Data Protection Advisor for Human Configuration Error Detection

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Solution Overview

Problem

In large data centers, determining the cause of errors or failures is challenging, particularly when they are related to human configuration changes during periodic events like backups or data replication, as existing methods lack efficiency in isolating and resolving such errors quickly.

Innovation Solution

A data protection advisor application automatically detects errors by analyzing configuration, status, and event data, identifying configuration changes made by human users, and provides feedback with suggested resolutions, enabling quick identification and remediation of error causes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual error detection methods are used in large data centers, then operators can identify errors, but the process is time-consuming and inefficient for isolating human configuration errors during periodic events

Engineering Contradiction:

Improveerror detection speedVSAvoidtime to isolate and resolve errors

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs self-diagnosis by automatically collecting configuration data, event data, and status data, then analyzing them to identify human configuration errors without requiring manual operator intervention for each error detection case

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical error detection processes are replaced with an automated electronic system that collects data from multiple sources, processes it through analysis algorithms, and generates error identification results automatically

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If comprehensive data collection is performed to accurately identify error causes, then detection accuracy improves, but system complexity increases

Engineering Contradiction:

Improveerror cause identification accuracyVSAvoiddata collection and analysis system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The data collection system is segmented into distinct modules: a configuration data collector that gathers configuration information, an event data collector that captures periodic event data, and a status data collector that monitors system status, with each module independently collecting specific data types

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

An analysis module acts as an intermediary that receives data from multiple collection modules, processes the combined information, and generates error identification results, thereby managing the complexity of integrating comprehensive data from diverse sources

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10452464B1Automatic human configuration error detection for reoccurring events

Publication Date: 2019.10.22 EMC IP HLDG CO LLC

AI summary

Detection of a cause of an error occurring in a data center is provided. A time period between a failure event in the data center and a previous successful event is determined. A set of devices involved in the failure event is generated using a data protection advisor (DPA). Data collected for the set of devices during the determined time period is scanned to detect at least one configuration change made during the determined time period. Based on a result of the scanned collected data, the at least one configuration change is displayed as a potential root cause of the error.