Error Lattice Predictive Configuration for Virtual Machine Hosts
Find Innovative SolutionsGenerate Solutions
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 while ensuring technical performance and operating efficiency.
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, predicts error outcomes, and applies system configuration updates to prevent errors, utilizing machine learning algorithms and dynamic resource management to distribute updates across virtual machine host platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error handling methods are used in electronic data transfer systems, then system simplicity is maintained, but error prediction capability and resolution efficiency deteriorate
Solution Approach 1:
The system performs preliminary error prediction by analyzing error log files and generating error lattices before actual errors occur in electronic data transfers. The computing platform proactively identifies potential error patterns and relationships, allowing the system to take preventive configuration updates before failures happen, thereby improving reliability without requiring complex real-time intervention mechanisms
Solution Approach 2:
The error lattice serves as an intermediary data structure that bridges raw error log files and predictive error outcomes. This intermediate representation aggregates and structures error codes, relationships, and patterns in a manageable format, enabling the system to process error information efficiently without directly handling the full complexity of raw logs and prediction algorithms simultaneously
2Productivity
If proactive error prediction and prevention mechanisms are implemented, then error resolution efficiency is improved, but system complexity and resource requirements worsen
Solution Approach 1:
The system extracts only the essential error codes, relationships, and patterns from voluminous error log files to construct error lattices. This extraction process isolates the critical predictive information needed for error prediction while discarding redundant data, enabling efficient error resolution without processing the entire log file contents and reducing the computational burden of the prediction system
Solution Approach 2:
The error lattice and system configuration are dynamically updated based on evolving error patterns discovered in log files. The computing platform continuously refines the error lattice structure and adjusts predictive models as new error relationships are identified, allowing the system to adapt its complexity to match the actual error characteristics rather than maintaining fixed high complexity
3Reliability
If system configuration updates are applied frequently to prevent errors, then error avoidance capability is improved, but system stability and performance worsen
Solution Approach 1:
The system applies preliminary anti-action by implementing configuration updates that specifically counteract predicted error conditions before they manifest. Rather than frequent arbitrary updates, the computing platform generates targeted configuration changes based on predicted error outcomes, applying only the necessary corrections to prevent specific error types while maintaining overall system stability
Solution Approach 2:
The system employs feedback mechanisms where error log analysis results feed into error lattice updates, which in turn inform predictive error outcomes and subsequent configuration updates. This closed-loop feedback ensures that configuration changes are based on actual observed error patterns rather than theoretical predictions, improving error avoidance while maintaining stability through evidence-based adjustments
Data Source
AI summary
Aspects of the disclosure relate to error resolution processing systems with improved error prediction features and enhanced resolution techniques. A computing platform may receive error log files identifying error codes corresponding to error occurrences on one or more different virtual machine host platforms. The computing platform may aggregate the error codes corresponding to the error occurrences to generate an error lattice. Using the error lattice, the computing platform may predict an error outcome. Based on the predicted error outcome, the computing platform may determine a system configuration update to be applied to the one or more virtual machine host platforms. The computing platform may direct a dynamic resource management computing platform to distribute relevant portions of the system configuration update to each of the one or more virtual machine host platforms. This may cause the one or more virtual machine host platforms to implement the system configuration update.