Smart Data Cap Avoidance with Personalized Predictions Based on Linear Regression or Historical Usage Alpha-Generation Patterns

Patent metadata

Field	Value
Publication number	US-2018013629-A1
Application number	US-201715437118-A
Country	US
Kind code	A1
Filing date	Feb 20, 2017
Priority date	Jul 7, 2016
Publication date	Jan 11, 2018
Grant date	—

Abstract

A system, apparatus, and method for controlling data usage at a customer premises. A gateway configured as a single point of entry receives all data entering the premises. A DataCap Management Unit monitors all data usage by client devices within the premises and uses either a linear regression model or a historical usage alpha-generation method to analyze data usage patterns at the premises and to predict future data usage at the premises for a current billing cycle. The DataCap Management Unit dynamically adjusts a premises bandwidth cap throughout the current billing cycle, using the predicted future data usage as an input, to smoothly keep actual total data usage for the current billing cycle from exceeding a data cap before the current billing cycle ends.

First claim

What is claimed is:

1 . A computer-controlled method of controlling data usage at a customer premises, the method comprising:
predicting future data usage at the customer premises for a current billing cycle; monitoring all actual data usage at a single point of entry for the premises; and dynamically adjusting a premises bandwidth cap throughout the current billing cycle, using the predicted future data usage as an input, to smoothly keep actual total data usage for the current billing cycle from exceeding a data cap before the current billing cycle ends.

2 . The method according to claim 1 , wherein predicting future data usage includes generating a linear regression model for future data usage at the customer premises based on past data usage at the premises.

3 . The method according to claim 2 , further comprising updating the linear regression model by recalculating coefficients as more recent data usage information becomes available.

4 . The method according to claim 2 , wherein the linear regression model utilizes a least square method.

5 . The method according to claim 2 , wherein dynamically adjusting the premises bandwidth cap includes:
applying the linear regression model to predict data consumption (Dc) for a current day; applying the linear regression model to predict total data consumption (Dctotal) for remaining days of the current billing cycle; calculating an offset of Dc for an overrun or underrun (Dceffective) by adding to Dc, the value: (data remaining within the data cap for the current billing cycle −Dctotal)/days remaining in the current billing cycle; and calculating an hourly premises bandwidth cap by dividing Dceffective by 24.

6 . The method according to claim 5 , wherein there are a plurality of Adaptive Bitrate (ABR) sessions active at the premises, and the method further comprises setting a bitrate limit for each ABR session by dividing the hourly premises bandwidth cap by the number of active ABR sessions.

7 . The method according to claim 2 , wherein dynamically adjusting the premises bandwidth cap includes:
applying the linear regression model to predict data consumption (Dc) for a current hour; applying the linear regression model to predict total data consumption (Dctotal) for remaining hours of the current billing cycle; and calculating an hourly premises bandwidth cap by adding to Dc, the value: (data remaining within the data cap for the current billing cycle −Dctotal)/hours remaining in the current billing cycle.

8 . The method according to claim 7 , wherein there are a plurality of Adaptive Bitrate (ABR) data sessions active at the premises, and the method further comprises setting a bitrate limit for each ABR data session by dividing the hourly premises bandwidth cap by the number of active ABR data sessions.

9 . The method according to claim 1 , wherein predicting future data usage includes generating a historical usage alpha-generation model for data usage at the customer premises based on historical data usage patterns at the premises during past billing cycles.

10 . The method according to claim 9 , wherein the billing cycles are months, and when more than a year's worth of historical data usage information is available, generating a historical model for data usage at the customer premises includes generating a historical model for a current month utilizing data usage information from the same month from a previous year.

11 . The method according to claim 9 , wherein the billing cycles are months, and when more than one month but less than a year's worth of historical data usage information is available, generating a historical model for data usage at the customer premises includes generating a historical model for a current month utilizing data usage information from a previous month.

12 . The method according to claim 9 , further comprising:
throttling only Adaptive Bitrate (ABR) data streams to maintain a current premises bitrate within the premises bandwidth cap, wherein throttling includes:
generating a value, α, equal to the time in the billing cycle (t m ) divided by the time spent consuming video (t c ) based on the historical data usage patterns at the premises during past billing cycles;
calculating, for the sum of all Adaptive Bitrate (ABR) data streams entering the premises, an allowed data rate (ABR_Rate) by multiplying α by the data remaining within the data cap divided by the days remaining in the billing cycle; and
setting a bitrate limit for each ABR data stream based on the ABR_Rate and the number of ABR data streams.

13 . A DataCap Management Unit for controlling data usage at a customer premises, the DataCap Management Unit comprising:
an interface configured to receive all data entering the premises and to monitor all data usage by the premises; and a processing circuit configured to:
predict future data usage at the customer premises for a current billing cycle; and
dynamically adjust a premises bandwidth cap throughout the current billing cycle, using the predicted future data usage as an input, to smoothly keep actual total data usage for the current billing cycle from exceeding a data cap before the current billing cycle ends.

14 . The DataCap Management Unit according to claim 13 , wherein the processing circuit is configured to predict future data usage by generating a linear regression model for future data usage at the customer premises based on past data usage at the premises.

15 . The DataCap Management Unit according to claim 14 , wherein the processing circuit is configured to update the linear regression model by recalculating coefficients as more recent data usage information becomes available.

16 . The DataCap Management Unit according to claim 14 , wherein the linear regression model utilizes a least square method.

17 . The DataCap Management Unit according to claim 14 , wherein the processing circuit is configured to:
apply the linear regression model to predict data consumption (Dc) for a current day; apply the linear regression model to predict total data consumption (Dctotal) for remaining days of the current billing cycle; calculate an offset of Dc for an overrun or underrun (Dceffective) by adding to Dc, the value: (data remaining within the data cap for the current billing cycle −Dctotal)/days remaining in the current billing cycle; and calculate an hourly premises bandwidth cap by dividing Dceffective by 24.

18 . The DataCap Management Unit according to claim 17 , wherein there are a plurality of Adaptive Bitrate (ABR) sessions active at the premises, and the processing circuit is configured to set a bitrate limit for each ABR session by dividing the hourly premises bandwidth cap by the number of active ABR sessions.

19 . The DataCap Management Unit according to claim 14 , wherein the processing circuit is configured to:
apply the linear regression model to predict data consumption (Dc) for a current hour; apply the linear regression model to predict total data consumption (Dctotal) for remaining hours of the current billing cycle; and calculate an hourly premises bandwidth cap by adding to Dc, the value: (data remaining within the data cap for the current billing cycle −Dctotal)/hours remaining in the current billing cycle.

20 . The DataCap Management Unit according to claim 19 , wherein there are a plurality of Adaptive Bitrate (ABR) data sessions active at the premises, and the processing circuit is configured to set a bitrate limit for each ABR data sessi

Assignees

• ERICSSON TELEFON AB L M (publ)

Inventors

• Phillips Chris
• Forsman Robert Hammond
• Reynolds Jennifer Ann
• Changrani Vishal

Classifications

• H04L41/0896 Primary

  Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities (flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76) · CPC title

• H04M15/8214

  Data or packet based · CPC title

• H04W4/24

  Accounting or billing · CPC title

• H04M15/8228

  Session based · CPC title

• H04L12/1407

  Policy-and-charging control [PCC] architecture · CPC title