Log Analytics for Cloud Problem Diagnosis

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Solution Overview

Problem

The increasing complexity and scale of cloud computing environments lead to overwhelming volumes of operational logs, making it challenging to diagnose problems efficiently due to the complexity of cross-component interactions and the need for human expertise to interpret relevant log entries.

Innovation Solution

A log analytics tool that constructs a reference model of normal log patterns for each request type, allowing for comparison with failed request logs to identify deviations and highlight high-value log entries likely to contain the root cause, using log correlation and comparison techniques combined with visualization interfaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If log analytics is performed manually to diagnose problems in cloud computing environments, then human expertise can interpret relevant log entries, but the overwhelming volume of logs and system complexity make diagnosis time-consuming and inefficient

Engineering Contradiction:

Improvediagnosis accuracyVSAvoiddiagnosis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-diagnosis by automatically comparing failed request logs against reference models to identify deviations and potential root causes, eliminating the need for manual human analysis of overwhelming log volumes while maintaining diagnostic accuracy

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Reference models are pre-built from healthy system logs before failures occur, enabling the system to quickly compare and identify anomalies during actual failures without time-consuming manual analysis

Inventive Principle:

Principle #10Preliminary action

2Productivity

If automated log processing is implemented to reduce diagnosis time, then large volumes of logs can be processed quickly, but the complexity of cross-component interactions makes it difficult to identify relevant log entries

Engineering Contradiction:

Improvelog processing speedVSAvoidlog entry relevance

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The system segments logs by request type and component, organizing overwhelming log volumes into manageable, structured groups that can be efficiently processed and compared against corresponding reference models to identify relevant deviations

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Reference models serve as intermediaries between raw logs and diagnostic conclusions, automatically filtering and highlighting only the log entries that deviate from normal behavior, thus solving the difficulty of identifying relevant entries in complex cross-component interactions

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS9727407B2Log analytics for problem diagnosis

Publication Date: 2017.08.08 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

In a set of problem log entries from a computing system, a subset of the set of problem log entries are identified, which pertain to a failed request. The subset is compared to a reference model which defines log entries per request type under a healthy state of the computing system, to identify a portion of the subset of problem log entries which deviate from corresponding log entries in the reference model. In the portion of the subset, at least one high-value log entry is identified. The at least one high-value log entry is output.