ML-Based Dependency Graph Focus for Microservice Debugging
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Solution Overview
Problem
In large-scale microservices-based applications, debugging and identifying the root cause of issues is challenging due to the complexity of dependency graphs, which often result in overwhelming visual representations and inefficient user interaction, leading to increased debug time and application downtime.
Innovation Solution
A machine learning-based classification model is used to analyze nodes in a dependency graph, generating scores that indicate the likelihood of each compute resource being problematic, thereby focusing user attention on the most critical nodes and edges, reducing visual complexity and improving debugging efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all nodes and edges in the dependency graph are displayed with equal detail, then complete information is provided, but visual complexity increases and debugging efficiency decreases
Solution Approach 1:
The patent applies local quality by differentiating the visual representation of nodes and edges based on their problem likelihood scores. High-score nodes and edges receive enhanced visual focus (different coloring, sizing, or highlighting) while low-score elements are de-emphasized. This allows the system to maintain information completeness while reducing visual complexity through selective emphasis on critical areas.
Solution Approach 2:
The dependency graph is segmented into priority groups based on machine learning scores. The system divides nodes and edges into different visual categories (e.g., high-priority problematic areas vs. normal operating areas), allowing users to focus on critical segments first. This segmentation resolves the contradiction by organizing information hierarchically rather than presenting all elements equally.
2Reliability
If focus is provided to all nodes and edges, then no problematic resources are missed, but debug time increases due to overwhelming visual information
Solution Approach 1:
The patent implements partial action by providing visual focus selectively to only those nodes and edges that exceed a threshold score or rank in the top N problematic areas. Instead of emphasizing all elements equally, the system applies focus enhancement only to the most likely problematic regions, reducing the time users spend scanning irrelevant areas while maintaining reliable problem detection through the ML-based scoring system.
Solution Approach 2:
The system replaces manual inspection mechanics with automated machine learning-based scoring and visual prioritization. The ML model automatically identifies problematic areas, and the system automatically applies visual focus to those areas, eliminating the need for users to manually evaluate each node and edge, thus reducing debug time while maintaining detection accuracy.
3Loss of information
If visual focus is provided to many nodes and edges, then comprehensive coverage is achieved, but user attention is分散 and debugging efficiency decreases
Solution Approach 1:
The patent changes the visual parameters (color intensity, node size, edge thickness) of dependency graph elements based on their problem likelihood scores. By dynamically adjusting these visual parameters, the system maintains comprehensive coverage of all nodes and edges in the underlying data structure while presenting a streamlined view to users that highlights only the most critical areas, thereby improving debugging efficiency without losing information completeness.
Data Source
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AI summary
Methods, systems, apparatuses, and computer-readable storage mediums are described for machine learning-based techniques for reducing the visual complexity of a dependency graph that is representative of an application or service. For example, the dependency graph is generated that comprises a plurality of nodes and edges. Each node represents a compute resource (e.g., a microservice) of the application or service. Each edge represents a dependency between nodes coupled thereto. A machine learning-based classification model analyzes each of the nodes to determine a likelihood that each of the nodes is a problematic compute resource. For instance, the classification model may output a score indicative of the likelihood that a particular compute resource is problematic. The nodes and/or edges having a score that exceed a predetermined threshold are provided focus via the dependency graph.