Method and apparatus to support application and network awareness of collaborative applications using multi-attribute clustering
First Claim
1. A method of clustering a multi-type vector space in accordance with a plurality of attributes including network attributes and application attributes, the method comprising:
- obtaining the network attributes from a network having a plurality of nodes;
obtaining the application attributes of an application;
obtaining a user'"'"'s communication interest as represented by at least one of;
a user request for a content update or a user subscription to a specific data item or to a set of proximal data sources;
forming a plurality of feature vectors, one for each of the plurality of nodes, where each single one of the plurality of feature vectors is based on the user'"'"'s communication interest, network attributes, and application attributes, such that each single one of the plurality of feature vectors comprises features extracted from a plurality of different types of sources; and
clustering the plurality of nodes based on the plurality of feature vectors, wherein the clustering is performed by a nested method in which one or more of said plurality of nodes are initially clustered based on a sub-set of the plurality of attributes and then re-clustered by iteratively considering additional ones of the plurality of attributes.
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Abstract
A method of clustering communication nodes based on network attributes such as network delays and forwarding capacity; on communication interest attributes; and on application attributes such as quality of service preferences/constraints in providing communications between users and application servers. A multi-attribute communication feature vector is formed. That vector is comprised of network attributes, communication interests attributes, and quality of service requirements and is used to form efficient group communication mechanisms for distributed collaborative applications. Then the multi-attribute communication feature vectors are clustered. The clustering methods for multi-type attribute feature vectors are: iterative clustering using a generalized distance space with normalized attribute subspace metrics; fusion clustering, and nested clustering.
170 Citations
9 Claims
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1. A method of clustering a multi-type vector space in accordance with a plurality of attributes including network attributes and application attributes, the method comprising:
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obtaining the network attributes from a network having a plurality of nodes; obtaining the application attributes of an application; obtaining a user'"'"'s communication interest as represented by at least one of;
a user request for a content update or a user subscription to a specific data item or to a set of proximal data sources;forming a plurality of feature vectors, one for each of the plurality of nodes, where each single one of the plurality of feature vectors is based on the user'"'"'s communication interest, network attributes, and application attributes, such that each single one of the plurality of feature vectors comprises features extracted from a plurality of different types of sources; and clustering the plurality of nodes based on the plurality of feature vectors, wherein the clustering is performed by a nested method in which one or more of said plurality of nodes are initially clustered based on a sub-set of the plurality of attributes and then re-clustered by iteratively considering additional ones of the plurality of attributes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer readable storage device containing an executable program for clustering a multi-type vector space in accordance with a plurality of attributes including network attributes and application attributes, where the program performs steps comprising:
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obtaining the network attributes from a network having a plurality of nodes; obtaining the application attributes of an application; obtaining user'"'"'s communication interest as represented by at least one of;
a user request for a content update or a user subscription to a specific data item or to a set of proximal data sources;forming a plurality of feature vectors, one for each of the plurality of nodes, where each single one of the plurality of feature vectors is based on the user'"'"'s communication interest, network attributes, and application attributes, such that each single one of the plurality of feature vectors comprises features extracted from a plurality of different types of sources; and clustering the plurality of nodes based on the plurality of feature vectors, wherein the clustering is performed by a nested method in which one or more of said plurality of nodes are initially clustered based on a sub-set of the plurality of attributes and then re-clustered by iteratively considering additional ones of the plurality of attributes.
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Specification