlevi; Kojima, Isao

doi:10.1007/s10723-008-9111-2

Cited by 13 publications

(1 citation statement)

References 11 publications

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“…(e) application service Grid that provides access to remote applications, libraries hosted on various data centers and computational Grids (e.g., NetSolve, GridSolve [17,69,107]), (f) interaction Grid that interacts and collaborates to provide visualization between participants, (g) knowledge Grid [106,113] that focuses on knowledge discovery, acquisition, management, and processing with the aim of providing analytical business services, and (h) utility Grid that provides all the typical Grid services such as data, computing power, discovery and allocation of resources, QoS and contract management. Figure 2 shows the classification of basic Grid types.…”

Section: Resource Brokermentioning

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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Section: Resource Brokermentioning

confidence: 99%

Survey on Grid Resource Allocation Mechanisms

et al. 2014

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Ontology‐based Grid resource management

et al. 2009

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Grid resources are typically diverse in nature with respect to their software and hardware configurations, resource usage policies and the kind of application they support. Aggregating and monitoring these resources, and discovering suitable resources for the applications become a challenging issue. This is partially due to the representation of Grid metadata supported by the existing Grid middleware which offers limited scope for matching the job requirements that directly affect scheduling decisions. This paper proposes a semantic component in conventional Grid architecture to support ontology‐based representation of Grid metadata and facilitate context‐based information retrieval that complements Grid schedulers for effective resource management. Web Ontology language is used for creating Grid resource ontology and Algernon inference engine has been used for resource discovery. This semantic component has been integrated with conventional Grid schedulers. Several experiments have also been carried out to investigate the performance overhead that arises while integrating this component with Grid schedulers. Copyright © 2009 John Wiley & Sons, Ltd.

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