Adaptive location-based SD-WAN policies

Patent metadata

Field	Value
Publication number	US-11902097-B2
Application number	US-202318305461-A
Country	US
Kind code	B2
Filing date	Apr 24, 2023
Priority date	Oct 29, 2021
Publication date	Feb 13, 2024
Grant date	Feb 13, 2024

Abstract

An example method includes receiving, by an SD-WAN system, WAN link characterization data for a plurality of WAN links of the SD-WAN system over a time period; and for each site of a plurality of sites of the SD-WAN system, generating, by the SD-WAN system, a local policy for the site, wherein generating the local policy is based on a machine learning model trained with the WAN link characterization data for the plurality of WAN links, and providing the local policy to an SD-WAN edge device of the site.

First claim

What is claimed is:

1. Non-transitory computer-readable storage media comprising instructions that, when executed, configure processing circuitry of a computing system to:
receive wide area network (WAN) link characterization data for a plurality of WAN links of a software-defined WAN (SD-WAN) system over a time period; and
for each site of a plurality of sites of the SD-WAN system,
generate a local policy for the site based on a global policy comprising one or more rules and applicable to all of the plurality of sites and a machine learning model trained with the WAN link characterization data for the plurality of WAN links, and
provide the local policy to an SD-WAN edge device of the site.

2. The non-transitory computer-readable storage media of claim 1 , wherein the instructions that configure processing circuitry of the computing system to generate the local policy for the site comprise instructions that configure processing circuitry of the computing system to generate, based on the machine learning model, one or more customized versions of rules of the global policy for inclusion in the local policy.

3. The non-transitory computer-readable storage media of claim 1 , wherein the instructions that configure processing circuitry of the computing system to generate the local policy comprise instructions that configure processing circuitry of the computing system to generate the local policy based on a location corresponding to the SD-WAN edge device.

4. The non-transitory computer-readable storage media of claim 1 , wherein the machine learning model is trained with policy parameters being applied at a time the WAN link characterization data was collected.

5. The non-transitory computer-readable storage media of claim 1 , wherein the WAN link characterization data includes one or more of an amount of jitter, an amount of latency, a time to first packet, an amount of packet loss, and a maximum transmission unit (MTU) for each of the plurality of WAN links.

6. The non-transitory computer-readable storage media of claim 5 , wherein the WAN link characterization data includes an amount of packet loss, the non-transitory computer-readable storage media further comprising instructions that, when executed, configure processing circuitry of a computing system to:
determine the packet loss based on determining a number of packets transmitted by the SD-WAN edge device that did not reach destination devices specified in the packets.

7. The non-transitory computer-readable storage media of claim 1 , further comprising instructions that, when executed, configure processing circuitry of a computing system to:
train the machine learning model based on historical WAN link characterization data received over a time period.

8. The non-transitory computer-readable storage media of claim 1 , wherein the instructions that configure processing circuitry of the computing system to generate the local policy for the site comprise instructions that configure processing circuitry of the computing system to generate a corresponding service policy for each service of one or more services.

9. The non-transitory computer-readable storage media of claim 8 , wherein the corresponding service policy specifies selection criteria for at least one of a network path or a service level assurance parameter.

10. The non-transitory computer-readable storage media of claim 1 , wherein the local policy includes a rule to determine whether to reassign an application or service from a first WAN link associated with the SD-WAN edge device to a second WAN link associated with the SD-WAN edge device, wherein the rule includes a Quality of Experience (QoE) parameter or a cost factor associated with each of the first WAN link and the second WAN link.

11. The non-transitory computer-readable storage media of claim 10 , further comprising instructions that, when executed, configure processing circuitry of a computing system to:
optimize the QoE parameter and the cost factor associated with each of the first WAN link and the second WAN link.

12. The non-transitory computer-readable storage media of claim 1 , wherein the local policy includes a rule to select a network path based on a path signature associated with the network path corresponding to one or more service network characteristics.

13. A software-defined wide area network (SD-WAN) system comprising processing circuitry in communication with memory and configured to:
receive WAN link characterization data for a plurality of WAN links of the SD-WAN system over a time period, and
for each site of a plurality of sites of the SD-WAN system,
generate a local policy for the site based on a global policy comprising one or more rules and applicable to all of the plurality of sites and a machine learning model trained with the WAN link characterization data for the plurality of WAN links, and
provide the local policy to an SD-WAN edge device of the site.

14. The SD-WAN system of claim 13 , wherein the processing circuitry is configured to generate, based on the machine learning model, one or more customized versions of rules of the global policy for inclusion in the local policy.

15. The SD-WAN system of claim 13 , wherein the processing circuitry is configured to generate the local policy based on a location corresponding to the SD-WAN edge device.

16. The SD-WAN system of claim 13 , wherein the WAN link characterization data includes one or more of an amount of jitter, an amount of latency, a time to first packet, an amount of packet loss, and a maximum transmission unit (MTU) for each of the plurality of WAN links.

17. The SD-WAN system of claim 13 , wherein the machine learning model is trained based on historical WAN link characterization data received over a time period.

18. The SD-WAN system of claim 13 , wherein the local policy includes a rule to determine whether to reassign an application or service from a first WAN link associated with the SD-WAN edge device to a second WAN link associated with the SD-WAN edge device, wherein the rule includes a Quality of Experience (QoE) parameter or a cost factor associated with each of the first WAN link and the second WAN link.

19. Non-transitory computer-readable storage media comprising instructions that, when executed, configure processing circuitry of a software-defined wide area network (SD-WAN) edge device to:
receive, from a network analysis system, a machine learning model trained with WAN link characterization data for a plurality of WAN links of a plurality of sites;
generate a local policy for the SD-WAN edge device based on the machine learning model; and
assign, based on the local policy, a service or application of the SD-WAN edge device to a WAN link of a plurality of WAN links associated with the SD-WAN edge device.

20. The non-transitory computer-readable storage media of claim 19 , wherein the local policy includes a rule to determine whether to reassign the service or application from a first WAN link of the plurality of WAN links associated with the SD-WAN edge device to a second WAN link of the plurality of WAN links associated with the SD-WAN edge device, wherein the rule includes a Quality of Experience (QoE) parameter or a cost factor associated with each of the first WAN link and the second WAN link.

Assignees

• Juniper Networks Inc

Inventors

• Wang Jisheng
• Mehta Ruchit Rajkumar
• Agrawal Kaushik Adesh
• Timmons Patrick
• Melampy Patrick J
• Baj Michael

Classifications

• H04L41/0893 Primary

  Assignment of logical groups to network elements · CPC title

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• H04L41/5009

  Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF] · CPC title

• H04L41/147 Primary

  for predicting network behaviour · CPC title

• H04L41/14

  Network analysis or design · CPC title