467,974 research outputs found
Introducing risk management into the grid
Service Level Agreements (SLAs) are explicit statements about all expectations and obligations in the business partnership between customers and providers. They have been introduced in Grid computing to overcome the best effort approach, making the Grid more interesting for commercial applications. However, decisions on negotiation and system management still rely on static approaches, not reflecting the risk linked with decisions. The EC-funded project "AssessGrid" aims at introducing risk assessment and management as a novel decision paradigm into Grid computing. This paper gives a general motivation for risk management and presents the envisaged architecture of a "risk-aware" Grid middleware and Grid fabric, highlighting its functionality by means of three showcase scenarios
Performance of an Operating High Energy Physics Data Grid: D0SAR-Grid
The D0 experiment at Fermilab's Tevatron will record several petabytes of
data over the next five years in pursuing the goals of understanding nature and
searching for the origin of mass. Computing resources required to analyze these
data far exceed capabilities of any one institution. Moreover, the widely
scattered geographical distribution of D0 collaborators poses further serious
difficulties for optimal use of human and computing resources. These
difficulties will exacerbate in future high energy physics experiments, like
the LHC. The computing grid has long been recognized as a solution to these
problems. This technology is being made a more immediate reality to end users
in D0 by developing a grid in the D0 Southern Analysis Region (D0SAR),
D0SAR-Grid, using all available resources within it and a home-grown local task
manager, McFarm. We will present the architecture in which the D0SAR-Grid is
implemented, the use of technology and the functionality of the grid, and the
experience from operating the grid in simulation, reprocessing and data
analyses for a currently running HEP experiment.Comment: 3 pages, no figures, conference proceedings of DPF04 tal
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FutureGRID: A Program for long-term research into GRID systems architecture
Proceedings of the 2003 UK e-Science All Hands Meeting, 31st August - 3rd September, Nottingham UKThis is a project to carry out research into long-term GRID architecture, in the University of Cambridge
Computer Laboratory and the Cambridge eScience Center, with support from the Microsoft Research
Laboratory, Cambridge.
It is part of a larger vision for future systems architectures for public computing platforms, including
both scientitic GRID and commodity level computing such as games, peer2peer computing and storage
services and so forth, based on work in the laboratories in recent years into massively scaleable distributed systems for storage, computation, content distribution and collaboration[26]
Reasoning Services for the Semantic Grid
The Grid aims to support secure, flexible and coordinated resource sharing through providing a middleware platform for advanced distributing computing. Consequently, the Grid’s infrastructural machinery aims to allow collections of any kind of resources—computing, storage, data sets, digital libraries, scientific instruments, people, etc—to easily form Virtual Organisations (VOs) that cross organisational boundaries in order to work together to solve a problem. A Grid depends on understanding the available resources, their capabilities, how to assemble them and how to best exploit them. Thus Grid middleware and the Grid applications they support thrive on the metadata that describes resources in all their forms, the VOs, the policies that drive then and so on, together with the knowledge to apply that metadata intelligently
S-OGSA as a Reference Architecture for OntoGrid and for the Semantic Grid
The Grid aims to support secure, flexible and coordinated resource sharing through providing a middleware platform for advanced distributing computing. Consequently, the Grid’s infrastructural machinery aims to allow collections of any kind of resources—computing, storage, data sets, digital libraries, scientific instruments, people, etc—to easily form Virtual Organisations (VOs) that cross organisational boundaries in order to work together to solve a problem. A Grid depends on understanding the available resources, their capabilities, how to assemble them and how to best exploit them. Thus Grid middleware and the Grid applications they support thrive on the metadata that describes resources in all their forms, the VOs, the policies that drive then and so on, together with the knowledge to apply that metadata intelligently
A REVIEW OF GRID COMPUTING
Grid computing is a combination of interconnected resources which can be spread all over the world having higher computing capabilities. The benefit of grid computing includes higher computation and memory capacity because of grid resources spread all over the world. The grid computing is managed by intra-grid scope which refers to the methodologies and the algorithms used for managing the grid network related issues such as task scheduling, resource balancing and security of the network. The advantages of grid computing include access to inaccessible resources, resource utilization and balancing, reliability, and parallel computing and scalability. The limitations of the grid computing include application in limited fields and suitability with applications running in batch mode only based on parallel processin
Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies
Grid is an infrastructure that involves the integrated and collaborative use
of computers, networks, databases and scientific instruments owned and managed
by multiple organizations. Grid applications often involve large amounts of
data and/or computing resources that require secure resource sharing across
organizational boundaries. This makes Grid application management and
deployment a complex undertaking. Grid middlewares provide users with seamless
computing ability and uniform access to resources in the heterogeneous Grid
environment. Several software toolkits and systems have been developed, most of
which are results of academic research projects, all over the world. This
chapter will focus on four of these middlewares--UNICORE, Globus, Legion and
Gridbus. It also presents our implementation of a resource broker for UNICORE
as this functionality was not supported in it. A comparison of these systems on
the basis of the architecture, implementation model and several other features
is included.Comment: 19 pages, 10 figure
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