AI Server Error Classification for Cloud Monitoring
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Solution Overview
Problem
In cloud computing environments, server errors often go unnoticed due to high traffic volumes, leading to underlying software and hardware issues that can cause detrimental impacts, and existing monitoring methods are inefficient and resource-intensive.
Innovation Solution
A server device processes server logs from multiple cloud servers using artificial intelligence techniques to identify and classify error types, providing actionable information to managers and automating the detection and prediction of errors, thereby reducing mean time to detect (MTTD) and conserving computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional monitoring methods are used to detect server errors, then the system can identify errors, but the process is inefficient and resource-intensive
Solution Approach 1:
The patent replaces traditional mechanical monitoring systems with an AI-based system that uses machine learning models to analyze server logs. The system substitutes conventional monitoring mechanisms with neural networks that can process and classify error patterns, significantly improving detection efficiency while reducing computational resource consumption through intelligent error categorization and prioritization.
Solution Approach 2:
The patent transforms the approach to error monitoring by changing key parameters: instead of monitoring all errors uniformly, the system changes the monitoring focus to prioritized error categories based on impact assessment. The system modifies detection parameters to identify errors based on their business impact rather than simple occurrence frequency, enabling more efficient resource allocation for error resolution.
2Reliability
If server errors are not addressed promptly, then high traffic volumes can mask errors, but underlying software and hardware issues cause detrimental impacts
Solution Approach 1:
The patent implements preliminary action by continuously training and updating AI models with historical error data and patterns. The system performs preliminary analysis of server logs to identify potential errors before they manifest as critical failures. By pre-classifying error types and establishing baseline patterns, the system enables faster detection and response when errors occur, reducing mean time to detect while maintaining high reliability.
Solution Approach 2:
The system establishes feedback loops where detected errors and their resolutions are fed back into the AI modeling system. This continuous feedback mechanism allows the system to learn from actual error patterns and improve its detection accuracy over time. The feedback mechanism enables the system to adapt to changing error patterns in the cloud environment, maintaining high reliability while progressively reducing detection time through improved model accuracy.
Data Source
AI summary
A device obtains a plurality of server logs from a plurality of servers, where each server log includes a plurality of log entries, and generates, based on the plurality of log entries, a plurality of data structures, where each data structure includes one or more log entries that concern a server request. The device identifies a set of data structures associated with one or more server errors and processes the set of data structures using an artificial intelligence technique to determine a respective classification score of each data structure. The device determines, based on the respective classification score of each data structure, a respective server error type of each data structure, and causes display of information concerning at least one server error type associated with the set of data structures.