A Scalable Routing Architecture for Resource Discovery in Cloud Computing

Gui De Zheng; Ming Chen

doi:10.4028/www.scientific.net/AMR.179-180.203

Paper Titles

Research on Multidisciplinary Modeling and Robust Control for Electric Power Steering System
p.179

Static Strength and Fatigue Analysis of the Automobile Sub-Frame by FEA Simulation Method and Fatigue Test
p.186

Nitrogen and Lanthanum Co-Doped TiO₂ with Enhanced Photocatalytic Activity
p.192

The Characteristic of Petroleum Contaminant Adsorption Materials and the Estimation of Adsorption Attenuation
p.197

A Scalable Routing Architecture for Resource Discovery in Cloud Computing
p.203

Study on Static Assembly Interference Pressure between a Thin-Wall Flexible Cup and a Hollow Flexible Shaft
p.212

Real-Time Model Building of CNC System and its Schedulable Analysis
p.220

A Method of the Image Emotion Notation Based Fuzzy Reasoning
p.226

Algorithm Researching of RBF Neural Network Based on Improved PSO
p.233

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 179-180A Scalable Routing Architecture for Resource...

A Scalable Routing Architecture for Resource Discovery in Cloud Computing

Abstract:

Cloud Computing technology enables the sharing and collaborating of wide variety of resources. To fully utilize these resources, effective discovery techniques are necessities. Proposing and designing a resource discovery scheme based on Economic Agent. Base on the economic model and the technique in agent of grouping nodes sharing similar files to improve efficiency, this thesis suggests a resource discovery scheme based on economic agent, which is called EAGRD. Theoretical models on resource discovery are provided, under which EAGRD is compared with existing schemes theoretically. By controlling propagation of message into related communities, EAGRD improves time and network efficiency at the cost of topological maintenance overhead. Results from simulation demonstrate that this architecture is very effective in Cloud Computing resource discovery.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 179-180)

Pages:

203-211

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.179-180.203

Citation:

Cite this paper

Online since:

January 2011

Authors:

Gui De Zheng, Ming Chen

Keywords:

Cloud Computing Resource, Multi-Economic Agent, Resource Discovery

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] R.K. Jain, D.M. Chiu, and W.R. Hawe. A quantitative measure of fariness and discrimination for resource allocation in shared computer system[R]. Technical Report DED-TR-30. Digital Equipment Corporation. Eastern Research Lan. (1984).

Google Scholar

[2] A. Stefanescu, M.V. Stefanescu. The arbitrated solution for multi-objective convex programming[J]. Rev. Roum. Math. Pure Appl. 1984, 29: 593-598.

Google Scholar

[3] A. Muthoo. Bargaining Theory with Applications [M]. Cambridge Univ. Press, Cambridge U.K. (1999).

Google Scholar

[4] R. Buyya and M. Murshed. Cloud ComputingSim: a toolkit for the modeling and simulation of distributedmanagement and scheduling for Cloud Computing computing [J]. Concurrency and Computation: Practice and Experience, 2002, 14: 13-15.

DOI: 10.1002/cpe.710

Google Scholar

[5] C.H. Papadimitriou and M. Yannakakis. On the approximability of trade-offs and optimal access of web sources[C]. In Proc. of the 41th IEEE Symp. on Foundations of Computer Science, Novermber 2000, 86-92.

DOI: 10.1109/sfcs.2000.892068

Google Scholar