Fault Localization in Distributed Systems via Invariant Analysis
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Solution Overview
Problem
Existing methods for fault detection and isolation in large-scale distributed systems struggle to effectively rank monitoring metrics to identify the root cause of anomalies, as they rely solely on broken invariants without considering the influence of neighboring metrics.
Innovation Solution
The method employs graph analysis and neighborhood information to calculate anomaly scores by integrating immediate neighbor scores, using mRank and gRank processes to determine the anomaly ranking of metrics, and introduces temporal ranking through an ARX model to predict and compare expected and observed broken invariant patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fault detection methods are used that only consider broken invariants, then the detection process is simple, but the accuracy of root cause identification is low
Solution Approach 1:
The method segments the fault analysis process into multiple scoring components: broken invariant scoring, neighbor metric scoring, and temporal pattern scoring. Each component evaluates different aspects of metric anomaly independently, then combines them to produce a comprehensive root cause ranking that improves identification accuracy while maintaining manageable complexity through modular analysis.
Solution Approach 2:
The invention adds temporal dimension to traditional invariant analysis by computing temporal scores that evaluate whether the timing pattern of broken invariants matches expected patterns for metric faults. This temporal dimension transforms the analysis from static invariant checking to dynamic pattern matching, significantly improving root cause identification accuracy.
2Measurement precision
If comprehensive metric analysis considering neighborhood information is performed, then the ranking accuracy improves, but the computational complexity increases
Solution Approach 1:
The method applies local quality by focusing computational resources on local neighborhood relationships in the invariant network. Instead of analyzing all metrics globally, it computes scores based on immediate neighbors of broken metrics, then propagates these scores through the network. This localized approach maintains high ranking accuracy while reducing overall computational complexity.
Solution Approach 2:
The invention uses partial action by computing scores for only the most relevant metrics - specifically those with broken invariants and their immediate neighbors - rather than performing exhaustive analysis on all system metrics. This selective scoring approach achieves sufficient ranking accuracy with reduced computational power requirements.
3Measurement precision
If temporal patterns are analyzed using ARX models, then the fault detection precision improves, but the analysis time increases
Solution Approach 1:
The method performs preliminary action by pre-computing temporal scores and expected invariant breakage patterns before final root cause determination. The ARX models are used to generate temporal expectations in advance, which are then quickly compared against observed patterns during fault analysis, reducing real-time investigation time while maintaining high detection precision.
Solution Approach 2:
The invention substitutes complex temporal pattern recognition with a mathematical model approach using ARX (Auto-Regressive with Exogenous inputs) models. These models provide closed-form solutions for expected invariant breakage patterns, replacing time-consuming manual or heuristic temporal analysis with efficient computational mathematics that maintains high precision.
Data Source
AI summary
A computer implemented method for temporal ranking in invariant networks includes considering an invariant network and a set of broken invariants in the invariant network, assuming, for each time point inside a window W, that each metric with broken invariants is affected by a fault at that time point, computing an expected pattern for each invariant of a metric with assumed fault, said pattern indicative of time points at which an invariant will be broken given that its associated metric was affected by a fault at time t, comparing the expected pattern with the pattern observed over the time window W; and determining a temporal score based on a match from the prior comparing.