COGNITIVE NETWORK LOAD PREDICTION METHOD AND APPARATUS

• US 20120020216A1
• Filed: 01/11/2011
• Published: 01/26/2012
• Est. Priority Date: 01/15/2010
• Status: Abandoned Application

• Current Assignee: TT Government Solutions, Inc. (Perspecta, Inc.)
• Original Assignee: Telcordia Technologies Incorporated (Telefonaktiebolaget LM Ericsson)
• Inventors: Vashist, Akshay, Poylisher, Alexander, Mau, Siun-Chuon, Ghosh, Abhrajit, Chadha, Ritu
• Application Number:
  US13/004,324
• Publication Number:
  US 20120020216A1
• Time in Patent Office: Days
• US Class Current: 370/235
• CPC Class Codes: H04L 41/14 Network analysis or design
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/149 for prediction of maintenance
• CPC Class Codes: H04L 47/10 Flow control; Congestion control
• CPC Class Codes: H04L 47/127 by using congestion prediction
• CPC Class Codes: H04L 47/25 with rate being modified by the source upon detecting a change of network conditions
• CPC Class Codes: H04W 28/0284 detecting congestion or overload during communication monitoring arrangements H04L43/00
• CPC Class Codes: H04W 8/04 Registration at HLR or HSS [Home Subscriber Server]

Abstract

Loads for a wireless network having a plurality of end nodes are predicted by constructing a computer data set of end-to-end pairs of the end nodes included in the network using a computer model of the network; constructing a computerized set of observables from social information about users of the network; developing a computerized learned model of predicted traffic using at least the data set and the observables; and using the computerized learned model to predict future end-to-end network traffic.

Claims

1. A method for predicting loads for a wireless network having a plurality of end nodes, comprising:

constructing a computer data set of end-to-end pairs of said end nodes included in said network using a computer model of said network;

constructing a computerized set of observables from social information about users of the network derived from outside the network itself;

developing a computerized learned model of predicted traffic using at least said data set and said observables; and

using said computerized learned model to predict future end-to-end network traffic.

2. The method of claim 1 further using historical traffic data to develop said computerized learned model.

3. The method of claim 1 further comprising:

modifying said network to reduce future network congestion by applying said prediction to said network.

4. The method of claim 1 further comprising:

obtaining at least one of new network information and new social information about users of the network and applying that new information to said computerized learned model to predict future end-to-end network traffic.

5. A non-transitory computer-readable storage medium comprising instructions that, when executed in a system, cause the system to perform a method for predicting loads for a wireless network having a plurality of end nodes, the method comprising the steps of:

constructing a computer data set of end-to-end pairs of the end nodes included in the network using a computer model of the network;

constructing a computerized set of observables from social information about users of the network;

developing a computerized learned model of predicted traffic using at least the data set and the observables; and

using the computerized learned model to predict future end-to-end network traffic.