A Cloud Computing Platform for FY-4 Based on Resource Scheduling Technology

2016 ◽  
Author(s):  
Xiangang Zhao ◽  
Lizi Xie ◽  
Lan Wei ◽  
Manyun Lin ◽  
Zhanyun Zhang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Resource Scheduling ◽  
Computing Platform ◽  
Cloud Computing Platform

The Research on Computation Resource Scheduling Strategy for B2C Automatic Negotiation in Cloud Computing Platform

2012 ◽  
Vol 241-244 ◽  
pp. 2929-2933
Author(s):  
Hai Wen Han ◽  
Zhi Fei Zhu
Keyword(s):  
Cloud Computing ◽  
Resource Scheduling ◽  
Negotiation Process ◽  
Scheduling Strategy ◽  
Computing Platform ◽  
Automatic Negotiation ◽  
Cloud Computing Platform ◽  
Scheduling Strategies

The scheduling mechanism for cloud computing platform is described at first in this paper. Secondly the analysis of the B2C auto-negotiation process and the characteristics of its computation resource scheduling mechanism are given. Finally some computation resource scheduling strategies for B2C auto-negotiation in cloud computing platform are discussed.

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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