Intelligent steering in 5G

Patent metadata

Field	Value
Publication number	US-2022286894-A1
Application number	US-202217699455-A
Country	US
Kind code	A1
Filing date	Mar 21, 2022
Priority date	Mar 8, 2021
Publication date	Sep 8, 2022
Grant date	—

Abstract

The present disclosure relates to systems and methods for cloud-based 5G security network architectures intelligent steering, workload isolation, identity, and secure edge steering. Specifically, various approaches are described to integrate cloud-based security services into Multiaccess Edge Compute servers (MECs). That is, existing cloud-based security services are in line between a UE and the Internet. The present disclosure includes integrating the cloud-based security services and associated cloud-based system within service provider's MECs. In this manner, a cloud-based security service can be integrated with a service provider's 5G network or a 5G network privately operated by the customer. For example, nodes in a cloud-based system can be collocated within a service provider's network, to provide security functions to 5G users or connected by peering from the cloud-based security service into the 5G service provider's regional communications centers.

First claim

What is claimed is:

1 . A method of intelligent traffic path steering comprising:
receiving a request from an endpoint for connection to a workload destination; identifying a path by utilizing a set of data identification services to assess all parts of an endpoint to workload traffic flow; determining how to steer each part of the endpoint traffic flow to achieve a desired goal; and steering the endpoint traffic based on the determination.

2 . The method of claim 1 , wherein the identifying and determining is performed manually, by Artificial Intelligence (AI), and/or Machine Learning (ML), based on datasets.

3 . The method of claim 1 , wherein the data is calculated data by an AI-engine for known sets of workload, protocol, latency, speed, and endpoint combinations.

4 . The method of claim 3 , wherein the AI-engine delivers an outcome to a steering platform that instructs it on how and where to send the traffic.

5 . The method of claim 3 , wherein the AI-engine exists as a core function of an internet access platform & can be queried for insights.

6 . The method of claim 1 , wherein the desired goals are based on performance and cost.

7 . The method of claim 1 , wherein workload providers are able to select exactly where a workload may appear for a user & enforce only local placement and connectivity.

8 . A non-transitory computer-readable medium comprising instructions that, when executed, cause one or more processors to perform steps of
receiving a request from an endpoint for connection to a workload destination; identifying a path by utilizing a set of data identification services to assess all parts of an endpoint to workload traffic flow; determining how to steer each part of the endpoint traffic flow to achieve a desired goal; and steering the endpoint traffic based on the determination.

9 . The non-transitory computer-readable medium of claim 8 , wherein the identifying and determining is performed manually, by Artificial Intelligence (AI), and/or Machine Learning (ML), based on datasets.

10 . The non-transitory computer-readable medium of claim 8 , wherein the data is calculated data by an AI-engine for known sets of workload, protocol, latency, speed, and endpoint combinations.

11 . The non-transitory computer-readable medium of claim 10 , wherein the AI-engine delivers an outcome to a steering platform that instructs it on how and where to send the traffic.

12 . The non-transitory computer-readable medium of claim 10 , wherein the AI-engine exists as a core function of an internet access platform & can be queried for insights.

13 . The non-transitory computer-readable medium of claim 8 , wherein the desired goals are based on performance and cost.

14 . The non-transitory computer-readable medium of claim 8 , wherein workload providers are able to select exactly where a workload may appear for a user & enforce only local placement and connectivity.

15 . An edge compute system configured for cloud-based 5G security via an endpoint service, the edge compute system comprising:
one or more processors and memory storing instructions that, when executed, cause the one or more processors to
receive a request from an endpoint for connection to a workload destination;
identify a path by utilizing a set of data identification services to assess all parts of an endpoint to workload traffic flow;
determine how to steer each part of the endpoint traffic flow to achieve a desired goal; and
steer the endpoint traffic based on the determination.

16 . The edge compute system of claim 15 , wherein the identifying and determining is performed manually, by Artificial Intelligence (AI), and/or Machine Learning (ML), based on datasets.

17 . The edge compute system of claim 15 , wherein the data is calculated data by an AI-engine for known sets of workload, protocol, latency, speed, and endpoint combinations.

18 . The edge compute system of claim 17 , wherein the AI-engine delivers an outcome to a steering platform that instructs it on how and where to send the traffic.

19 . The edge compute system of claim 17 , wherein the AI-engine exists as a core function of an internet access platform & can be queried for insights.

20 . The edge compute system of claim 15 , wherein the desired goals are based on performance and cost.

Assignees

• Zscaler Inc

Inventors

• Howe Nathan
• Urquhart Kenneth B
• Srinivasan Subramanian
• Deshpande Sridhar Kartik Kumar Chatnalli
• Foxhoven Patrick

Classifications

• H04W28/0215

  based on user or device properties, e.g. MTC-capable devices (services for machine-to-machine communication [M2M] or machine type communication [MTC] H04W4/70; wireless resource selection or allocation plan definition based on terminal or device properties H04W72/51) · CPC title

• H04W28/0247 Primary

  based on conditions of the access network or the infrastructure network (central resource management H04W28/16) · CPC title

• H04W28/0268

  using specific QoS parameters for wireless networks, e.g. QoS class identifier [QCI] or guaranteed bit rate [GBR] (negotiating SLA or negotiating QoS H04W28/24) · CPC title