Hypernode Graph Clustering for Distributed Application Fault Localization
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Solution Overview
Problem
Fault localization in distributed applications with microservice architectures is challenging due to the complexity of causality among services and the overhead of instrumentation and tracing, as existing techniques fail to account for causality and tokens flowing across services.
Innovation Solution
A system that generates a hypernode graph using causality data from log data to cluster services into groups, utilizing a modularity metric and token vectors to facilitate fault localization, accounting for causality and token flow across services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If instrumentation and tracing are used to monitor services, then fault detection capability is improved, but system overhead increases
Solution Approach 1:
The patent extracts only the necessary causality information from log data without implementing full instrumentation and tracing across all services. By selectively analyzing log data to infer causality relationships, the system achieves fault detection capability while avoiding the overhead of comprehensive tracing instrumentation.
Solution Approach 2:
The patent creates a hypernode graph that is a simplified copy or representation of the actual service dependency relationships. This graph model captures essential causality information without requiring direct instrumentation of the production system, enabling fault detection with minimal overhead.
2Ease of manufacture
If textual analysis of log lines is used to group services, then implementation simplicity is improved, but fault localization accuracy deteriorates
Solution Approach 1:
The patent transitions from one-dimensional textual analysis to multi-dimensional analysis by incorporating causality relationships, token flow patterns, and temporal sequences. The hypernode graph adds structural dimensions to the analysis, enabling more accurate fault localization while building upon simple log data.
Solution Approach 2:
The patent changes the analysis parameters from simple text matching to include causality strength, token flow frequency, and temporal relationships. By adjusting these parameters in the objective function, the system achieves higher fault localization accuracy while maintaining implementation feasibility.
3Productivity
If services are clustered into groups, then fault localization efficiency is improved, but complexity of grouping methodology increases
Solution Approach 1:
The patent segments the service clustering process into distinct components: hypernode graph construction, token vector generation, and objective function optimization. This segmentation makes the complex grouping methodology more manageable and implementable by breaking it down into modular steps.
Solution Approach 2:
The hypernode graph serves as an intermediary structure that simplifies the clustering process. Instead of directly clustering services based on complex multi-factor analysis, the system first builds the hypernode graph as an intermediate representation, then performs clustering on this structured data, reducing overall complexity.
Data Source
AI summary
Systems, devices, computer-implemented methods, and/or computer program products that facilitate fault localization for distributed applications. In one example, a system can comprise a process that executes computer executable components stored in memory. The computer executable components can comprise a graph compiler and a partitioning component. The graph compiler can generate a hypernode graph for a distributed application using causality data inferred from log data generated by services emitting errors. The distributed application can comprise the services. The partitioning component can cluster the services into a plurality of groups using the hypernode graph and an objective function.