Log File Remediation via ML Risk Scoring

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Solution Overview

Problem

Conventional approaches to handling and triaging service requests in IT infrastructure are highly manual, making it difficult to efficiently identify the root cause of issues from log files, which hampers the speed and accuracy of service request remediation.

Innovation Solution

An apparatus and method utilizing a machine learning model, such as BERT, to process log files by splitting them into segments, generating log pattern identifiers, determining risk scores, and identifying critical areas based on these scores, thereby automating the identification of remedial actions for service requests.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual review of log files is used to identify root causes, then support engineers can analyze issues, but the process is time-consuming and inefficient

Engineering Contradiction:

Improveservice request remediation speedVSAvoidtime to identify root cause

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The log file is divided into multiple segments, each processed independently by the machine learning model to generate risk scores. This segmentation enables parallel processing and reduces the overall time required to analyze large log files while maintaining comprehensive coverage of all log entries.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The manual mechanical process of support engineers reviewing log files is replaced with an automated machine learning-based system. The ML model automatically segments logs, generates risk scores, identifies critical areas, and suggests remedial actions, eliminating the time-consuming manual review process while improving productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If support engineers manually analyze log files to determine root causes, then issues can be identified, but the workload and complexity increase

Engineering Contradiction:

Improveaccuracy of root cause identificationVSAvoidcomplexity of analysis process

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The machine learning model acts as an intermediary between the raw log files and the support engineers. It processes the complex log data, generates risk scores, identifies critical areas, and presents simplified findings with suggested remedial actions. This intermediary layer maintains high accuracy in root cause identification while reducing the perceived complexity for engineers.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system transforms raw log data into meaningful risk scores through parameter changes. By converting unstructured log entries into quantifiable risk metrics, the system maintains measurement precision for root cause identification while simplifying the analysis process through standardized scoring mechanisms.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If comprehensive log file analysis is performed to ensure accurate diagnosis, then root causes can be identified, but the processing time increases

Engineering Contradiction:

Improveaccuracy of service request triagingVSAvoidefficiency of issue identification

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary action by pre-segmenting log files and pre-generating risk scores for each segment before comprehensive analysis. This preliminary processing organizes the data in advance, enabling faster and more reliable identification of critical areas without sacrificing diagnostic accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies partial action by focusing computational resources on segments with higher risk scores. After initial comprehensive segmentation, the system prioritizes analysis of high-risk areas, maintaining reliable diagnosis accuracy while improving overall processing efficiency by not uniformly analyzing all segments at the same depth.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11822424B2Service request remediation with machine learning based identification of critical areas of log segments

Publication Date: 2023.11.21 DELL PROD LP

AI summary

An apparatus comprises a processing device configured to receive a service request associated with a given asset, to obtain a log file associated with the given asset, to split the log file into log segments, to generate sets of log pattern identifiers for the log segments, and to determine risk scores for the log segments utilizing a machine learning model that takes as input the sets of log pattern identifiers and provides as output information characterizing risk of the log segments. The processing device is also configured to identify critical areas of the log file based at least in part on the determined risk scores, a given critical area comprising a sequence of log segments having determined risk scores above a designated risk score threshold. The processing device is further configured to analyze the identified critical areas to determine remedial actions to be applied for resolving the service request.