scholarly journals Long Paddock: climate risk and grazing information for Australian rangelands and grazing communities

2019 ◽  
Vol 41 (3) ◽  
pp. 225 ◽  
Author(s):  
G. Stone ◽  
R. Dalla Pozza ◽  
J. Carter ◽  
G. McKeon
Keyword(s):  
Cloud Computing ◽  
Web Services ◽  
Natural Resource ◽  
Climate Risk ◽  
Resource Base ◽  
Computing Platform ◽  
Amazon Web Services ◽  
Cloud Computing Platform ◽  
Natural Resource Base ◽  
Tailored Information

The Queensland Government’s Long Paddock website has been redeveloped on Amazon Web Services cloud computing platform, to provide Australian rangelands and grazing communities (i.e. rural landholders, managers, pastoralists (graziers), researchers, advisors, students, consultants and extension providers) with easier access to seasonal climate and pasture condition information. The website provides free, tailored information and services to support management decisions to maximise productivity, while maintaining the natural resource base. For example, historical rainfall and pasture analyses (i.e. maps, posters and data) have been developed to assist in communicating the risk of multi-year droughts that are a feature of Queensland’s highly variable climate.

Power Management of Virtualized Cloud Computing Platform

2012 ◽  
Vol 35 (6) ◽  
pp. 1262 ◽  
Author(s):  
Ke-Jiang YE ◽  
Zhao-Hui WU ◽  
Xiao-Hong JIANG ◽  
Qin-Ming HE
Keyword(s):  
Cloud Computing ◽  
Power Management ◽  
Computing Platform ◽  
Cloud Computing Platform

Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform

2020 ◽  
Vol 29 (2) ◽  
pp. 1-24
Author(s):  
Yangguang Li ◽  
Zhen Ming (Jack) Jiang ◽  
Heng Li ◽  
Ahmed E. Hassan ◽  
Cheng He ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Node Failures

In defense of decentralized research data management

Neuroforum ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Michael Hanke ◽  
Franco Pestilli ◽  
Adina S. Wagner ◽  
Christopher J. Markiewicz ◽  
Jean-Baptiste Poline ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Research Data ◽  
Data Archive ◽  
Individual Scientist ◽  
Computing Platform ◽  
Research Data Management ◽  
Cloud Computing Platform ◽  
Research Objects ◽  
Central Services

Abstract Decentralized research data management (dRDM) systems handle digital research objects across participating nodes without critically relying on central services. We present four perspectives in defense of dRDM, illustrating that, in contrast to centralized or federated research data management solutions, a dRDM system based on heterogeneous but interoperable components can offer a sustainable, resilient, inclusive, and adaptive infrastructure for scientific stakeholders: An individual scientist or laboratory, a research institute, a domain data archive or cloud computing platform, and a collaborative multisite consortium. All perspectives share the use of a common, self-contained, portable data structure as an abstraction from current technology and service choices. In conjunction, the four perspectives review how varying requirements of independent scientific stakeholders can be addressed by a scalable, uniform dRDM solution and present a working system as an exemplary implementation.

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