Methods and system for modeling network traffic
First Claim
1. A method for modeling network traffic comprising:
- decomposing the network traffic into a plurality of categories for training an artificial neural network with the network traffic within a category being different than the network traffic of other categories;
defining a plurality of inputs which permit prediction of network traffic to be determined from a respective combination of inputs; and
constructing the artificial neural network based on the categories of network traffic such that the artificial neural network is configured to evaluate the plurality of inputs and develop a plurality of outputs that represent a prediction of bandwidth profile for the related inputs, wherein each output represents the prediction of the bandwidth profile for a respective category, and wherein the plurality of outputs generated by the artificial neural network to provide a model of the network traffic, and wherein the plurality of outputs generated by the artificial neural network are aggregated to estimate a required network capacity.
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Abstract
A method and system are provided for modeling network traffic in which an artificial neural network architecture is utilized in order to intelligently and adaptively model the capacity of a network. Initially, the network traffic is decomposed into a plurality of categories, such as individual users, application usage or common usage groups. Inputs to the artificial neural network are then defined such that a respective combination of inputs permits prediction of bandwidth capacity needs for that input condition. Outputs of the artificial neural network are representative of the network traffic associated with the respective inputs. For example, a plurality of bandwidth profiles associated with respective categories may be defined. An artificial neural network may then be constructed and trained with those bandwidth profiles and then utilized to relate predict future bandwidth needs for the network.
20 Citations
22 Claims
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1. A method for modeling network traffic comprising:
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decomposing the network traffic into a plurality of categories for training an artificial neural network with the network traffic within a category being different than the network traffic of other categories; defining a plurality of inputs which permit prediction of network traffic to be determined from a respective combination of inputs; and constructing the artificial neural network based on the categories of network traffic such that the artificial neural network is configured to evaluate the plurality of inputs and develop a plurality of outputs that represent a prediction of bandwidth profile for the related inputs, wherein each output represents the prediction of the bandwidth profile for a respective category, and wherein the plurality of outputs generated by the artificial neural network to provide a model of the network traffic, and wherein the plurality of outputs generated by the artificial neural network are aggregated to estimate a required network capacity. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for modeling network traffic comprising:
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providing an artificial neural network to evaluate a plurality of inputs and produce a plurality of outputs, wherein each output is representative of a predicted bandwidth profile related to the respective inputs; receiving a plurality of attributes representative of the network traffic at the inputs of the artificial neural network, wherein receiving the plurality of attributes comprises receiving attributes representative of the network traffic following decomposition into a plurality of categories with the network traffic within a category being different than the network traffic of other categories; processing the plurality of attributes with the artificial neural network in order to generate the plurality of outputs, wherein each output represents the prediction of the bandwidth profile for a respective category;
generating a model of the network traffic based upon the plurality of outputs; andaggregating the plurality of outputs generated by the artificial neural network to estimate a required network capacity. - View Dependent Claims (9, 10, 11, 12, 14, 15)
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13. A system for modeling network traffic comprising:
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a memory device configured to store a representation of an artificial neural network which relates a plurality of inputs to a plurality of outputs, wherein each output is representative of a predicted capacity bandwidth profile; and a processor configured to receive a plurality of attributes representative of the network traffic as inputs to the artificial neural network with the plurality of attributes comprising attributes representative of the network traffic following decomposition into a plurality of categories with the network traffic within a category being different than the network traffic of other categories, the processor also configured to process the plurality of attributes with the artificial neural network in order to generate the plurality of outputs with each output representing the prediction of the bandwidth profile for a respective category, the processor further configured to generate a model of the network traffic based upon the plurality of outputs, and the processor further configured to aggregate the plurality of outputs generated by the artificial neural network to estimate a required network capacity. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22)
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Specification