Method for predicting usage for cloud storage service and system therefor

Patent metadata

Field	Value
Publication number	US-2024220833-A1
Application number	US-202318382733-A
Country	US
Kind code	A1
Filing date	Oct 23, 2023
Priority date	Dec 29, 2022
Publication date	Jul 4, 2024
Grant date	—

Abstract

Provided are a method for predicting usage for cloud storage service and system therefor. The method according to some embodiments may include obtaining a time series dataset through monitoring usage of storage resource, extracting a plurality of candidate training sets from the time series dataset, evaluating suitability of the plurality of candidate training sets to a linear regression model, wherein an independent variable of the linear regression model comprises a time variable and a dependent variable represents usage of the storage resource; selecting a training set from the plurality of candidate training sets based on the evaluation result, and predicting future usage of the storage resource through the linear regression model trained with the training set.

First claim

What is claimed is:

1 . A method for predicting usage for cloud storage service performed by at least one computing device, the method comprising:
obtaining a time series dataset through monitoring usage of storage resource; extracting a plurality of candidate training sets from the time series dataset; evaluating suitability of the plurality of candidate training sets to a linear regression model, wherein an independent variable of the linear regression model comprises a time variable and a dependent variable represents usage of the storage resource; selecting a training set from the plurality of candidate training sets based on the evaluation result; and predicting future usage of the storage resource through the linear regression model trained with the training set.

2 . The method of claim 1 , wherein the extracting the plurality of candidate training sets comprises:
dividing the time series dataset into a plurality of partial datasets; extracting the most recent partial dataset among the plurality of partial datasets as a first candidate training set; and extracting other partial dataset different from the most recent partial dataset as a second candidate training set.

3 . The method of claim 2 , wherein the other partial dataset is a neighboring dataset of the most recent partial dataset.

4 . The method of claim 2 , wherein the evaluating the suitability of the plurality of candidate training sets comprises:
evaluating suitability of the first candidate training set using a linear regression model for evaluation trained with the first candidate training set; additionally training the linear regression model for evaluation with the other partial dataset; and evaluating suitability of the second candidate training set using the additionally trained linear regression model.

5 . The method of claim 1 , wherein suitability of each of the plurality of candidate training sets is evaluated based on a determination coefficient of a linear regression model for evaluation trained with a candidate training set.

6 . The method of claim 1 , wherein the evaluating the suitability of the plurality of candidate training sets comprises:
evaluating suitability of a specific candidate training set based on a residual of a linear regression model for evaluation for the specific candidate training set.

7 . The method of claim 6 , wherein the evaluating the suitability of the specific candidate training set comprises:
training the linear regression model for evaluation using a first partial dataset of the specific candidate training set; and calculating a residual of the linear regression model for evaluation using a second partial data set of the specific candidate training set different from the first partial data set.

8 . The method of claim 1 , wherein the training set is a first training set selected from a first time series dataset generated through monitoring up to a first time point,
wherein the linear regression model is a first linear regression model for predicting future usage after the first time point, the method further comprises: selecting a second training set from a second time series dataset obtained through monitoring up to a second time point after the first time point, wherein the second time series dataset comprises additional dataset generated through monitoring after the first time point; and predicting future usage after the second time point through a second linear regression model trained with the second training set.

9 . The method of claim 8 , wherein learned parameters of a linear regression model for evaluation obtained during a process of determining the first training set are stored in a storage,
wherein the selecting the second training set comprises: updating the learned parameters by learning the additional dataset; selecting the second training set by evaluating suitability of candidate training sets using the updated parameters; and storing the updated parameters in the storage.

10 . The method of claim 1 , wherein the predicting the future usage comprises:
predicting usage of the storage resource at a future time point by inputting a value indicating the future time point into the trained linear regression model.

11 . The method of claim 1 , wherein the predicting the future usage comprises:
predicting a time point when future usage of the storage resource reaches a specific amount through the trained linear regression model.

12 . The method of claim 1 , wherein the training set is a dataset for a specific client,
wherein the predicting the future usage comprises: predicting a time point when future usage of the storage resource allocated to the specific client reaches an allocated amount through the trained linear regression model; and allocating additional storage resource to the specific client before the predicted time point.

13 . A system for predicting usage for a cloud storage service comprising:
one or more processors; and a memory for storing instructions, wherein the one or more processors, by executing the stored instructions, perform operations comprising:
obtaining a time series dataset through monitoring usage of storage resource;
extracting a plurality of candidate training sets from the time series dataset:
evaluating suitability of the plurality of candidate training sets to a linear regression model, wherein an independent variable of the linear regression model comprises a time variable and a dependent variable represents usage of the storage resource;
determining at least one training set from the plurality of candidate training sets based on the evaluation result; and
predicting future usage of the storage resource through the linear regression model trained with the at least one training set.

14 . The system of claim 13 , wherein the extracting the plurality of candidate training sets comprises:
dividing the time series dataset into a plurality of partial datasets; extracting the most recent partial dataset among the plurality of partial datasets as a first candidate training set; and extracting other partial dataset different from the most recent partial dataset as a second candidate training set.

15 . The system of claim 13 , wherein suitability of each of the plurality of candidate training sets is evaluated based on a determination coefficient of a linear regression model for evaluation trained with a candidate training set.

16 . The system of claim 13 , wherein the evaluating the suitability of the plurality of candidate training sets comprises:
evaluating suitability of a specific candidate training set based on a residual of a linear regression model for evaluation for the specific candidate training set.

17 . The system of claim 13 , wherein the predicting the future usage comprises:
predicting usage of the storage resource at a future time point by inputting a value indicating the future time point into the trained linear regression model; and predicting a time point when future usage of the storage resource reaches a specific amount through the trained linear regression model.

18 . A computer program combined with a computing device,
wherein the computer program is stored on a computer-readable recording medium for executing steps comprising: obtaining a time series dataset through monitoring usage of storage resource; extracting a plurality of candidate training sets from the time series dataset; evaluating suitability of the plurality of candidate training sets to a linear regression model, whe

Assignees

• Samsung Sds Co Ltd

Inventors

• Lee Hyo Jung
• Lee Jeong Hyun
• Han Seung Wan
• Choi Sung Hoon

Classifications

• G06F2009/45591

  Monitoring or debugging support · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06Q10/04

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title