Automated Root Cause Identification for Distributed Service Latency

Solution Overview

Problem

Conventional techniques for monitoring and detecting performance issues in distributed systems, such as data centers and cloud computing systems, are inefficient and require extensive manual configuration and review, leading to potential service outages and performance degradation due to the inability to accurately identify root causes of latency and capacity issues.

Innovation Solution

A method that involves performing load tests on services to collect performance metrics, calculating severity scores based on deviations from baseline performance, and using machine learning models to identify potential root causes of performance issues, which are then grouped by call paths to reduce false positives and automate the identification process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual monitoring and review of performance metrics is performed, then detection capability is improved, but time consumption and operational complexity increase

Engineering Contradiction:

Improvedetection capabilityVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system automatically performs load tests, collects performance metrics, calculates severity scores, and identifies root causes without requiring manual intervention. The automated root cause identification system serves itself by continuously monitoring and analyzing performance data, eliminating the need for manual review while maintaining high detection capability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical review processes with automated computational analysis. Machine learning models and automated algorithms substitute human engineers in analyzing performance metrics and identifying root causes, dramatically reducing time consumption while maintaining or improving detection accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual review of performance metrics is performed, then detection capability is improved, but device complexity increases

Engineering Contradiction:

Improvedetection capabilityVSAvoidoperational complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system automatically performs load tests, collects performance metrics, calculates severity scores, and identifies root causes without requiring manual intervention. The automated root cause identification system serves itself by continuously monitoring and analyzing performance data, eliminating the need for manual review while maintaining high detection capability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical review processes with automated computational analysis. Machine learning models and automated algorithms substitute human engineers in analyzing performance metrics and identifying root causes, dramatically reducing time consumption while maintaining or improving detection accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If aggregated performance metrics are used, then monitoring efficiency is improved, but measurement precision deteriorates

Engineering Contradiction:

Improvemonitoring efficiencyVSAvoiddetection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent segments the analysis by grouping services into call paths and identifying root causes at different levels (individual services, call paths, and systems). This hierarchical segmentation allows efficient aggregated monitoring while maintaining precision by drilling down to specific root cause locations when anomalies are detected.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent adds the dimension of call path analysis to traditional service-level monitoring. By organizing services into call path hierarchies and analyzing performance metrics across multiple dimensions (individual service, call path, system), the system maintains monitoring efficiency while improving detection accuracy through multi-dimensional analysis.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS10983856B2Identifying root causes of performance issues

Publication Date: 2021.04.20 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

The disclosed embodiments provide a system for identifying root causes of performance issues. During operation, the system obtains a call graph containing a set of call paths for a set of services. Next, the system determines, based on a load test of the set of services, severity scores for the set of services, wherein the severity scores represent levels of abnormal behavior in the set of services. The system then groups the severity scores by the set of call paths and identifies, based on the grouped severity scores, one or more services as potential root causes of performance issues in the set of services. Finally, the system outputs the identified one or more services as the potential root causes of the performance issues.