# US 2021/0240626 A1 · Cache update prediction mechanism

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- Number: US 2021/0240626 A1
- Document ID: US20210240626A1
- Status: Published application
- Kind: A1
- Application number: 16/777,579
- Filed: 2020-01-30
- Published: 2021-08-05
- Earliest claimed: 2020-01-30
- Assignees: Hewlett Packard Enterprise Development LP
- Applicants: Hewlett Packard Enterprise Development LP
- Inventors: SUBHAJIT DASGUPTA; Charles E. Fowler; Michelle Frolik; Charles Greenidge; Jerry Harrow; Sandesh V. Madhyastha; Clifford A. McCarthy; Abhay Padlia; Rajeev Pandey; Jonathan M. Sauer; Geoffery Schunicht; Latha Srinivasan; Gary L. Thunquest
- Web page: https://patentgrep.com/patent/US20210240626A1

## CPC

- G06F 12/0837: with software control, e.g. non-cacheable data
- G06F 9/5072: {Grid computing}
- G06F 12/0804: with main memory updating (G06F12/0806 takes precedence)
- G06N 20/00: Machine learning
- H04L 67/1097: for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
- 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

A system to facilitate infrastructure management is described. The system includes one or more processors and a non-transitory machine-readable medium storing instructions that, when executed, cause the one or more processors to detect a first data update received at a data cache associated with a first of a plurality of data sources, generate a time of arrival value associated with a time at which the update was received at the data cache, adjust one or more parameters in a machine learning model based on the time of arrival value and generate a predicted time of arrival value based on the one or more parameters, wherein the predicted time of arrival value corresponds to a predicted arrival time of a second data update to the data cache for the first data source

## Claims

### Claim 1

A system to facilitate infrastructure management, comprising:
one or more processors; and
a non-transitory machine-readable medium storing instructions that, when executed, cause the one or more processors to execute a controller to:
detect a first data update received at a data cache associated with a first of a plurality of data sources;
generate a time of arrival value associated with a time at which the update was received at the data cache;
adjust one or more parameters in a machine learning model based on the time of arrival value; and
generate a predicted time of arrival value based on the one or more parameters, wherein the predicted time of arrival value corresponds to a predicted arrival time of a second data update to the data cache for the first data source.

### Claim 2 (depends on 1)

The system of claim 1, wherein the controller generates a rate of update value based on the predicted arrival time.

### Claim 3 (depends on 2)

The system of claim 2, wherein the rate of update value comprises a percentage represented by an elapsed time since the first data update divided by a delay time until the predicted arrival time of the second data update.

### Claim 4 (depends on 3)

The system of claim 3, wherein the machine learning model comprises a gradient descent model.

### Claim 5 (depends on 4)

The system of claim 4, wherein the gradient descent model generates the predicted time of arrival value based on a linear regression of the one or more parameters.

### Claim 6 (depends on 5)

The system of claim 5, wherein the first data source comprises an on-premise infrastructure controller that controls a plurality of infrastructure devices.

### Claim 7 (depends on 6)

The system of claim 6, wherein the first data source comprises an infrastructure device.

### Claim 8 (depends on 1)

The system of claim 1, wherein each of the plurality of data sources comprises a unique identifier.

### Claim 9 (depends on 8)

The system of claim 8, wherein the first data update comprises a data stream including a first identifier associated with the first data source.

### Claim 10

A non-transitory machine-readable medium storing instructions which, when executed by a processor, cause the processor to:
detect a first data update received at a data cache associated with a first of a plurality of data sources;
generate a time of arrival value associated with a time at which the update was received at the data cache;
adjust one or more parameters in a machine learning model based on the time of arrival value; and
generate a predicted time of arrival value based on the one or more parameters, wherein the predicted time of arrival value corresponds to a predicted arrival time of a second data update to the data cache for the first data source.

### Claim 11 (depends on 10)

The non-transitory machine-readable medium of claim 10, storing instructions which, when executed by a processor, cause the processor to generates a rate of update value based on the predicted arrival time.

### Claim 12 (depends on 11)

The non-transitory machine-readable medium of claim 11, wherein the rate of update value comprises a percentage represented by an elapsed time since the first data update divided by a delay time until the predicted arrival time of the second data update.

### Claim 13 (depends on 12)

The non-transitory machine-readable medium of claim 12, wherein the machine learning model comprises a gradient descent model.

### Claim 14 (depends on 13)

The non-transitory machine-readable medium of claim 13, wherein the gradient descent model generates the predicted time of arrival value based on a linear regression of the one or more parameters.

### Claim 15 (depends on 14)

The non-transitory machine-readable medium of claim 14, wherein the first data source comprises an on-premise infrastructure controller that controls a plurality of infrastructure devices.

### Claim 16

A method to facilitate infrastructure management, comprising:
detecting a first data update received at a data cache associated with a first of a plurality of data sources;
generating a time of arrival value associated with a time at which the update was received at the data cache;
adjusting one or more parameters in a machine learning model based on the time of arrival value; and
generating a predicted time of arrival value based on the one or more parameters, wherein the predicted time of arrival value corresponds to a predicted arrival time of a second data update to the data cache for the first data source.

### Claim 17 (depends on 10)

The method of claim 10, storing instructions which, when executed by a processor, cause the processor to generates a rate of update value based on the predicted arrival time.

### Claim 18 (depends on 17)

The method of claim 17, wherein the rate of update value comprises a percentage represented by an elapsed time since the first data update divided by a delay time until the predicted arrival time of the second data update.

### Claim 19 (depends on 18)

The method of claim 18, wherein the machine learning model comprises a gradient descent model.

### Claim 20 (depends on 19)

The method of claim 19, wherein the gradient descent model generates the predicted time of arrival value based on a linear regression of the one or more parameters.