# US 12,223,365 B2 · Predictive resource allocation in an edge computing network utilizing machine learning

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- Number: US 12,223,365 B2
- Document ID: US12223365B2
- Status: Granted
- Kind: B2
- Application number: 18/518,701
- Filed: 2023-11-24
- Published: 2024-03-14
- Granted: 2025-02-11
- Earliest claimed: 2019-11-06
- Assignees: CenturyLink Intellectual Property LLC
- Applicants: CenturyLink Intellectual Property LLC
- Inventors: Steven M. Casey; Felipe Castro; Stephen Opferman; Kevin M. McBride
- Web page: https://patentgrep.com/patent/US12223365B2

## CPC

- G06F 9/5072: {Grid computing}
- G06F 9/505: {considering the load}
- G06F 9/5077: {Logical partitioning of resources; Management or configuration of virtualized resources (specific details on emulation or internal functioning of virtual machines G06F9/455)}
- G06F 11/3409: {for performance assessment}
- G06N 3/0499: Feedforward networks
- G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning
- G06N 3/09: Supervised learning
- G06N 3/092: Reinforcement learning
- G06N 20/00: Machine learning
- H04L 43/0882: Utilisation of link capacity
- H04L 43/12: Network monitoring probes
- H04L 43/16: Threshold monitoring
- H04L 47/83: based on usage prediction
- H04L 61/50: Address allocation
- H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46)
- H04L 67/1008: based on parameters of servers, e.g. available memory or workload (monitoring of computer activity G06F11/30)
- H04L 67/1021: based on client or server locations
- H04L 67/146: Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding
- H04L 67/60: Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources (admission control or resource allocation H04L47/70)
- H04L 67/63: Routing a service request depending on the request content or context
- G06F 2209/5019: Workload prediction
- H04L 2101/622: Layer-2 addresses, e.g. medium access control [MAC] addresses
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## 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 method that includes aggregating historical request data for a plurality of requests, wherein the aggregated historical request data a time of the request, a location of a device from which the request originated, and/or a type of service being requested. The method also incudes training a machine learning model based on the aggregated historical request data; generating, from the trained machine learning model, a prediction for a type of service to be request; identifying an edge node, from a plurality of edge nodes, based on a physical location of the edge node; and based on predicted service, allocating computing resources for the computing service on the identified edge node.

## Claims

### Claim 1

A computer-implemented method for reducing latency in providing a computing service, the method comprising:
aggregating historical request data for a plurality of requests;
training a machine learning model based on the aggregated historical request data;
assigning a unique identifier to one or more mobile computing devices to allow for tracking of the mobile computing device;
generating, from the trained machine learning model, a prediction for a type of computing service to be requested at a predicted time and a predicted location based on at least geographic location data obtained by the tracking of the one or more mobile computing devices;
based on the generated predicted location, identifying an edge node, from a plurality of edge nodes, based on a physical location of the edge node; and
based on the generated predicted type of computing service and the predicted time, allocating computing resources for the computing service on the identified edge node.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein the machine learning model is at least one of a decision tree, a random forest, a neural network, a continual learning model, or a deep learning model.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, wherein the unique identifier is one of a cookie or a media access control (MAC) address.

### Claim 4 (depends on 1)

The computer-implemented method of claim 1, wherein allocating the computing resources comprises performing at least one of:
deploying a virtualized software;
deploying a virtualized instance;
deploying a virtualized machine;
deploying virtualized infrastructure;
deploying a virtualized container;
loading a database into memory of the identified edge node;
caching content for the computing service; or
allocating storage resources in memory of the identified edge node.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1, wherein identified edge node includes at least one of a server, a graphics processing unit (GPU), a central processing unit (CPU), or a field-programmable gate array (FPGA).

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, further comprising:
receiving, from a computing device, a request for the predicted service type at the predicted time; and
performing, by the identified edge node, the requested service with the allocated computing resources.

### Claim 7 (depends on 6)

The computer-implemented method of claim 6, wherein the computing device is one of a smart phone, laptop, vehicle, drone, a mobile computer, or a plane.

### Claim 8

A system for reducing latency in providing a computing service, the system comprising:
a plurality of edge nodes having different physical locations;
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:
training a machine learning model based on historical request data for a plurality of requests;
assigning a unique identifier to one or more mobile computing devices to allow for tracking of the mobile computing device;
generating, from the trained machine learning model, a prediction for a type of computing service to be requested at a predicted time and a predicted location based on at least geographic location data obtained by the tracking of the one or more mobile computing devices;
based on the generated predicted location, identifying an edge node, from a plurality of edge nodes, based on a physical location of the edge node; and
based on the generated predicted type of computing service and the predicted time, allocating computing resources for the computing service on the identified edge node prior to the predicted time.

### Claim 9 (depends on 8)

The system of claim 8, wherein the historical request data includes at least the following data for a plurality of requests: a time of the request, a location of the device from where the request originated, and a type of service being requested.

### Claim 10 (depends on 8)

The system of claim 8, wherein the machine learning model is at least one of a decision tree, a random forest, a neural network, a continual learning model, or a deep learning model.

### Claim 11 (depends on 8)

The system of claim 8, wherein allocating the computing resources comprises performing at least one of:
deploying a virtualized software;
deploying a virtualized instance;
deploying a virtualized machine;
deploying virtualized infrastructure;
deploying a virtualized container;
loading a database into memory of the identified edge node;
caching content for the computing service; or
allocating storage resources in memory of the identified edge node.

### Claim 12 (depends on 8)

The system of claim 8, wherein identified edge node includes at least one of a server, a graphics processing unit (GPU), a central processing unit (CPU), or a field-programmable gate array (FPGA).

### Claim 13 (depends on 8)

The system of claim 8, wherein the operation further comprise:
receiving, from one of the one or more mobile computing devices, a request for the predicted service type at the predicted time; and
performing, by the identified edge node, the requested service with the allocated computing resources.

### Claim 14 (depends on 13)

The system of claim 13 wherein the one mobile computing device is one of a smart phone, laptop, vehicle, drone, a mobile computer, or a plane.