Noise-Subtracted Call Trace Classification for Root-Cause Analysis
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Solution Overview
Problem
The increasing complexity of distributed computing systems has led to management and administration challenges, including significant inefficiencies and computational overheads, making traditional approaches to automated management and administration impractical for diagnosing operational problems and failures.
Innovation Solution
The use of call-trace-classification rules generated from noise-subtracted datasets to facilitate root-cause analysis, which partitions automatically labeled call traces and provides diagnostic suggestions for distributed-application and distributed-computer-system managers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional automated management and administration approaches are used to diagnose operational problems in distributed computing systems, then existing management functionalities can be maintained, but the systems become increasingly complex and computationally overhead increases significantly
Solution Approach 1:
The patent extracts the essential diagnostic functionality from complex automated management systems by using call-trace-classification rules that filter and classify call traces to identify failures. This extraction separates the core diagnostic task from the surrounding management overhead, enabling reliable diagnosis without requiring complex management systems.
Solution Approach 2:
The patent changes the parameter of data representation by transforming raw call traces into classified categories using decision trees and classification rules. This parameter transformation simplifies the diagnostic approach from analyzing complex raw data to categorizing traces into failure types, reducing computational overhead while maintaining diagnosis reliability.
2Measurement precision
If comprehensive monitoring of all call traces is implemented to ensure complete diagnostic coverage, then diagnostic accuracy improves, but computational overhead and time required for analysis increases significantly
Solution Approach 1:
The patent segments the analysis process into two stages: (1) collecting and storing call traces during normal operation, and (2) classifying traces using pre-generated decision trees when failures occur. This segmentation allows comprehensive monitoring to be performed efficiently without real-time analysis overhead, as traces are only analyzed when needed and are classified using pre-computed rules.
Solution Approach 2:
The patent performs preliminary action by pre-generating call-trace-classification rules and decision trees during system training phase using labeled datasets. These pre-computed classification rules are then applied during actual failure diagnosis, eliminating the need for real-time complex analysis and significantly reducing diagnostic time while maintaining high accuracy.
3Measurement precision
If complex machine learning models are used to classify call traces, then diagnostic precision improves, but the models become difficult to interpret and maintain
Solution Approach 1:
The patent inverts the typical approach by not using complex black-box machine learning models, but rather constructing interpretable decision trees and classification rules that can be manually generated and maintained. The system trains on labeled call traces to generate human-readable rules that map failure patterns to diagnostic conclusions, making both the model and its reasoning transparent and maintainable.
Solution Approach 2:
The patent changes the model representation parameter from complex neural networks to simplified decision trees with hierarchical classification rules. This parameter transformation maintains diagnostic precision by capturing failure patterns through structured if-then rules, while dramatically improving interpretability and maintainability as the rules can be reviewed, modified, and explained in terms of actual failure scenarios.
Data Source
AI summary
The current document is directed to methods and systems that employ call traces collected by one or more call-trace services to generate call-trace-classification rules to facilitate root-cause analysis of distributed-application operational problems and failures. In a described implementation, a set of automatically labeled call traces is partitioned by the generated call-trace-classification rules. Call-trace-classification-rule generation is constrained to produce relatively simple rules with greater-than-threshold confidences and coverages. The call-trace-classification rules may point to particular services and service failures, which provides useful information to distributed-application and distributed-computer-system managers and administrators attempting to diagnose operational problems and failures that arise during execution of distributed applications within distributed computer systems. A first dataset is collected during normal distributed-application operation and a second dataset is collected during problem-associated or failure-associated operation of the distributed application. The first and second datasets are used to generate noise-subtracted call-trace-classification rules and/or diagnostic suggestions.