Network congestion prediction

• US 9,197,565 B2
• Filed: 04/29/2013
• Issued: 11/24/2015
• Est. Priority Date: 04/27/2012
• Status: Active Grant

• Current Assignee: Video Solutions Pte. Ltd. (Transpacific IP Group Limited)
• Original Assignee: Magor Communications Corporation (Constellation Software Incorporated)
• Inventors: Khanchi, Aziz, Shirmohammadi, Shervin, Semsarzadeh, Mehdi, Javadtalab, Abbas
• Primary Examiner(s): Duong, Frank
• Application Number:
  US13/872,376
• Publication Number:
  US 20130286837A1
• Time in Patent Office: 939 Days
• Field of Search: 370/229, 370/230, 370/235, 3703955-39552
• US Class Current: 1/1
• CPC Class Codes: H04L 47/11 Identifying congestion
• CPC Class Codes: H04L 47/127 by using congestion prediction
• CPC Class Codes: H04L 47/263 Rate modification at the source after receiving feedback

Abstract

To control the transmission of real time transport packets over a network, incoming packets received over the network from a transmitter are monitored. A statistical analysis is performed on the received packets to determine network congestion. A message is sent to the transmitter to control a transmission parameter in accordance with the determined network congestion.

Claims

1. A method of controlling the transmission of real time transport packets over a network, comprising:

monitoring incoming packets received over the network from a transmitter;

performing a statistical analysis of the received packets to determine network congestion; and

sending a message to the transmitter to control a transmission parameter in accordance with the determined network congestion.

2. A method as claimed in claim 1, wherein statistical analysis is performed using Bayesian statistics.

3. A method as claimed in claim 1, wherein the statistical analysis is performed on weighted values of packet inter-arrival times D_i.

4. A method as claimed in claim 1, wherein the statistical analysis is performed on a parameter J_i defined by the expression

5. A method as claimed in claim 4, wherein the statistical analysis is performed on the basis that the parameter J_i follows a Pareto distribution with fixed minimum and shape parameters.

6. A method as claimed in claim 5, comprising computing a shape parameter for the Pareto distribution, computing moving averages for the shape parameter, and sending said message when the difference between said moving averages exceed defined bounds.

7. A method as claimed in claim 6, wherein when the ith packet arrives (i>

n2) the difference is computed as

8. A method as claimed in claim 7, wherein the shape parameter for the Pareto distribution is updated upon the arrival of each packet, and a new estimate of the shape parameter computed in accordance with the equation:

ρ

_n=E(ρ

)=(α

₀+n)b_n

where ρ

_n is the expectation value of the shape parameter after the arrival of the n^th packet.

9. A method as claimed in claim 1, wherein the transmission parameter comprises the bit rate.

10. A method as claimed in claim 1, wherein the transmission parameter comprises a coding parameter.

11. A real time communication system using a real time transport protocol, comprising:

a receiver for receiving the packets over a network from a remote transmitter and outputting a data stream; and

a network congestion computation module at the receiver for determining network congestion based on incoming packets, said network congestion computation module being configured to perform a statistical analysis of the received packets to determine network congestion and to send a message to the transmitter to control a transmission parameter in accordance with the determined network congestion.

12. A real time communication system as claimed in claim 11, wherein the network congestion module is configured to perform said statistical analysis is performed using Bayesian statistics.

13. A real time communication system as claimed in claim 11, wherein the network congestion module is configured to perform the statistical analysis on weighted values of packet inter-arrival times D_i.

14. A real time communication system as claimed in claim 13, wherein the statistical analysis is performed on a parameter J_i defined by the expression

15. A real time communication system as claimed in claim 14, wherein the statistical analysis is performed on the basis that the parameter J_i follows a Pareto distribution with fixed minimum and shape parameters.

16. A real time communication system as claimed in claim 15, wherein the network congestion module is configured to compute a shape parameter for the Pareto distribution, compute moving averages for the shape parameter, and send said message when the difference between said moving averages exceed defined bounds.

17. A real time communication system as claimed in claim 16, wherein when the ith packet arrives (i>

n2) the network congestion module is configured to compute the difference Diff as

18. A real time communication system as claimed in claim 17, wherein the network congestion module is configured to update the shape parameter for the Pareto distribution upon the arrival of each packet, and a new estimate of the shape parameter computed in accordance with the equation:

ρ

_n=E(ρ

)=(α

₀+n)b_n

where ρ

_n is the expectation value of the shape parameter after the arrival of the n^th packet.

19. A real time communication system as claimed in any one of claims 11 to 18, wherein the transmission parameter comprises the bit rate.

20. A real time communication system as claimed in any one of claims 11 to 18, wherein the transmission parameter comprises a coding parameter.