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- Publisher Website: 10.1109/ICCS.2008.4737189
- Scopus: eid_2-s2.0-62949128060
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Conference Paper: A nature-inspired algorithm for intelligent optimization of network resources
Title | A nature-inspired algorithm for intelligent optimization of network resources |
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Authors | |
Keywords | Distributed And Parallel Algorithm Dynamical Process Economic Generalized Particle Model (Egpm) Intelligent Bandwidth Allocation Price And Demands Dynamic Modulation |
Issue Date | 2008 |
Citation | 2008 11Th Ieee Singapore International Conference On Communication Systems, Iccs 2008, 2008, p. 284-288 How to Cite? |
Abstract | In complex computer networks having the characteristic of social dynamics, bandwidth allocation is a fundamental problem where bandwidth has to be reserved for connections in advance. This paper presents the theory and approach of the economic generalized particle model (EGPM) for intelligent allocation of network bandwidth. This approach transforms the complicated network bandwidth allocation problem into efficient, parallel allocation of network bandwidth. This approach is an important extension and further development of the generalized particle model (GPM) [1]. EGPM emphasizes the use of pricing as the network control mechanism. For the pricing, it makes use of the tatonnement process in economics. EGPM arises from GPM but can overcome some of GPM's deficiencies for the network bandwidth allocation problem. © 2008 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/151941 |
References |
DC Field | Value | Language |
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dc.contributor.author | Feng, X | en_US |
dc.contributor.author | Lau, FCM | en_US |
dc.contributor.author | Shuai, D | en_US |
dc.date.accessioned | 2012-06-26T06:31:15Z | - |
dc.date.available | 2012-06-26T06:31:15Z | - |
dc.date.issued | 2008 | en_US |
dc.identifier.citation | 2008 11Th Ieee Singapore International Conference On Communication Systems, Iccs 2008, 2008, p. 284-288 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/151941 | - |
dc.description.abstract | In complex computer networks having the characteristic of social dynamics, bandwidth allocation is a fundamental problem where bandwidth has to be reserved for connections in advance. This paper presents the theory and approach of the economic generalized particle model (EGPM) for intelligent allocation of network bandwidth. This approach transforms the complicated network bandwidth allocation problem into efficient, parallel allocation of network bandwidth. This approach is an important extension and further development of the generalized particle model (GPM) [1]. EGPM emphasizes the use of pricing as the network control mechanism. For the pricing, it makes use of the tatonnement process in economics. EGPM arises from GPM but can overcome some of GPM's deficiencies for the network bandwidth allocation problem. © 2008 IEEE. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | 2008 11th IEEE Singapore International Conference on Communication Systems, ICCS 2008 | en_US |
dc.subject | Distributed And Parallel Algorithm | en_US |
dc.subject | Dynamical Process | en_US |
dc.subject | Economic Generalized Particle Model (Egpm) | en_US |
dc.subject | Intelligent Bandwidth Allocation | en_US |
dc.subject | Price And Demands Dynamic Modulation | en_US |
dc.title | A nature-inspired algorithm for intelligent optimization of network resources | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Lau, FCM:fcmlau@cs.hku.hk | en_US |
dc.identifier.authority | Lau, FCM=rp00221 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1109/ICCS.2008.4737189 | en_US |
dc.identifier.scopus | eid_2-s2.0-62949128060 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-62949128060&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.spage | 284 | en_US |
dc.identifier.epage | 288 | en_US |
dc.identifier.scopusauthorid | Feng, X=55200149100 | en_US |
dc.identifier.scopusauthorid | Lau, FCM=7102749723 | en_US |
dc.identifier.scopusauthorid | Shuai, D=7003359432 | en_US |