Maintenance cost estimation

Patent metadata

Field	Value
Publication number	US-2021233003-A1
Application number	US-202016750678-A
Country	US
Kind code	A1
Filing date	Jan 23, 2020
Priority date	Jan 23, 2020
Publication date	Jul 29, 2021
Grant date	—

Abstract

Estimating maintenance for a storage system includes accessing a model that outputs time and materials estimates based on input configuration data, providing configuration data of the storage system to the model, and obtaining an estimate of maintenance time and materials based on the configuration data provided to the model. The model may be provided by a neural network, which may be a self-organized map. Weights of neurons of the self-organized map may be initialized randomly. The model may be initially configured using training data that may include an I/O load of the storage system, memory size of the storage system, a drive count of the storage system, and/or size and parameter information corresponding to hardware being added for the maintenance operation. The training data may include actual time and materials for prior storage system maintenance operations used for the training data. The model may be provided on the storage system.

First claim

What is claimed is:

1 . A method of estimating maintenance for a storage system, comprising:
accessing a model that outputs time and materials estimates based on input configuration data; providing configuration data of the storage system to the model; and obtaining an estimate of maintenance time and materials based on the configuration data provided to the model.

2 . A method, according to claim 1 , wherein the model is provided by a neural network.

3 . A method, according to claim 2 , wherein the neural network is a self-organized map.

4 . A method, according to claim 3 , wherein weights of neurons of the self-organized map are initialized randomly.

5 . A method, according to claim 1 , wherein the model is initially configured using training data.

6 . A method, according to claim 5 , wherein the training data includes at least one of: an I/O load of the storage system, memory size of the storage system, a drive count of the storage system, and size and parameter information corresponding to hardware being added for the maintenance operation.

7 . A method, according to claim 6 , wherein the size and parameter information corresponding to hardware being added includes at least one of: physical storage unit capacity of the hardware, a CPU count of the hardware, and a memory size of the hardware.

8 . A method, according to claim 5 , wherein the training data includes actual time and materials for prior storage system maintenance operations used for the training data.

9 . A method, according to claim 1 , wherein the estimate of maintenance time and materials is broken into separate phases.

10 . A method, according to claim 1 , wherein the model is provided on the storage system.

11 . A non-transitory computer readable medium containing software that estimates maintenance for a storage system, the software comprising:
executable code that accesses a model that outputs time and materials estimates based on input configuration data; executable code that provides configuration data of the storage system to the model; and executable code that obtains an estimate of maintenance time and materials based on the configuration data provided to the model.

12 . A non-transitory computer readable medium, according to claim 11 , wherein the model is provided by a neural network.

13 . A non-transitory computer readable medium, according to claim 12 , wherein the neural network is a self-organized map.

14 . A non-transitory computer readable medium, according to claim 13 , wherein weights of neurons of the self-organized map are initialized randomly.

15 . A non-transitory computer readable medium, according to claim 11 , wherein the model is initially configured using training data.

16 . A non-transitory computer readable medium, according to claim 15 , wherein the training data includes at least one of: an I/O load of the storage system, memory size of the storage system, a drive count of the storage system, and size and parameter information corresponding to hardware being added for the maintenance operation.

17 . A non-transitory computer readable medium, according to claim 16 , wherein the size and parameter information corresponding to hardware being added includes at least one of: physical storage unit capacity of the hardware, a CPU count of the hardware, and a memory size of the hardware.

18 . A non-transitory computer readable medium, according to claim 15 , wherein the training data includes actual time and materials for prior storage system maintenance operations used for the training data.

19 . A non-transitory computer readable medium, according to claim 1 , wherein the estimate of maintenance time and materials is broken into separate phases.

20 . A non-transitory computer readable medium, according to claim 11 , wherein the software is provided on the storage system.

Assignees

• Emc Ip Holding Co Llc

Inventors

• Brit-Artzi Hagit
• Alshawabkeh Malak
• Don Arieh

Classifications

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title

• G06Q10/20 Primary

  Administration of product repair or maintenance · CPC title

• G06Q10/06315 Primary

  Needs-based resource requirements planning or analysis · CPC title