NEXT-GENERATION BANDWIDTH MANAGEMENT CONTROL SYSTEMS FOR MULTIPLE-SERVICE CALLS, SESSIONS, PACKET-LEVEL PROCESSES, AND QoS PARAMETERS - PART 1: STRUCTURAL AND FUNCTIONAL ARCHITECTURES

• US 20130003543A1
• Filed: 07/02/2012
• Published: 01/03/2013
• Est. Priority Date: 06/30/2011
• Status: Abandoned Application

• Current Assignee: Avistar Communications Corporation (Avistar)
• Original Assignee: Avistar Communications Corporation (Avistar)
• Inventors: LUDWIG, Lester F.
• Application Number:
  US13/540,545
• Publication Number:
  US 20130003543A1
• Time in Patent Office: Days
• US Class Current: 370/230
• CPC Class Codes: H04L 47/762 triggered by the network
• CPC Class Codes: H04L 47/78 Architectures of resource allocation
• CPC Class Codes: H04L 47/822 Collecting or measuring resource availability data
• CPC Class Codes: H04L 47/824 Applicable to portable or mobile terminals

Abstract

System and method for addressing immense, long-standing problem of bandwidth management, for example, in enterprise networks, VPNs, real-time and stored video services, mobile applications, wireless networks, and cloud computing applications. Described features include an automatic closed-loop control system infrastructure encompassing multiple time-scales and performing control actions optimized to the extent possible with respect to administrator-provided performance metrics. One aspect utilizes available or innovatively accessible means of session and QoS control (settings in configuration files, gateway APIs, QoS parameters, application bit-rate settings, etc.) within the context of practical multiple-vendor products in evolving multiple-service networks. Another aspect utilizes available or innovatively accessible means of session and QoS observations (values in reporting log files, gateway APIs, network monitoring, etc.) within the context of practical multiple-vendor products in evolving multiple-service networks. Traffic-measurement controlled adaptive reservations for distributed myopic single-service gatekeepers effectively shapes the permitted state-space boundary over a range of arbitrary curvatures.

Claims

1. A real-time control environment for managing bandwidth allocation in a bandwidth-constrained network, the control environment comprising:

means of observing at least one session-level network performance parameter of a session-level bandwidth allocation process, the session-level network performance parameter comprising an associated value;

means of observing at least one QoS-level network performance parameter of a QoS-level bandwidth allocation process, the QoS-level network performance parameter comprising an associated value;

means of adjusting at least one session-level control parameter of the session-level bandwidth allocation process;

means of adjusting at least one QoS-level control parameter of the QoS-level bandwidth allocation process; and

a real-time control system, the control system comprising a first operation comprising a first time-scale associated with sessions, a second operation comprising a second time-scale associated with packets, and a third operation comprising both the first time-scale and the second time-scale,

wherein the real-time control system performs control actions by adjusting the at least one of the session-level control parameter and the QoS-level control parameter;

wherein the control actions comprise utilizing the value of the session-level network performance parameter and the value of the QoS-level network performance parameter as inputs to real-time control calculations, the real-time control calculations comprising the first operation, second operation, and third operation, and

wherein the adjusting the at least one of the session-level control parameter is responsive to the first operation and third operation, and the adjusting the at least one of the QoS-level control parameter is responsive to the second operation and third operation.

2. The real-time control environment of claim 1, wherein the bandwidth-constrained network is an enterprise network.

3. The real-time control environment of claim 1, wherein the bandwidth-constrained network is the interconnection network for a computing cloud.

4. The real-time control environment of claim 1, wherein the bandwidth-constrained network is a wireless network.

5. The real-time control environment of claim 1, wherein the bandwidth-constrained network is a mobile network.

6. The real-time control environment of claim 1, wherein the means of observing the at least one session-level network performance parameter comprises network monitoring.

7. The real-time control environment of claim 1, wherein the means of observing the at least one QoS-level network performance parameter comprises network monitoring.

8. The real-time control environment of claim 1, wherein the means of adjusting the at least one session-level network performance parameter comprises communications utilizing an API of a router.

9. The real-time control environment of claim 1, wherein the means of adjusting the at least one session-level network performance parameter comprises communications utilizing configuration file of a router.

10. The real-time control environment of claim 1, wherein the means of observing the at least one QoS-level network performance parameter comprises communications utilizing an API of a router.

11. The real-time control environment of claim 1, wherein the means of adjusting the at least one session-level network performance parameter comprises communications utilizing an API of a switch.

12. The real-time control environment of claim 1, wherein the means of adjusting the at least one session-level network performance parameter comprises communications utilizing a configuration file of a switch.

13. The real-time control environment of claim 1, wherein the means of observing the at least one QoS-level network performance parameter comprises communications utilizing an API of a switch.

14. The real-time control environment of claim 1, wherein the means of observing the at least one QoS-level network performance parameter comprises communications utilizing an API of a gatekeeper.

15. The real-time control environment of claim 1, wherein the means of observing the at least one QoS-level network performance parameter comprises communications utilizing a configuration file of a gatekeeper.

16. The real-time control environment of claim 1, wherein the means of adjusting the at least one session-level network performance parameter comprises communications utilizing an API of a gatekeeper.

17. The real-time control environment of claim 1, wherein the means of adjusting the at least one session-level network performance parameter comprises communications utilizing an API of a service-specific communications manager.

18. The real-time control environment of claim 1, wherein the means of observing the at least one QoS-level network performance parameter comprises communications utilizing an API of a service-specific communications manager.

19. The real-time control environment of claim 1, wherein the wherein the real-time control calculations are responsive to the rate-of-change of the at least one session-level network performance parameter;

20. The real-time control environment of claim 1, wherein the wherein the real-time control calculations are responsive to the rate-of-change of the at least one QoS-level network performance parameter;

21. The real-time control environment of claim 1, wherein the real-time control calculations further comprise a PID controller, wherein the adjusting of at least one of the session-level control parameter and the QoS-level control parameter is responsive to the output of the PID controller.

22. The real-time control environment of claim 1, wherein the real-time control calculations further comprise a linear predictor, wherein the adjusting of at least one of the session-level control parameter and the QoS-level control parameter is responsive to the output of the linear predictor.

23. The real-time control environment of claim 1, wherein the real-time control calculations further comprise a nonlinear predictor, wherein the adjusting of at least one of the session-level control parameter and the QoS-level control parameter is responsive to the output of the nonlinear predictor.

24. The real-time control environment of claim 1, wherein the real-time control calculations further comprise a Gaussian Kalman filter and wherein the adjusting of at least one of the session-level control parameter and the QoS-level control parameter is responsive to the output of the Gaussian Kalman filter.

25. The real-time control environment of claim 1, wherein the real-time control calculations further comprise a non-Gaussian Kalman filter and wherein the adjusting of at least one of the session-level control parameter and the QoS-level control parameter is responsive to the output of the non-Gaussian Kalman filter.

26. The real-time control environment of claim 1, wherein the real-time control calculations further comprise a time-driven ramp generator and wherein the adjusting of at least one of the session-level control parameter and the QoS-level control parameter is responsive to the output of the time-driven ramp generator.

27. The real-time control environment of claim 1, wherein the real-time control calculations further comprise a dithering function and wherein the adjusting of at least one of the session-level control parameter and the QoS-level control parameter is responsive to the output of the dithering function.

28. The real-time control environment of claim 1, wherein the real-time control calculations further comprise a quantization process and wherein the adjusting of at least one of the session-level control parameter and the QoS-level control parameter is responsive to the output of the quantization process.

29. The real-time control environment of claim 1, wherein the real-time control calculations further comprise hysteresis and wherein the adjusting of at least one of the session-level control parameter and the QoS-level control parameter is responsive to the output of the hysteresis process.