Root Cause Anomaly Detection in Distributed Systems
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Solution Overview
Problem
Existing technologies face challenges in determining the root cause of application performance issues in distributed systems, due to increased data usage and complexity, leading to higher costs and time in resolving issues.
Innovation Solution
A system is configured to pinpoint root causes of anomalies by generating a graph based on infrastructure templates, additional information, and metrics, using algorithms to establish dependencies and identify contributing factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional tools and resources are used to pinpoint the root cause, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent segments the complex monitoring system into multiple specialized components: anomaly detection module, graph generation module, root cause analysis module, and recommendation module. Each component handles a specific aspect of the problem, reducing overall system complexity while maintaining high measurement precision through coordinated operation of these modular elements.
Solution Approach 2:
The patent introduces an intermediary graph structure that represents relationships between system components. This graph acts as a mediator between raw monitoring data and root cause identification, transforming complex data relationships into a structured format that simplifies analysis while preserving measurement precision.
2Measurement precision
If additional tools and resources are used to pinpoint the root cause, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary actions by continuously generating and updating the graph structure representing system relationships before anomalies occur. This pre-established structural model enables rapid root cause identification when anomalies are detected, achieving high measurement precision without increasing issue resolution time.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors performance metrics, updates the relationship graph, and validates root cause predictions against actual system behavior. This feedback loop ensures accurate measurement precision while maintaining efficient resolution times through iterative refinement.
3Measurement precision
If more data is collected and analyzed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent extracts only the essential relationship information from vast amounts of monitoring data to construct the graph structure. By taking out only the critical relationship elements rather than processing all raw data, the system achieves high measurement precision while significantly reducing computational energy consumption.
Solution Approach 2:
The patent changes the parameter representation from raw voluminous data to structured relationship graphs with defined attributes. This parameter transformation reduces the dimensional complexity of the data, enabling accurate anomaly detection with lower energy consumption by working with transformed rather than raw data parameters.
Data Source
AI summary
A system obtains a graph representing a set of resources of a distributed system. At least one node of the graph represents a resource-metric pair. The system further obtains time series data that indicates anomalies from the system. Then, the system determines a root cause anomaly that caused other anomalies based at least in part on the graph and the time series data.