Microservice Dependency Analysis for Probable Error Cause Reporting
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Solution Overview
Problem
In modern cloud deployments with microservices, identifying the probable cause of intermittent issues caused by subtle inter-service compatibility problems is challenging due to the lack of effective tools for tracking dependencies and changes across distributed services.
Innovation Solution
A network analyzer is used to store service dependency data and version control information, enabling it to identify impacted services and upstream services based on inter-service dependencies, and select candidate modifications as probable causes of errors by applying time-based filtering and weight-based relevance scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If microservices are deployed as autonomous components that can be developed, deployed, operated, and scaled independently, then service flexibility and independence are improved, but identifying the source of errors becomes more difficult
Solution Approach 1:
The system performs preliminary actions by automatically tracking service dependencies and changes before errors occur. It maintains a database of service dependency information and version control data, enabling proactive identification of potential issues rather than reactive debugging after failures manifest.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring service performance metrics and comparing them against baseline data. When deviations are detected, the system provides feedback about potential root causes by analyzing which service changes correlate with performance degradation, enabling faster troubleshooting in autonomous microservice architectures.
2Ease of manufacture
If any changes are made in microservices, then service functionality is improved, but performance of other microservices may be impacted
Solution Approach 1:
The system performs preliminary analysis by tracking all service changes and their potential impacts before deployment. It maintains version control information and dependency maps, enabling pre-deployment assessment of how changes in one service might affect others, allowing teams to make informed decisions about change deployment strategies.
Solution Approach 2:
The system establishes feedback loops that monitor service performance metrics continuously. When changes are deployed, the system compares new performance data against baselines and provides feedback about potential negative impacts. This enables rapid detection and response to performance degradation caused by service changes.
3Measurement precision
If manual analysis of service dependencies and changes is performed, then accuracy of root cause identification is improved, but time to repair services increases
Solution Approach 1:
The system performs self-service by automatically analyzing service dependencies, version control data, and performance metrics to identify root causes. It autonomously processes large amounts of data from multiple services and their interactions, eliminating the need for manual analysis while maintaining high accuracy through systematic evaluation of all available data.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational systems. Instead of human operators manually tracing dependencies and changes, the system uses computer processing to automatically analyze service dependency databases, version control information, and performance metrics, significantly reducing analysis time while maintaining precision through algorithmic evaluation.
Data Source
AI summary
Examples described herein relate to a method and a network analyzer configured to report a probable cause of errors in a microservice environment. In some examples, the network analyzer may identify an impacted service that reported an error. Further, the network analyzer identifies one or more upstream services related to the impacted service based on a service dependency between the one or more upstream services and the impacted service. Furthermore, the network analyzer identifies at least one modification in one or more of the impacted service or the one or more upstream services based on respective versions of the impacted service and the one or more upstream services, then reports a set of candidate modifications selected from the at least one modification as probable causes of the error.