Technique selection for file system utilization prediction

Patent metadata

Field	Value
Publication number	US-2021342290-A1
Application number	US-202016863640-A
Country	US
Kind code	A1
Filing date	Apr 30, 2020
Priority date	Apr 30, 2020
Publication date	Nov 4, 2021
Grant date	—

Abstract

A trained classification model is executed, causing a classification of a first set of file system usage data into a set of categories comprising a trend category and a periodicity category. Responsive to the first set of file system usage data being classified into the trend category, a time series of the first set of file system usage data is generated. Responsive to the first set of file system usage data being classified into the periodicity category, using an anomaly detection model, an anomaly within the first set of file system usage data is detected. Responsive to predicting that the time series will exceed a threshold, a first reconfiguring of a file system resource is caused, altering a capacity of the file system. Responsive to detecting the anomaly, a second reconfiguring of the file system resource is caused, altering a capacity of the file system.

First claim

What is claimed is:

1 . A computer-implemented method comprising:
executing, using a processor and a memory, a trained classification model, the executing causing a classification of a first set of file system usage data into a set of categories, the set of categories comprising a trend category and a periodicity category, the first set of file system usage data comprising usage data of a file system on a data storage device; causing, using the processor and the memory, at least one operation from a set of operations, the set of operations comprising (i) generating, responsive to the first set of file system usage data being classified into the trend category, a time series of the first set of file system usage data, and (ii) detecting, responsive to the first set of file system usage data being classified into the periodicity category, using an anomaly detection model, an anomaly within the first set of file system usage data; causing, using the processor and the memory, at least one alteration from a set of alterations, the set of alterations comprising (i) responsive to predicting that the time series will exceed a threshold, a first reconfiguring of a file system resource, the first reconfiguring altering a capacity of the file system, and (ii) responsive to detecting the anomaly, a second reconfiguring of the file system resource, the second reconfiguring altering a capacity of the file system.

2 . The computer-implemented method of claim 1 , further comprising:
adjusting, using a second set of file system usage data, the trained classification model, the second set of file system usage data comprising file system usage data collected subsequent to the first reconfiguring or the second reconfiguring.

3 . The computer-implemented method of claim 1 , further comprising:
adjusting, using a second set of file system usage data, the threshold, the second set of file system usage data comprising file system usage data collected subsequent to the first reconfiguring.

4 . The computer-implemented method of claim 1 , further comprising:
adjusting, using a second set of file system usage data, the first reconfiguring, the second set of file system usage data comprising file system usage data collected subsequent to the first reconfiguring.

5 . The computer-implemented method of claim 1 , further comprising:
adjusting, using a second set of file system usage data, the anomaly detection model, the second set of file system usage data comprising file system usage data collected subsequent to the second reconfiguring.

6 . The computer-implemented method of claim 1 , further comprising:
adjusting, using a second set of file system usage data, the second reconfiguring, the second set of file system usage data comprising file system usage data collected subsequent to the second reconfiguring.

7 . The computer-implemented method of claim 1 , further comprising:
causing, using the processor and the memory, a third alteration, the third alteration comprising, responsive to predicting that the time series will go below a threshold, a third reconfiguration of the file system, the third reconfiguring altering a capacity of the file system on the storage device.

8 . The computer-implemented method of claim 1 , wherein the trained classification model comprises a neural network classification model trained using a training set, the training set comprising file system usage data classified into a category in the set of categories.

9 . A computer program product for file system utilization prediction, the computer program product comprising:
one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising:
program instructions to execute, using a processor and a memory, a trained classification model, the executing causing a classification of a first set of file system usage data into a set of categories, the set of categories comprising a trend category and a periodicity category, the first set of file system usage data comprising usage data of a file system on a data storage device;
program instructions to cause, using the processor and the memory, at least one operation from a set of operations, the set of operations comprising (i) generating, responsive to the first set of file system usage data being classified into the trend category, a time series of the first set of file system usage data, and (ii) detecting, responsive to the first set of file system usage data being classified into the periodicity category, using an anomaly detection model, an anomaly within the first set of file system usage data;
program instructions to cause, using the processor and the memory, at least one alteration from a set of alterations, the set of alterations comprising (i) responsive to predicting that the time series will exceed a threshold, a first reconfiguring of a file system resource, the first reconfiguring altering a capacity of the file system, and (ii) responsive to detecting the anomaly, a second reconfiguring of the file system resource, the second reconfiguring altering a capacity of the file system.

10 . The computer program product of claim 9 , further comprising:
program instructions to adjust, using a second set of file system usage data, the trained classification model, the second set of file system usage data comprising file system usage data collected subsequent to the first reconfiguring or the second reconfiguring.

11 . The computer program product of claim 9 , further comprising:
program instructions to adjust, using a second set of file system usage data, the threshold, the second set of file system usage data comprising file system usage data collected subsequent to the first reconfiguring.

12 . The computer program product of claim 9 , further comprising:
program instructions to adjust, using a second set of file system usage data, the first reconfiguring, the second set of file system usage data comprising file system usage data collected subsequent to the first reconfiguring.

13 . The computer program product of claim 9 , further comprising:
program instructions to adjust, using a second set of file system usage data, the anomaly detection model, the second set of file system usage data comprising file system usage data collected subsequent to the second reconfiguring.

14 . The computer program product of claim 9 , further comprising:
program instructions to adjust, using a second set of file system usage data, the second reconfiguring, the second set of file system usage data comprising file system usage data collected subsequent to the second reconfiguring.

15 . The computer program product of claim 9 , further comprising:
program instructions to cause, using the processor and the memory, a third alteration, the third alteration comprising, responsive to predicting that the time series will go below a threshold, a third reconfiguration of the file system, the third reconfiguring altering a capacity of the file system on the storage device.

16 . The computer program product of claim 9 , wherein the trained classification model comprises a neural network classification model trained using a training set, the training set comprising file system usage data classified into a category in the set of categories.

17 . The computer program product of claim 9 , wherein the stored program instructions are stored in the at least one of the one or more storage media of a local data processing system, and wherein the stored pro

Assignees

• IBM

Inventors

• Shanmugam Sundaravelu
• Venkatesan Vidhya Shankar
• Mahamuni Amol Bhaskar

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/08

  Learning methods · CPC title