5,436 research outputs found
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
Network-aware heuristics for inter-domain meta-scheduling in Grids
AbstractGrid computing generally involves the aggregation of geographically distributed resources in the context of a particular application. As such resources can exist within different administrative domains, requirements on the communication network must also be taken into account when performing meta-scheduling, migration or monitoring of jobs. Similarly, coordinating efficient interaction between different domains should also be considered when performing such meta-scheduling of jobs. A strategy to perform peer-to-peer-inspired meta-scheduling in Grids is presented. This strategy has three main goals: (1) it takes the network characteristics into account when performing meta-scheduling; (2) communication and query referral between domains is considered, so that efficient meta-scheduling can be performed; and (3) the strategy demonstrates scalability, making it suitable for many scientific applications that require resources on a large scale. Simulation results are presented that demonstrate the usefulness of this approach, and it is compared with other proposals from literature
FRTRUST: a fuzzy reputation based model for trust management in semantic P2P grids
Grid and peer-to-peer (P2P) networks are two ideal technologies for file
sharing. A P2P grid is a special case of grid networks in which P2P
communications are used for communication between nodes and trust management.
Use of this technology allows creation of a network with greater distribution
and scalability. Semantic grids have appeared as an expansion of grid networks
in which rich resource metadata are revealed and clearly handled. In a semantic
P2P grid, nodes are clustered into different groups based on the semantic
similarities between their services. This paper proposes a reputation model for
trust management in a semantic P2P Grid. We use fuzzy theory, in a trust
overlay network named FR TRUST that models the network structure and the
storage of reputation information. In fact we present a reputation collection
and computation system for semantic P2P Grids. The system uses fuzzy theory to
compute a peer trust level, which can be either: Low, Medium, or High. Our
experimental results demonstrate that FR TRUST combines low (and therefore
desirable) a good computational complexity with high ranking accuracy.Comment: 12 Pages, 10 Figures, 3 Tables, InderScience, International Journal
of Grid and Utility Computin
Novel mechanism for evaluating feedback in the grid environment on resource allocation
The primary concern in proffering an infrastructure for general purpose computational grids formation is security. Grid implementations have been devised to deal with the security concerns. The chief factors that can be problematic in the secured selection of grid resources are the wide range of selection and the high degree of strangeness. Moreover, the lack of a higher degree of confidence relationship is likely to prevent efficient resource allocation and utilization. In this paper, we propose an efficient approach for the secured selection of grid resources, so as to achieve secure execution of the jobs. The presented approach utilizes trust and reputation for securely selecting the grid resources by also evaluation user’s feedback on the basis of the feedback already available about the entities. The proposed approach is scalable for an increased number of resources
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