Microservice Architecture Risk Detection Using Chaos Graph Machine Learning
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Solution Overview
Problem
Managing and identifying technology architecture risk, particularly in distributed microservice architectures, is challenging due to the complexity and potential for service disruptions, necessitating improved methods to predict and remediate operational risks.
Innovation Solution
A method involving generating chaos graph patterns, training a machine learning model to recognize these patterns, and using the model to identify and predict remedial reconfigurations for architecture design based on similarity to robust and disruptive patterns, which can be automatically applied or presented to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed microservice architectures are implemented to improve system flexibility and scalability, then adaptability and productivity are improved, but device complexity and difficulty of detecting and measuring architecture risk increase
Solution Approach 1:
The patent replaces manual architecture risk assessment with an automated machine learning system that processes architecture graphs and chaos graph patterns. The ML model automatically identifies risky architecture patterns and generates remediation recommendations, substituting human analysis with automated computational methods.
Solution Approach 2:
The patent introduces chaos graph patterns as an intermediary representation layer between the architecture graph and the ML model. These patterns serve as mediators that capture critical architecture characteristics and risk indicators, making the complex architecture data more suitable for machine learning processing.
2Ease of operation
If manual methods are used to identify and remediate architecture risk, then ease of operation is maintained, but productivity and speed of risk remediation deteriorate
Solution Approach 1:
The system enables self-service architecture risk management by automatically analyzing architecture graphs, identifying risky patterns, and generating remediation recommendations without requiring manual intervention. The ML model autonomously performs the entire risk assessment workflow.
Solution Approach 2:
The patent implements a feedback loop where the ML model continuously learns from chaos graph patterns and architecture graph analyses. The system processes remediation outcomes and uses this feedback to improve future risk predictions and recommendations, creating a self-improving system.
3Reliability
If comprehensive architecture analysis is performed to improve reliability and identify all potential risks, then measurement precision is improved, but loss of time and computational resources increases
Solution Approach 1:
The patent extracts only the most critical and informative features from architecture graphs for ML analysis. Instead of processing all possible architecture attributes, the system identifies and extracts key structural patterns and relationships that are most relevant for risk detection, reducing analysis complexity.
Solution Approach 2:
The patent performs preliminary processing of architecture data by generating chaos graph patterns that pre-compute and store critical architecture characteristics. This preliminary action prepares the data in advance, making the actual ML analysis faster and more efficient when risk assessment is needed.
Data Source
AI summary
Systems and methods for identifying and remediating architecture risk are disclosed. In one aspect, a method includes generating a first chaos graph pattern and a second chaos graph pattern; training a machine learning model to recognize the first chaos graph pattern and the second chaos graph pattern; identifying an architecture graph pattern of an evaluated architecture; including the architecture graph pattern in an architecture testing graph; recognizing by the machine learning model that a shape of the architecture graph pattern is similar to a shape of the first chaos graph pattern and that the shape of the architecture graph pattern is similar to a shape of the second chaos graph pattern; and predicting a remedial reconfiguration, wherein the remedial reconfiguration includes a reconfiguration of a design of the evaluated architecture.