Container Event Modeling for Dependency Failure Root Cause Detection
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Solution Overview
Problem
Conventional software deployment scenarios face challenges in identifying and resolving conflicts arising from disparate library versions among concurrently running applications, especially in systems with multiple layers of containers, where tracking the root of the problem is difficult due to complex dependencies.
Innovation Solution
A computer-implemented process logs events performed by source code and software applications in containers to create models that predict dependency relationships, allowing for the identification of potential failure points and generating a software architecture template to address these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If containers are destroyed and recreated when encountering problematic events, then the container can be reset, but the inherent problem conditions persist and root cause identification becomes difficult
Solution Approach 1:
The system performs preliminary actions by logging events and creating dependency models before failures occur. The prediction engine continuously monitors container events and builds models of dependency relationships, enabling proactive identification of potential failures and their root causes before they manifest as container crashes.
Solution Approach 2:
The system implements feedback mechanisms where container events are logged, analyzed, and used to update dependency models. The prediction engine uses this feedback to continuously improve its ability to predict failures and identify root causes, creating a closed-loop system that learns from past events.
2Adaptability or versatility
If multiple applications use disparate library versions, then application flexibility is maintained, but conflicts arise and dependency failures increase
Solution Approach 1:
The prediction engine acts as an intermediary between applications and their dependencies. It monitors and analyzes the interactions between applications and libraries, identifying conflicting dependency relationships and predicting potential failures before they occur, thereby mediating the conflicts between application flexibility and dependency reliability.
3Measurement precision
If container events are logged and analyzed to predict failures, then failure prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of failure prediction into distinct components: event logging, event analysis, dependency model creation, and failure prediction. Each component handles a specific aspect of the problem, making the overall system more manageable while improving prediction accuracy through specialized processing at each stage.
Data Source
AI summary
A method for tuning software container interdependent systems includes receiving, by a computer processor operating a prediction engine, data from one or more software containers. The data includes operations from one or more software applications in the software containers. The prediction engine identifies a plurality of dependencies in the operations. The prediction engine develops a model of interdependencies based on the identified dependencies. The prediction engine determines one or more failure candidates in the identified dependencies. The prediction engine generates a software architecture template based on the identification of failure candidates.