The hierarchical resource management model based on cloud computing

2012 ◽  
Author(s):  
Ping Guo ◽  
Ling-ling Bu
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Management Model ◽  
Model Based

A P2P Resource Management Model Based on Cloud Computing

2014 ◽  
Vol 989-994 ◽  
pp. 1930-1933
Author(s):  
Yang Lu ◽  
Guang Feng Liu
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Cloud Layer ◽  
Management Model ◽  
Cloud Systems ◽  
Computing Platform ◽  
The Core ◽  
Model Based ◽  
Cloud Computing Platform ◽  
And Storage

The technology of cloud computing has become a hot issue of research in the service of network in recent years. Cloud computing platform provide computing and storage services to customers. And it has been widely applied in e-business, e-education and etc.. While Cloud systems are usually hosted in large datacenters which may become a bottleneck to the system. In this paper we describe the design of a double-layer P2P model based on cloud computing. In the model, user nodes grouped into clusters form the Extended Cloud layer, and transfer file to each other without participation of the Core Cloud layer. The new model has better scalability and efficiency.

An improved resource management model based on MDS

2005 ◽  
Author(s):  
Man Yuan ◽  
Changying Sun ◽  
Pengfei Li ◽  
Yongdong Sun ◽  
Rui He
Keyword(s):  
Resource Management ◽  
Management Model ◽  
Model Based

An Effective Grid Resource Management Model Based on Cloud Computing Platform

2011 ◽  
Vol 50-51 ◽  
pp. 358-362
Author(s):  
Yue Zhang ◽  
Yun Xia Pei
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Management Model ◽  
Utilization Rate ◽  
Grid Resource ◽  
Grid Environment ◽  
Computing Platform ◽  
Scheduling Model ◽  
Cloud Computing Platform ◽  
Simulation Results

Resource management model was presented based on cloud computing using virtualization technologies. According to this strategy, a resource scheduling model was designed. This strategy can really effectively improve resource utilization rate. This algorithm met the needs of cloud computing more than others for grid environment with shorter response time and better performance, which were proved by the simulation results in the Gridsim environment.

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