Error Lattice for Predictive VM Configuration Control
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Solution Overview
Problem
Existing systems face challenges in optimizing the performance and efficiency of computing systems that perform electronic data transfers, particularly in predicting, avoiding, and resolving errors effectively.
Innovation Solution
A computing platform that receives error log files from virtual machine host platforms, generates an error lattice to identify relationships between error codes, determines predicted error outcomes, and applies system configuration updates to prevent errors, utilizing machine learning algorithms and dynamic resource management to distribute updates across virtual machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If system configuration updates are applied to prevent errors, then error prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments error handling by creating an error lattice that categorizes error codes into hierarchical groups and clusters. This segmentation allows the system to manage complexity by organizing errors into manageable units while maintaining high prediction accuracy through pattern recognition across segmented error data.
Solution Approach 2:
The error lattice serves as an intermediary structure between raw error logs and prediction outcomes. It mediates the transformation of unstructured error data into organized error patterns, enabling accurate predictions without requiring the entire system to directly process all error information, thus reducing overall system complexity.
2Productivity
If dynamic resource management is used to distribute updates, then productivity is improved, but device complexity increases
Solution Approach 1:
The system implements dynamic resource management that automatically adjusts error handling resource allocation based on real-time error patterns detected in the lattice. This dynamic adaptation improves productivity by concentrating resources on high-frequency error types while reducing complexity through automated decision-making algorithms that eliminate manual configuration needs.
Solution Approach 2:
The system changes operational parameters by adjusting the granularity and depth of error lattice analysis based on system load and error frequency. This allows the system to optimize between thorough error analysis and resource consumption, improving productivity during high-error periods while reducing computational overhead during normal operation.
Data Source
AI summary
Aspects of the disclosure relate to dynamic system configuration control systems with improved resource allocation techniques. A computing platform may receive commands directing the computing platform to distribute relevant portions of a system configuration update. The computing platform may identify one or more virtual machine host platforms to which the system configuration update is applicable, and may direct applicable virtual machine host platforms to perform system updates based on the system configuration update. The computing platform may generate an error map identifying correlations between error codes and a respective operator for each error code. The computing platform may determine, based on the error map, an operator associated with resolution of various error codes. The computing platform may direct user devices associated with the determine operators to cause display of an operator interface, and may direct a client management computing platform to cause display of an error correction hub.