METHOD AND APPARATUS FOR DISTRIBUTING MEDIA CONTENT

• US 20160301747A1
• Filed: 06/23/2016
• Published: 10/13/2016
• Est. Priority Date: 11/25/2013
• Status: Active Grant

• Current Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Original Assignee: AT&T Intellectual Property I LP (AT&T, Inc.)
• Inventors: LEE, SEUNGJOON, RAMAKRISHNAN, KADANGODE, GOPALAKRISHNAN, VIJAY, Archer, Aaron Francis, APPLEGATE, DAVID LEE
• Granted Patent: US 9,942,313 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06F 9/5027 the resource being a machine, e.g. CPUs, Servers, Terminals
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 45/124 using a combination of metrics
• CPC Class Codes: H04L 65/60 Network streaming of media packets
• CPC Class Codes: H04L 65/612 for unicast
• CPC Class Codes: H04L 65/762 at the source reformatting of additional data in video distribution servers H04N21/2355
• CPC Class Codes: H04L 65/80 Responding to QoS
• CPC Class Codes: H04L 67/1017 based on a round robin mechanism
• CPC Class Codes: H04L 67/1023 based on a hash applied to IP addresses or costs
• CPC Class Codes: H04L 67/1029 using data related to the state of servers by a load balancer
• CPC Class Codes: H04L 67/568 Storing data temporarily at an intermediate stage, e.g. caching

Abstract

A system that incorporates teachings of the present disclosure may include, for example, initializing a boundary estimate for an optimization of a linear programming model describing a network of media servers for servicing requests for media content items from subscriber devices, where the boundary estimate is an estimate of an infeasible solution of the linear programming model, and calculating iteratively, using an exponential potential function, additional boundary estimates for the linear programming model, wherein the calculating resolves to an improved boundary estimate that corresponds to placement of copies of the media content items at the media servers subject to a set of constraints on storage capacity of media servers and on bandwidth for communication links in the network. Other embodiments are disclosed.

Claims

1. A server comprising:

a processing system including a processor; and

a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, comprising;

initiating a boundary estimate for evaluating a linear programming model of a network of media servers for placement of copies of media content items, wherein the boundary estimate is an estimate of a solution of the linear programming model,

calculating iteratively, using an exponential potential function, a series of additional boundary estimates for the linear programming model, wherein the calculating resolves to an improved boundary estimate that corresponds to the placement of the copies of the media content items at the media servers and to routing of requests from subscriber devices for the media content items from local media servers to non-local media servers to reduce total data traffic over communication links in the network according to a set of constraints; and

provisioning the placement of the copies of the media content items at the media servers, and the routing of the requests over the communications links in the network, according to the improved boundary estimate.

2. The server of claim 1, wherein the operations further comprise receiving a forecast of demand for the media content items for streaming from the network of the media servers in response to the requests, and wherein the exponential potential function uses Dantzig-Wolfe decomposition.

3. The server of claim 1, wherein the linear programming model comprises a uniform block strategy.

4. The server of claim 1, wherein the linear programming model comprises a round robin strategy.

5. The server of claim 1, wherein the exponential potential function allows fractions of the media content items be calculated, and wherein the processor further performs operations comprising re-calculating, via the exponential potential function, the improved boundary estimate to round the fractions of the media content items into integer numbers of the media content items.

6. The server of claim 1, wherein the series of additional boundary estimates do not comprise a binary search for the improved boundary estimate.

7. The server of claim 1, wherein the series of additional boundary estimates are associated with a lower boundary for the linear programming model.

8. The server of claim 1, wherein the calculating of the series of additional boundary estimates further comprises smoothing ordinary duals of the exponential potential function for solving a plurality of lower boundary estimates.

9. The server of claim 1, wherein a particular media content item is streamed from a local media server if the particular media content item is stored at the local media server and is streamed from a non-local media server if the particular media content item is not stored at the local media server.

10. The server of claim 1, wherein the calculating of the series of additional boundary estimates further comprises:

determining, via a first portion of the calculating, a first plurality of variables associated with the linear programming model according to parallel computation of a plurality of functional blocks;

substituting the first plurality of variables that are determined via the parallel computation into the linear programming model; and

determining, via a second portion of the calculating, a second plurality of variables associated with the linear programming model according to sequential programming of the plurality of functional blocks according to the substituting.

11. A method, comprising:

initializing, by a processing system including a processor, a boundary estimate for an optimization of a linear programming model describing a network of media servers for servicing requests for media content items from subscriber devices by streaming the media content items to the subscriber devices over the network, wherein the boundary estimate is an estimate of a solution of the linear programming model, wherein the linear programming model comprises a uniform random block strategy;

computing iteratively, by the processing system and via an exponential potential function, a series of additional boundary estimates for the linear programming model, wherein the computing resolves to an improved boundary estimate that corresponds to placement of copies of media content items at the media servers and to routing of requests from subscriber devices for the media content items from local media servers to non-local media servers so as to reduce total data traffic over communication links in the network according to a set of constraints; and

provisioning, by the processing system, the placement of the copies of the media content items at the media servers, and the routing of the requests over the communications links in the network, according to the improved boundary estimate.

12. The method of claim 11, further comprise receiving, by the processing system, a forecast of demand for the media content items for streaming from the network of the media servers in response to the requests.

13. The method of claim 11, wherein the exponential potential function uses Dantzig-Wolfe decomposition.

14. The method of claim 11, wherein the linear programming model comprises a round robin strategy.

15. The method of claim 11, wherein the series of additional boundary estimates are associated with a lower boundary for the linear programming model.

16. The method of claim 11, wherein the computing of the series of additional boundary estimates further comprises smoothing ordinary duals of the exponential potential function for solving a plurality of lower boundary estimates.

17. The method of claim 11, wherein the computing of the series of additional boundary estimates further comprises:

determining, via a first portion of the computing, a first plurality of variables associated with the linear programming model according to parallel computation of a plurality of functional blocks;

substituting the first plurality of variables that are determined via the parallel computation into the linear programming model; and

determining, via a second portion of the computing, a second plurality of variables associated with the linear programming model according to sequential programming of the plurality of functional blocks according to the substituting.

18. A machine-readable storage medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, comprising:

initializing a boundary estimate for an optimization of a linear programming model describing a network of media servers wherein the boundary estimate is an estimate of a solution of the linear programming model, wherein the linear programming model of the network of the media servers comprises a round robin strategy; and

calculating iteratively, using an exponential potential function, a series of additional boundary estimates for the linear programming model, wherein the calculating resolves to an improved boundary estimate that corresponds to placement of copies of media content items at the media servers subject to a set of constraints on storage capacity of the media servers and on bandwidth for communication links in the network; and

provisioning the placement of the copies of the media content items at the media servers, and routing of requests from subscriber devices over the communications links in the network, according to the improved boundary estimate.

19. The machine-readable storage medium of claim 18, wherein the operations further comprise obtaining, by the processing system, a forecast of demand for the media content items for streaming from the network of the media servers in response to the requests.

20. The machine-readable storage medium of claim 18, wherein the linear programming model comprises a uniform block strategy.