A systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee is proposed to support quality of service （QoS） management on current Internet. An automatic unwatched discretization algorit...A systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee is proposed to support quality of service （QoS） management on current Internet. An automatic unwatched discretization algorithm for discretizing continuous numeric-values is brought forth to reshape these QoS metrics and contexts into their discrete forms. For QoS qualitative diagnosis, causal relationships between a QoS metric and its contexts are exploited with K2 Bayesian network （BN） structure learning by treating QoS metrics and contexts as BN nodes. A QoS metric node is qualitatively diagnosed to be causally related to its parent context nodes. To guarantee QoS quantitatively, those causal relationships are next modeled quantitatively by BN parameter learning. Then, BN inference can be carried out on the BN. Finally, the QoS metric is guaranteed to a specific value with certain probability by tuning its causal contexts to suitable values suggested by the BN inference. Our approach is validated to be sound and effective by simulations on a peer-to-peer （P2P） network.展开更多
Drawing on the contextual history of the Douglas College Sport Science Department （British Columbia, Canada） combinedwith the shifting landscape of physical education/kinesiology, this article traces the development...Drawing on the contextual history of the Douglas College Sport Science Department （British Columbia, Canada） combinedwith the shifting landscape of physical education/kinesiology, this article traces the development of an integrated movement analysiscourse. Specifically, the developed curriculum is detailed and reflection given on the pragmatic, inquiry-based approach used todevelop a course curriculum intended to integrate various kinesiology sub-disciplines. The article explores the combined use of severalframeworks to promote meta-cognition and peer-collaboration as students meet the learning outcomes of creating their ownevidence-based, intradisciplinary process to analyze movement.展开更多
To support quality of service （QoS） management on current Internet working with best effort,we bring forth a systematic approach for end-to-end QoS diagnosis and quantitative guarantee. For QoS diagnosis,we take con...To support quality of service （QoS） management on current Internet working with best effort,we bring forth a systematic approach for end-to-end QoS diagnosis and quantitative guarantee. For QoS diagnosis,we take contexts of a service into consideration in a comprehensive way that is realized by exploiting causal relationships between a QoS metric and its contexts with the help of Bayesian network （BN） structure learning. Context discretization algorithm and node ordering algorithm are proposed to facilitate BN structure learning. The QoS metric is diagnosed to be causally related to its causal contexts,and the QoS metric can be quantitatively guaranteed by its causal contexts. For quantitative QoS guarantee,those causal relationships are first modeled quantitatively by BN parameter learning. Then,the QoS metric is guaranteed to certain value with a probability given its causal contexts tuned to suitable values,that is,quantitative QoS guarantee is reached. Simulations with three sequential stages：context discretization,QoS diagnosis and quantitative QoS guarantee,on a peer-to-peer （P2P） network,are discussed and our approach is validated to be effective.展开更多
基金Supported by the National High Technology Research and Development Program of China （No. 2007AA010302, 2009AA012404） the National Basic Research Program of China （No. 2007CB307103）+1 种基金 the National Natural Science Foundation of China （No. 60432010, 60802034） the Specialized Research Fund for the Doctoral Program of Higher Education （No. 20070013026）.
文摘A systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee is proposed to support quality of service （QoS） management on current Internet. An automatic unwatched discretization algorithm for discretizing continuous numeric-values is brought forth to reshape these QoS metrics and contexts into their discrete forms. For QoS qualitative diagnosis, causal relationships between a QoS metric and its contexts are exploited with K2 Bayesian network （BN） structure learning by treating QoS metrics and contexts as BN nodes. A QoS metric node is qualitatively diagnosed to be causally related to its parent context nodes. To guarantee QoS quantitatively, those causal relationships are next modeled quantitatively by BN parameter learning. Then, BN inference can be carried out on the BN. Finally, the QoS metric is guaranteed to a specific value with certain probability by tuning its causal contexts to suitable values suggested by the BN inference. Our approach is validated to be sound and effective by simulations on a peer-to-peer （P2P） network.
文摘Drawing on the contextual history of the Douglas College Sport Science Department （British Columbia, Canada） combinedwith the shifting landscape of physical education/kinesiology, this article traces the development of an integrated movement analysiscourse. Specifically, the developed curriculum is detailed and reflection given on the pragmatic, inquiry-based approach used todevelop a course curriculum intended to integrate various kinesiology sub-disciplines. The article explores the combined use of severalframeworks to promote meta-cognition and peer-collaboration as students meet the learning outcomes of creating their ownevidence-based, intradisciplinary process to analyze movement.
基金supported by the National Basic Research Program of China (2007CB307103)the National Natural Science Foundation of China (60432010, 60802034)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (20070013026)the Beijing Nova Program (2008B50)
文摘To support quality of service （QoS） management on current Internet working with best effort,we bring forth a systematic approach for end-to-end QoS diagnosis and quantitative guarantee. For QoS diagnosis,we take contexts of a service into consideration in a comprehensive way that is realized by exploiting causal relationships between a QoS metric and its contexts with the help of Bayesian network （BN） structure learning. Context discretization algorithm and node ordering algorithm are proposed to facilitate BN structure learning. The QoS metric is diagnosed to be causally related to its causal contexts,and the QoS metric can be quantitatively guaranteed by its causal contexts. For quantitative QoS guarantee,those causal relationships are first modeled quantitatively by BN parameter learning. Then,the QoS metric is guaranteed to certain value with a probability given its causal contexts tuned to suitable values,that is,quantitative QoS guarantee is reached. Simulations with three sequential stages：context discretization,QoS diagnosis and quantitative QoS guarantee,on a peer-to-peer （P2P） network,are discussed and our approach is validated to be effective.
