Method For Modeling User Behavior In IP Networks

• US 20100268524A1
• Filed: 04/16/2010
• Published: 10/21/2010
• Est. Priority Date: 04/17/2009
• Status: Active Grant

• Current Assignee: Empirix Incorporated (Empirix Holdings I, Inc.)
• Original Assignee: Empirix Incorporated (Empirix Holdings I, Inc.)
• Inventors: Nath, Anuj, Turgeon, Anne-Marie, Hsing-Wang Wong, David, Ivanyi, Tibor, Eidelman, Sergey
• Granted Patent: US 10,326,848 B2
• Time in Patent Office: Days
• US Class Current: 703/23
• CPC Class Codes: H04L 63/123 received data contents, e.g. message integrity
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A method of modeling user behavior in an IP network, comprising the steps of allowing a testing user to create a behavior profile specifying one or more user activities from a plurality of user activities performed by a user over the IP network, one or more bandwidth usage levels corresponding to the one or more user activities, and an importance level associated with each user activity; associating one or more applications from a plurality of applications with the one or more user activities; emulating the behavior profile over the IP network by performing the one or more user activities using the one or more applications; and measuring a quality of experience (QoE) score for each user activity from the one or more user activities.

Claims

1. A method of modeling user behavior in an IP network, comprising the steps of:

allowing a testing user to create a behavior profile specifying one or more user activities from a plurality of user activities performed by a user over the IP network, and one or more bandwidth usage levels corresponding to the one or more user activities;

associating one or more applications from a plurality of applications with the one or more user activities;

emulating the behavior profile over the IP network by performing the one or more user activities using the one or more applications;

measuring a quality of experience (QoE) score for each user activity from the one or more user activities; and

combining the QoE score for each user activity from the one or more user activities and generating an overall QoE score for the behavior profile.

2. The method as recited in claim 1, wherein the step of emulating the behavior profile is performed in real-time.

3. The method as recited in claim 1, further comprising the step of allowing the testing user to specify the one or more applications used to perform the one or more user activities.

4. The method as recited in claim 1, further comprising the step of allowing the testing user to specify an operating system used to emulate the one or more applications.

5. The method as recited in claim 4, wherein the one or more applications are determined based on the operating system.

6. The method as recited in claim 1, wherein the behavior profile further comprises an average bandwidth usage level for each application from the one or more applications.

7. The method as recited in claim 1, wherein the behavior profile further comprises a maximum bandwidth usage level for each application from the one or more applications.

8. The method as recited in claim 1, wherein the behavior profile further comprises a minimum bandwidth usage level for each application from the one or more applications.

9. The method as recited in claim 1, wherein the behavior profile further comprises one or more from the group consisting of an average bandwidth usage level, a maximum bandwidth usage level, and a minimum bandwidth usage level.

10. The method as recited in claim 1, wherein the one or more bandwidth usage levels are specified as a percentage, the one or more bandwidth usage levels adding up to no more than 100 percent.

11. The method as recited in claim 1, wherein the behavior profile further comprises an application bandwidth usage level for each application from the one or more applications.

12. The method as recited in claim 1, wherein the one or more bandwidth usage levels fluctuate over a range of bandwidth levels.

13. The method as recited in claim 12, wherein the range of bandwidth levels is defined by a probability distribution.

14. The method as recited in claim 1, wherein the one or more user activities are selected from the group consisting of web browsing, sending email, receiving email, downloading a first file, uploading a second file, peer to peer file sharing, Voice over Internet Protocol (VOIP), online gaming, media streaming, and secure session.

15. The method as recited in claim 1, wherein the behavior profile further comprises a QoE importance level for each user activity from the one or more user activities.

16. The method as recited in claim 15, wherein the step of allowing a testing user to create a behavior profile further comprises the step of allowing the testing user to specify the QoE importance level for each user activity.

17. The method as recited in claim 15, wherein the step of measuring a QoE score further comprises the step of adjusting the QoE score of each user activity from the one or more user activities based on the QoE importance level for each user activity from the one or more user activities.

18. The method as recited in claim 1, further comprising the step of allowing the testing user to define a new user activity for the behavior profile.

19. The method as recited in claim 18, further comprising the step of allowing the testing user to specify a new application associated with the new user activity.

20. The method as recited in claim 18, further comprising the step of allowing the testing user to specify a protocol used by the new user activity.

21. The method as recited in claim 18, further comprising the step of allowing the testing user to specify a script emulating the traffic associated with the new user activity.

22. The method as recited in claim 1, further comprising the step of allowing an emulation of the behavior profile to fluctuate by varying a value of a parameter of the behavior profile over a range of values.

23. The method as recited in claim 22, wherein the parameter is selected from the group consisting of an application used to perform a user activity, an average user activity bandwidth usage level, a maximum user activity bandwidth level, a minimum user activity bandwidth level, an average application bandwidth usage level, a maximum application bandwidth usage level, a minimum application bandwidth usage level, and an operating system used to emulate the behavior profile.

24. The method as recited in claim 22, wherein the emulation fluctuates equally over the range of values.

25. The method as recited in claim 22, wherein the emulation fluctuates randomly over the range of values.

26. The method as recited in claim 22, wherein the emulation fluctuates over the range of values using a probability distribution.

27. The method as recited in claim 1, wherein an application from the one or more applications is emulated by executing a set of steps similar to the execution of the application.

28. The method as recited in claim 1, further comprising the step of emulating a use of encryption by a set of applications from the one or more applications.

29. The method as recited in claim 1, further comprising the step of emulating a use of onion routing by a set of applications from the one or more applications.

30. The method as recited in claim 1, further comprising the step of emulating the use of anonymization by a set of applications from the one or more applications.

31. The method as recited in claim 1, wherein the one or more user activities are emulated in parallel.

32. The method as recited in claim 1, the one or more applications are emulated in parallel according to a set of parallel association rules.

33. The method as recited in claim 32, wherein the set of parallel association rules includes random starting times for the one or more applications.

34. The method as recited in claim 32, wherein the set of parallel association rules includes conditional statements.

35. The method as recited in claim 32, wherein the set of parallel association rules includes one or more time events associated with the one or more applications.

36. The method as recited in claim 1, further comprising the step of emulating a plurality of instances of the behavior profile over the IP network.

37. The method as recited in claim 1, further comprising the steps of:

allowing the testing user to create a plurality of behavior profiles; and

emulating a plurality of instances of each behavior profile from the plurality of behavior profiles.

38. The method as recited in claim 37, further comprising the step of measuring a QoE score for each user activity from the one or more user activities of each instance from the plurality of instances of each behavior profile from the plurality of behavior profiles.

39. The method as recited in claim 1, wherein the step of combining the QoE score further comprises the step of combining the QoE score for each user activity using a linear weighted sum.