Monitoring Computing Infrastructure via Data Consolidation
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Solution Overview
Problem
Existing methods for monitoring large computing infrastructures are inefficient due to the use of multiple evaluation tools providing different formats and potentially misleading results, leading to unnecessary maintenance and downtime.
Innovation Solution
A computer-implemented method that integrates and consolidates evaluation results from various tools by extracting data using specific collectors, converting it to a common format, and using a risk evaluator to determine the presence of issues or vulnerabilities, thereby reducing false positives and unnecessary maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple evaluation tools are used to assess computing devices, then the coverage of detected issues and vulnerabilities increases, but the complexity of integrating and consolidating results from different tools increases
Solution Approach 1:
The patent introduces an intermediary consolidation system that receives evaluation results from multiple independent evaluation tools, standardizes their formats, and integrates them into a unified view. This mediator layer handles the complexity of result integration while allowing the evaluation tools to remain independent and specialized.
Solution Approach 2:
The consolidation system is designed with universal capabilities to handle results from different evaluation tools through a common interface. It provides multi-functional processing including format standardization, duplicate detection, conflict resolution, and unified reporting that works across various tool types.
2Productivity
If evaluation results from multiple tools are consolidated without validation, then the speed of assessment increases, but the accuracy of detected issues decreases due to false positives
Solution Approach 1:
The system performs preliminary validation and cross-checking of evaluation results before final consolidation. By预先 verifying the authenticity of detected issues through multiple evaluation tools and resolving conflicts in advance, the system maintains high assessment speed while ensuring detection accuracy.
Solution Approach 2:
The consolidation system implements feedback mechanisms where detection results are cross-validated against results from other evaluation tools. This feedback loop helps identify and filter false positives while confirming genuine vulnerabilities, thereby maintaining both speed and accuracy.
3Measurement precision
If all detected issues are validated before maintenance, then the reduction of false positives increases, but the time and cost of maintenance operations increase
Solution Approach 1:
The system applies partial validation by prioritizing validation of high-risk and critical issues while using less intensive validation for lower-risk findings. This selective approach reduces the overall time and cost of maintenance while still effectively reducing false positives for the most important issues.
Solution Approach 2:
Different validation depths are applied to different detected issues based on their risk levels and characteristics. Critical vulnerabilities receive comprehensive validation while minor issues use quicker verification methods, optimizing the balance between false positive reduction and maintenance efficiency.
4Reliability
If comprehensive evaluation of all computing devices is performed, then the detection of vulnerabilities increases, but the resource consumption and operational disruption increase
Solution Approach 1:
The evaluation process is segmented into multiple independent evaluation tools, each specializing in specific types of assessments. This segmentation allows parallel execution of different evaluation tasks, improving resource utilization and reducing overall computational overhead while maintaining comprehensive vulnerability detection.
Solution Approach 2:
The system implements periodic evaluation schedules where different types of assessments are performed at different intervals based on risk levels and device criticality. This periodic approach reduces continuous resource consumption while ensuring timely detection of vulnerabilities through strategically timed evaluations.
Data Source
AI summary
A technique for monitoring a computing infrastructure having one or more target devices includes receiving, from a plurality of evaluation services, evaluation results of one or more target devices. The technique further includes extracting, using a different data collector for each of the plurality of evaluation services, data from each of the evaluation results. The technique further includes converting the extracted data to a common format, determining whether an issue or a vulnerability is present in the one or more target devices based on the extracted and converted data, and reporting the issue or the vulnerability.