Bayesian Network Root Cause Analysis for Kubernetes Failures
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Solution Overview
Problem
Troubleshooting in dynamic computing environments like Kubernetes is challenging due to complex resource dependencies, requiring tools that can accurately identify the root cause of failures amidst numerous potential causes, which current visualization tools fail to do effectively.
Innovation Solution
A system and method using a Bayesian network generated from a correlation directed acyclic graph (CDAG) of computing system entities, with edge weights derived from log entries and a dictionary file, to compute posterior probabilities of possible failure causes, thereby identifying probable causes with associated likelihoods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current visualization tools are used to analyze dependencies, then resource dependencies can be visualized, but the root cause of failures cannot be accurately identified
Solution Approach 1:
The patent introduces a Bayesian network as an intermediary computational model between the complex dependency graph and the failure analysis task. This intermediary layer processes the dependency relationships and computes posterior probabilities, enabling accurate root cause identification without requiring direct complex analysis of all resource dependencies by the user.
Solution Approach 2:
The patent replaces manual visual analysis and mechanical troubleshooting processes with an automated probabilistic computation system. The Bayesian network automatically computes failure probabilities based on observed failures and dependency relationships, substituting human expert analysis with algorithmic probability computation.
2Productivity
If manual analysis of resource dependencies is performed, then potential causes can be identified, but significant time is required for root cause analysis
Solution Approach 1:
The patent performs preliminary action by pre-computing the Bayesian network structure and conditional probability tables from the dependency graph before failure analysis is needed. When a failure occurs, the system can quickly compute posterior probabilities using the pre-prepared model, significantly reducing the time required for root cause analysis compared to building the analysis model from scratch.
3Reliability
If comprehensive dependency tracking is implemented, then all potential failure causes can be identified, but the system becomes more complex and difficult to manage
Solution Approach 1:
The patent extracts only the essential dependency relationships needed for failure analysis from the complete system state, representing them in a simplified Bayesian network structure. This extraction process maintains the necessary information for comprehensive failure analysis while removing unnecessary complexity from the management and visualization of all possible dependencies.
Data Source
AI summary
A system and method for performing a failure analysis on a computing system uses a Bayesian network that is generated from a correlation directed acyclic graph (CDAG) of nodes that represent entities in the computing system with edge weights for edges between the nodes. The CDAG is generated using log entries of the computing system with reference to a dictionary file, which specifies entity types, dependency types of the entity types, expressions that return values representing prior probabilities of failure for the dependency types, and the edge weights for the dependency types. The Bayesian network generated from the CDAG includes conditional probabilities for edges of the Bayesian network based on the edge weights of the CDAG. The Bayesian network is used to compute posterior probabilities of possible causes of a new failure event in the computing system and output at least one probable cause with an associated posterior probability for the new failure event.