The Deployment and Maintenance of a Condor-Based Campus Grid

2009 ◽  
pp. 165-176
Author(s):  
Dru Sepulveda ◽  
Sebastien Goasguen
Keyword(s):  
Campus Grid

Resource Description and Discovery of Campus Grid

2011 ◽  
pp. 660-668
Author(s):  
Zhi-yun Zheng ◽  
Jun-xia Zhao ◽  
Xing-jin Zhang ◽  
Lun Li
Keyword(s):  
Campus Grid ◽  
Resource Description

scholarly journals The Research and Design of Intelligent Tutoring System in Intelligent Turtoring System Based on Campus Grid

2013 ◽  
Vol 347-350 ◽  
pp. 3677-3680
Author(s):  
Yao Wen Xia ◽  
Ji Li Xie ◽  
Sai Dong Lv ◽  
Guo Hua Tang
Keyword(s):  
Technology Development ◽  
Intelligent Tutoring System ◽  
System Structure ◽  
Grid Technology ◽  
Tutoring System ◽  
Teaching System ◽  
Network Teaching ◽  
Grid Middleware ◽  
Campus Grid ◽  
Network Teaching System

Grid technology has become the computer and network technologys research hotspot and frontier, Grid technology development and the consummation will promote the development of educational information. The intelligent has shortcoming which are resource isolated, update difficulty etc, Based on the grid technology research and application, using OGSA (Open Grid Services Architecture) grid system structure, Taking GLOBUS as the grid middleware to build grid platform. This paper discuss the educational grid minimum implementation unit - - teaching system of campus grid and put forward the network teaching system model based on the grid technology. Then establish a distributed, heterogeneous sharing information resources of the library, it provides an efficient, fully shared virtual teaching space for network tutoring system. Finally, intelligent network teaching system and the grid binding laid certain foundation.

Distributed image processing over an adaptive Campus Grid

2009 ◽  
Vol 21 (3) ◽  
pp. 321-336 ◽  
Author(s):  
Simon J. Caton ◽  
Omer F. Rana ◽  
Bruce G. Batchelor
Keyword(s):  
Image Processing ◽  
Campus Grid

A new node organization mechanism of a campus Grid and performance analysis

2006 ◽  
Vol 18 (14) ◽  
pp. 1787-1798
Author(s):  
Zhiqun Deng ◽  
Guanzhong Dai ◽  
Ting Xie ◽  
Daowu Zhou ◽  
Dejun Mu ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Campus Grid ◽  
And Performance ◽  
Organization Mechanism

scholarly journals Huddersfield University Campus Grid: QGG of OSCAR Clusters

2010 ◽  
Vol 256 ◽  
pp. 012022 ◽  
Author(s):  
Dr Violeta Holmes ◽  
Ibad Kureshi
Keyword(s):  
University Campus ◽  
Campus Grid

Towards greening a campus grid: Free cooling during unsociable hours

2012 ◽  
Author(s):  
Jun Okitsu ◽  
Ken Naono ◽  
Shaharin Anwar Sulaiman ◽  
Nordin Zakaria ◽  
Alan Oxley
Keyword(s):  
Free Cooling ◽  
Campus Grid

Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid

2010 ◽  
Vol 368 (1926) ◽  
pp. 4005-4021 ◽  
Author(s):  
Simon N. Gosling ◽  
Dan Bretherton ◽  
Keith Haines ◽  
Nigel W. Arnell
Keyword(s):  
Climate Change ◽  
Water Resource ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Resource Stress ◽  
Runoff Changes ◽  
Campus Grid ◽  
The Impact

Uncertainties associated with the representation of various physical processes in global climate models (GCMs) mean that, when projections from GCMs are used in climate change impact studies, the uncertainty propagates through to the impact estimates. A complete treatment of this ‘climate model structural uncertainty’ is necessary so that decision-makers are presented with an uncertainty range around the impact estimates. This uncertainty is often underexplored owing to the human and computer processing time required to perform the numerous simulations. Here, we present a 189-member ensemble of global river runoff and water resource stress simulations that adequately address this uncertainty. Following several adaptations and modifications, the ensemble creation time has been reduced from 750 h on a typical single-processor personal computer to 9 h of high-throughput computing on the University of Reading Campus Grid. Here, we outline the changes that had to be made to the hydrological impacts model and to the Campus Grid, and present the main results. We show that, although there is considerable uncertainty in both the magnitude and the sign of regional runoff changes across different GCMs with climate change, there is much less uncertainty in runoff changes for regions that experience large runoff increases (e.g. the high northern latitudes and Central Asia) and large runoff decreases (e.g. the Mediterranean). Furthermore, there is consensus that the percentage of the global population at risk to water resource stress will increase with climate change.

Sign in / Sign up

Export Citation Format

Share Document