Log Export Time Estimation for Data Protection

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Solution Overview

Problem

Existing data protection products lack the ability to accurately estimate the time and size of a log file during export, due to the complexity of data storage environments and varying computing resources, leading to unpredictable user experiences.

Innovation Solution

A method that acquires attributes related to the target asset, tasks, and computing resources, using a machine learning model to determine the export time and size of a log, incorporating attributes such as asset types, task frequencies, and hardware specifications to provide accurate predictions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If data protection products export logs in complex storage environments, then the log export function is provided, but the export time and file size become unpredictable and difficult to estimate

Engineering Contradiction:

Improvelog export functionVSAvoidexport time prediction

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary estimation of log export time and file size before actual export operations. By calculating predicted values in advance based on asset attributes, task characteristics, and computing resource status, users can plan resource allocation and schedule exports without unexpected delays

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system establishes a feedback mechanism where actual export results are compared with predicted values, and the difference is used to continuously optimize the prediction model. This closed-loop approach improves estimation accuracy over time by learning from real export performance data

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If data protection products support multiple asset types and tasks, then comprehensive protection is achieved, but the complexity of estimating log parameters increases

Engineering Contradiction:

Improveasset and task coverageVSAvoidestimation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system manages complexity by parameterizing the estimation process. It identifies key parameters such as asset attributes, task characteristics, and computing resource status that influence log export, and uses these parameters to calculate predictions without requiring complex analysis of all possible variations

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system implements a universal estimation framework that works across multiple asset types and task kinds. By creating a generalized prediction model that handles diverse scenarios through common parameters and algorithms, it provides comprehensive support without proportionally increasing estimation complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12174721B2Method, electronic device, and computer program product for exporting log

Publication Date: 2024.12.24 DELL PROD LP

AI summary

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product that involve exporting a log. The method includes acquiring a first set of attributes indicating a target asset among assets protected by a data protection product, a second set of attributes indicating target tasks executed on the target asset, and a third set of attributes indicating a computing resource running the data protection product. The method further includes determining an export time consumed to export a log of the target asset based on the first set of attributes, the second set of attributes, and the third set of attributes. With the embodiments of the present disclosure, the time required for exporting a log can be accurately estimated while the log is exported.