Intelligent Log Collection via Dynamic System Scoring
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Solution Overview
Problem
Conventional log collection technologies fail to trigger log collection before an error occurs, fail to identify targeted components for error log collection, and lack dynamic weighted evaluation and average criteria calculation, leading to inefficient error troubleshooting and recovery in information systems.
Innovation Solution
A log collection optimization system that includes a system status monitoring module, a system anomaly detection module, and a target component assignment module, which monitors system status, determines a system score using dynamic weighted calculation and average performance evaluation, and invokes log collection from targeted components before an error occurs, facilitating early error detection and troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional log collection technologies are used, then log collection is performed, but log collection is not triggered before an error occurs and targeted components are not identified
Solution Approach 1:
The system performs preliminary actions by monitoring system status and calculating system scores to predict impending errors before they occur. This triggers log collection in advance, ensuring critical information is captured before the error state manifests, thereby reducing troubleshooting time and improving reliability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring system status, calculating system scores based on multiple criteria, and using this information to dynamically adjust log collection triggers. This feedback loop enables the system to identify targeted components and predict errors, improving both reliability and response time.
2Measurement precision
If dynamic weighted evaluation and average criteria calculation are implemented, then system score for error prediction is improved, but system complexity increases
Solution Approach 1:
The system segments the evaluation process into distinct modules: status monitoring, criteria evaluation, weighted calculation, and average calculation. Each module handles a specific aspect of system score determination, making the complex calculation mechanism more manageable and maintainable while preserving measurement precision.
Solution Approach 2:
The system design implements universal calculation mechanisms that can evaluate multiple criteria (CPU usage, memory usage, I/O load, capacity usage) using the same weighted evaluation and average calculation framework. This multi-functional approach improves measurement precision across different system parameters without proportionally increasing complexity.
Data Source
AI summary
Methods, system, and non-transitory processor-readable storage medium for a log collection optimization system are provided herein. An example method includes monitoring, by a system status monitoring module, at least one system status associated with an information system, where the information system comprises a plurality of system components. A system anomaly detection module determines a system score for the information system, based on a dynamic weighted calculation and an average performance evaluation. The system score facilitates predicting an impending error before the error occurs on the information system. A target component assignment module determines a plurality of targeted components that require log collection based on the system score, wherein the plurality of system components comprises the plurality of targeted components. A log collection module invokes system log collection from the targeted components to initiate the system log collection before the error occurs on the information system.