Computer Architecture Failure Prediction Using Graph-Based Self-Healing
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Solution Overview
Problem
Modern computer architectures, particularly in large-scale networks, are complex and prone to single points of failure that are difficult to detect and analyze, leading to potential data loss, hardware damage, and significant financial losses due to unexpected failures.
Innovation Solution
A two-stage machine learning system analyzes architecture diagrams to create a data graph, determines potential failure points, and performs remedial actions, including automated restarts of software components to mitigate failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundancy is added to computer architectures to improve reliability, then failure resistance improves, but device complexity increases and single points of failure become harder to detect
Solution Approach 1:
The patent segments the complex architecture into a graph data structure where nodes represent components and edges represent relationships. This segmentation allows the system to manage complexity by breaking down the architecture into analyzable units while maintaining the ability to detect single points of failure within the redundant structure.
Solution Approach 2:
The patent introduces an intermediary machine learning-based analysis system that acts as a mediator between the complex architecture and the operator. This intermediary automatically analyzes the graph data to identify failure risks, eliminating the need for operators to manually navigate the complex architecture while maintaining high reliability.
2Measurement precision
If automated analysis systems are implemented to detect failure points, then measurement precision of failure detection improves, but device complexity and implementation cost increase
Solution Approach 1:
The patent replaces manual mechanical analysis of architecture diagrams with an automated machine learning system. The system uses trained models to automatically process graph data and identify failure points with high precision, eliminating the need for complex manual analysis procedures while reducing implementation complexity through automation.
Solution Approach 2:
The patent creates a digital copy of the architecture in the form of a graph data structure that mirrors the actual system. This copy can be analyzed repeatedly by machine learning models without adding physical complexity to the original system, allowing precise failure detection through virtual analysis rather than physical modification.
3Ease of operation
If manual analysis of architecture diagrams is performed to identify failure points, then ease of operation is maintained, but productivity and response time to failures decrease
Solution Approach 1:
The patent performs preliminary action by continuously maintaining an updated graph data structure of the architecture and pre-training machine learning models to recognize failure patterns. When a failure occurs, the system can immediately query the pre-built graph and apply pre-trained detection algorithms, achieving both ease of operation through automated response and high productivity through rapid failure identification.
Data Source
AI summary
Aspects discussed herein may relate to methods and techniques for using a multi-step approach to automatically analyze the computer architecture to determine the possible points of failure. A first stage may process, such as by using machine learning, one or more architecture diagrams the system architecture using hardware and/or software. The first stage may create a data graph that summarizes the network architecture and its relationships. A second stage may take the data graph and process it to determine likely points of failure and/or areas where redundancy may be needed. The system may then determine remedial steps to take in the case of failure