Predictive resource allocation in an edge computing network utilizing beacons

Patent metadata

Field	Value
Publication number	US-12118404-B2
Application number	US-202017090421-A
Country	US
Kind code	B2
Filing date	Nov 5, 2020
Priority date	Nov 6, 2019
Publication date	Oct 15, 2024
Grant date	Oct 15, 2024

Abstract

The present technology relates to improving computing services in a distributed network of remote computing resources, such as edge nodes in an edge compute network. In an aspect, the technology relates to a system that includes a plurality of edge nodes and a beacon. The system performs operations that may include collecting traffic data from the beacon over a period of time, wherein the traffic data includes at least an amount of devices sending probe requests to the beacon; comparing the amount of devices to a predetermined threshold for traffic data; and based on the comparison of the amount of devices to the predetermined threshold for traffic data, generating a recommendation for installation of a new edge node in addition to the plurality of edge nodes.

First claim

What is claimed is:

1. A system for allocating hardware resources in a network, the system comprising:
a plurality of edge nodes having different physical locations;
a beacon having a different physical location than the plurality of edge nodes;
at least one processor; and
memory, operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause the at least one processor to perform a set of operations comprising:
collecting traffic data from the beacon over a period of time, wherein the traffic data includes at least a number of devices sending probe requests to the beacon, wherein the number of devices is a numerical count of unique devices;
comparing the number of devices to a predetermined threshold for traffic data; and
based on the comparison of the number of devices to the predetermined threshold for traffic data, generating a recommendation for installation of a new edge node in addition to the plurality of edge nodes.

2. The system of claim 1 , wherein the operations further comprise:
extracting unique device identifiers from a plurality of the probe requests; and
based on the extracted unique device identifiers, determining device types that send the plurality of the probe requests.

3. The system of claim 2 , wherein the unique device identifiers are media access control (MAC) addresses.

4. The system of claim 2 , wherein the operations further comprise generating, based on the determined device types, predicted service types for requests generated by the devices sending the probe requests to the beacon.

5. The system of claim 4 , wherein the operations further comprise generating, based on the predicted services types, a recommendation for a hardware allocation for the new edge node.

6. The system of claim 1 , wherein the beacon includes at least one of a WiFi radio or a Bluetooth radio.

7. The system of claim 1 , wherein the beacon includes at least one of a wired interface or a cellular data interface.

8. The system of claim 1 , wherein the beacon is environmentally hardened.

9. The system of claim 8 , wherein the beacon comprises at least one rechargeable battery and a solar panel for recharging the at least one rechargeable battery.

10. The system of claim 1 , wherein the beacon is configured to receive requests from one or more mobile computing devices and forward the received requests to at least one of the plurality of edge nodes for processing of the received requests.

11. The system of claim 10 , wherein the beacon is further configured to extract, from the received requests, a service type requested.

12. The system of claim 11 , wherein the beacon is further configured to extract, from the received requests, a user identifier associated with the user requesting the service.

13. The system of claim 11 , wherein the operations further comprise generating, based on the services types, a recommendation for a hardware allocation for the new edge node.

14. A computer-implemented method for allocating hardware resources in a network, the method comprising:
collecting traffic data from a beacon over a period of time, wherein the traffic data includes at least a number of devices sending probe requests to the beacon, wherein the number of devices is a numerical count of unique devices;
comparing the number of devices to a predetermined threshold for traffic data; and
based on the comparison of the number of devices to the predetermined threshold for traffic data, generating a recommendation for installation of a new edge node in addition to a plurality of existing edge nodes.

15. The computer-implemented method of claim 14 , further comprising:
extracting unique device identifiers from a plurality of the probe requests; and
based on the extracted unique device identifiers, determining device types that send the plurality of the probe requests.

16. The computer-implemented method of claim 15 , wherein the unique device identifiers are media access control (MAC) addresses.

17. The computer-implemented method of claim 15 , further comprising generating, based on the determined device types, predicted service types for requests generated by the devices sending the probe requests to the beacon.

18. The computer-implemented method of claim 17 , further comprising generating, based on the predicted services types, a recommendation for a hardware allocation for the new edge node.

19. The computer-implemented method of claim 14 , further comprising receiving requests from one or more mobile computing devices and forward the received requests to at least one of the plurality of existing edge nodes for processing of the received requests.

Assignees

• Centurylink Ip Llc

Inventors

• Casey Steven M
• Castro Felipe
• Opferman Stephen
• Mcbride Kevin M

Classifications

• G06N3/092

  Reinforcement learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• H04L47/83

  based on usage prediction · CPC title