Inference Model for Microservices Incident Root Cause Detection
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Solution Overview
Problem
In software development, particularly with microservices architecture, identifying the root cause of incidents is challenging due to numerous abstraction layers, leading to increased time and resource wastage in resolving issues, as changes in one service can affect others indirectly, making it difficult to pinpoint the source of failures.
Innovation Solution
An inference-based incident detection and reporting system that uses an inference model to analyze changes and incident data from various sources, generating dependency maps and scores to predict the potential source of errors, thereby focusing efforts on the root cause, implemented using AWS Lambda for efficient processing and visualization through tools like Slackbots and spreadsheet reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If microservices architecture is used to enable independent scaling and updates, then service flexibility and deployment speed are improved, but incident root cause identification becomes more difficult due to increased system complexity and abstraction layers
Solution Approach 1:
The patent introduces an inference model as an intermediary component that sits between the complex microservices system and the incident analysis process. This model automatically analyzes service dependencies, change data, and incident information to infer root causes, thereby mediating the complexity gap without requiring engineers to manually navigate the complex system architecture
Solution Approach 2:
The patent replaces the manual mechanical process of incident analysis (where engineers systematically check each service and change) with an automated inference model that uses machine learning to predict root causes. This substitution eliminates the need for human engineers to manually traverse abstraction layers and service dependencies
2Measurement precision
If engineers manually analyze each service and change to identify incident root causes, then thorough investigation is achieved, but time and resources are wasted due to the large number of services and changes
Solution Approach 1:
The inference model performs preliminary analysis of service dependencies, recent changes, and incident patterns before engineers begin their investigation. By pre-calculating probability scores and identifying likely root causes in advance, the system prepares the ground for faster incident resolution without sacrificing analysis thoroughness
Solution Approach 2:
The patent transforms the incident analysis problem from a qualitative manual process into a quantitative automated process by introducing probability scores and metrics. The inference model calculates numerical probabilities for each potential root cause based on multiple parameters (service dependencies, change frequency, incident history), enabling rapid comparison and decision-making
3Ease of operation
If focus is placed on the downstream service showing errors, then immediate symptom addressing is achieved, but the actual upstream source of the failure is missed
Solution Approach 1:
Instead of starting analysis from the downstream service showing errors and working forward, the inference model inverts the approach by analyzing upstream services and changes that could have caused the downstream failure. The model traces dependencies backward to identify the actual source of the problem, preventing engineers from mistakenly focusing on symptomatic downstream services
Data Source
AI summary
Systems and methods that may be used to provide an inference-based incident detection and reporting process. In one or more embodiments, an inference model may be used to track and analyze changes and incident data from various sources of record to quickly predict potential sources of errors and help focus in on the root cause of a software incident.