System and method for modeling video network reliability

• US 8,291,015 B2
• Filed: 10/29/2001
• Issued: 10/16/2012
• Est. Priority Date: 10/29/2001
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Stephens, James H. Jr.
• Primary Examiner(s): SHAW, PELING ANDY
• Application Number:
  US10/045,303
• Publication Number:
  US 20030084102A1
• Time in Patent Office: 4,005 Days
• Field of Search: 709/204, 715719-722, 715/753
• US Class Current: 709/204
• CPC Class Codes: H04L 41/0213 Standardised network management protocols, e.g. simple network management protocol [SNMP]
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 65/80 Responding to QoS
• CPC Class Codes: H04N 17/004 for digital television systems

Abstract

A program product for modeling video network reliability includes a computer-usable medium that encodes computer instructions. The computer instructions cause a data processing system to perform modeling operations. Those operations include obtaining historical data for multiple video conferences, and executing a modeling algorithm that produces a model representing the historical data. The model can be analyzed to identify opportunities for improving reliability of a video network. In an example embodiment, the computer instructions output results that a user can use reconfigure the video network for improved reliability. In another embodiment, the computer instructions analyze the model to identify opportunities for improving reliability of the video network, and the computer instructions automatically reconfigure the video network, based on the identified opportunities. The modeling algorithm may be a decision tree algorithm, such as the ID3-based algorithm known as C4.5.

Claims

1. A method for modeling video conferencing network reliability, the method comprising:

obtaining historical data for multiple video conferences;

storing said historical data in a call history table, said historical data including video conferencing equipment vendor or model identification information;

executing a modeling algorithm that produces a model representing the historical data, which includes executing a decision tree algorithm;

analyzing the model to identify characteristics associated with undesirable outcomes for the video conferences;

configuring a video conferencing network to avoid at least one of the identified characteristics associated with undesirable outcomes; and

conducting a new video conference with the video conferencing network configured to avoid at least one of the identified characteristics associated with undesirable outcomes.

2. The method of claim 1, wherein the operation of executing a decision tree algorithm comprises executing an ID3-based algorithm.

3. The method of claim 1, further comprising:

updating the historical data to create new historical data that includes values representing characteristics of the new video conference;

executing the modeling algorithm to produce a new model representing the new historical data;

analyzing the new model to produce a result; and

reconfiguring the video conferencing network according to the result.

4. The method of claim 1, further comprising:

evaluating the model to determine whether the model provides a desired level of efficacy; and

in response to determining that the model does not provide a desired level of efficacy, using a different modeling algorithm to produce a different model.

5. The method of claim 1, wherein:

the method further comprises building a training set from the historical data;

the operation of executing the modeling algorithm comprises applying the modeling algorithm to the training set; and

the operation of analyzing the model comprises;

deriving a rule set from the model; and

analyzing the rule set to identify the characteristics associated with undesirable outcomes for the video conferences.

6. The method of claim 5, wherein:

the historical data includes attribute values for attributes of each video conference and an outcome value representing an outcome for each video conference; and

the operation of applying the modeling algorithm to the training set comprises;

using the outcome values as categorical attributes for the modeling algorithm; and

using the attribute values as non-categorical attributes for the modeling algorithm.

7. The method of claim 5, wherein:

the operation of obtaining historical data for multiple video conferences comprises obtaining a first endpoint identifier, a first endpoint vendor, a second endpoint identifier, a second endpoint vendor, and an outcome value for the multiple video conferences;

the operation of building a training set comprises including the first endpoint identifier, the first endpoint vendor, the second endpoint identifier, the second endpoint vendor, and the outcome value for the multiple video conferences in the training set; and

the operation of executing the modeling algorithm comprises using the first endpoint identifier, the first endpoint vendor, the second endpoint identifier, the second endpoint vendor, and the outcome value for the multiple video conferences to produce the model.

8. The method of claim 5, wherein:

the training set includes values representing a first set of attributes; and

the method further comprises;

evaluating the model to determine whether the model provides a desired level of efficacy;

in response to determining that the model does not provide a desired level of efficacy, building a different training set that includes a different set of attributes; and

applying the modeling algorithm to the different training set to produce a different model.

9. A computer storage medium storing instructions, which when executed by a computing device, causes the computing device to perform functions comprising:

obtaining historical data for multiple video conferences;

storing said historical data in a call history table, said historical data including vendor or model identification information; and

executing a modeling algorithm that produces a model representing the historical data, which includes executing a decision tree algorithm;

analyzing the model to identify characteristics associated with undesirable outcomes for the video conferences;

configuring a video conferencing network to avoid at least one of the identified characteristics associated with undesirable outcomes; and

conducting a new video conference with the video conferencing network configured to avoid at least one of the identified characteristics associated with undesirable outcomes.

10. The computer storage medium of claim 9, wherein the functions further comprise:

outputting results that reveal at least one of the opportunities for improving reliability of the video conferencing network, such that a user can reconfigure the video conferencing network, based on the results, to improve reliability of the video conferencing network.

11. The computer storage medium of claim 9, wherein the functions further comprise:

analyzing the model to identify the one or more opportunities for improving reliability of the video conferencing network; and

automatically reconfiguring the video conferencing network, based on the identified opportunities, to improve reliability of the video conferencing network.

12. The computer storage medium of claim 9, wherein:

the executing the decision tree algorithm comprises executing an ID3-based algorithm.

13. The computer storage medium of claim 9, wherein the functions further comprise:

updating the historical data to create new historical data that includes values representing characteristics of a new video conference;

executing the modeling algorithm to produce a new model representing the new historical data;

analyzing the new model to produce a result; and

reconfiguring the video conferencing network according to the result to improve reliability of the video conferencing network.

14. The computer storage medium of claim 9, wherein the functions further comprise:

building a training set from the historical data;

executing the modeling algorithm by applying the modeling algorithm to the training set; and

deriving a rule set from the model, such that the one or more opportunities for improving reliability of a video conferencing network can be identified with the rule set.

15. The computer storage medium of claim 14, wherein:

the historical data includes attribute values for attributes of each video conference and an outcome value representing an outcome for each video conference;

the modeling algorithm uses the outcome values as categorical attributes; and

the modeling algorithm uses the attribute values as non-categorical attributes.

16. The computer storage medium of claim 14, wherein the functions further comprise:

obtaining a first endpoint identifier, a first endpoint vendor, a second endpoint identifier, a second endpoint vendor, and an outcome value for the multiple video conferences;

storing in the training set the first endpoint identifier, the first endpoint vendor, the second endpoint identifier, the second endpoint vendor, and the outcome value for the multiple video conferences; and

using, by the modeling algorithm, the first endpoint identifier, the first endpoint vendor, the second endpoint identifier, the second endpoint vendor, and the outcome value for the multiple video conferences to produce the model.

17. A data processing system for modeling video conferencing network reliability, the data processing system comprising:

one or more processing units; and

a computer storage medium storing instructions, which when executed by the one or more processing units, causes the one or more processing units to perform functions including

obtaining historical data for multiple video conferences;

storing said historical data in a call history table, said historical data including vendor or model identification information; and

executing a modeling algorithm that produces a model representing the historical data, which includes executing a decision tree algorithm;

analyzing the model to identify characteristics associated with undesirable outcomes for the video conferences;

configuring a video conferencing network to avoid at least one of the identified characteristics associated with undesirable outcomes; and

conducting a new video conference with the video conferencing network configured to avoid at least one of the identified characteristics associated with undesirable outcomes.