Hierarchical Issue Localization in Computing Environments
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Solution Overview
Problem
Analyzing measurement data from large computing environments is challenging due to the complexity of recognizing patterns and localizing issues to specific objects, often resulting in inaccurate diagnostics and remediation actions.
Innovation Solution
The implementation of an issue localization engine with vertical and horizontal assessment logic, which compares metric values across hierarchical levels and computes localization scores to accurately attribute issues to specific subsets of objects, enhancing confidence in issue localization and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If measurement data is collected from large computing environments with multiple hierarchical levels, then the coverage of monitoring is improved, but the complexity of analyzing the data and localizing issues increases
Solution Approach 1:
The patent segments the computing environment into multiple hierarchical levels (infrastructure level, virtualization level, workload level) and analyzes measurement data at each level separately. This segmentation allows comprehensive monitoring coverage while managing analysis complexity by breaking down the large-scale data into manageable hierarchical segments, where issues can be localized to specific levels and subsets of objects within each level.
2Difficulty of detecting and measuring
If pattern recognition is performed across all objects to identify issues, then the detection capability is improved, but the accuracy of localizing issues to specific objects deteriorates
Solution Approach 1:
The patent introduces a hierarchical dimension to the analysis by evaluating measurement data across multiple levels (infrastructure, virtualization, workload) rather than treating all objects at a single level. This dimensional approach enables both broad pattern recognition across the entire system and precise localization to specific objects by tracing anomalies through the hierarchical layers, thereby improving both detection capability and localization accuracy simultaneously.
3Loss of information
If comprehensive measurement data is collected from all hierarchical levels, then the information available for diagnosis is improved, but the difficulty of recognizing patterns and localizing issues increases
Solution Approach 1:
The patent segments the comprehensive measurement data into hierarchical groups corresponding to different levels of the computing environment. By organizing data into infrastructure-level objects, virtualization-level objects, and workload-level objects, the system maintains all diagnostic information while making pattern recognition more manageable. The segmentation allows analysts to focus on specific hierarchical layers and use level-specific patterns rather than searching through all data simultaneously.
Data Source
AI summary
In some examples, a system identifies a potential issue based on comparing measurement data acquired at different hierarchical levels of a computing environment. Within a hierarchical level of the different hierarchical levels, the system determines, based on measurement data acquired for objects in the hierarchical level, whether the potential issue is localized to a subset of the objects.