Smart Priority Alert Ranking in Cloud Systems
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Solution Overview
Problem
In cloud computing environments, users face challenges in effectively prioritizing and managing enterprise alerts due to the vast amount of data related to Configuration Items (CIs) and their metadata, leading to difficulties in understanding alert severity, business criticality, and affected system components, which hinders efficient troubleshooting and corrective actions.
Innovation Solution
A system that calculates a 'smart priority' score for alerts using category mapping and weighting tables, combined with machine learning techniques, to rank alerts based on severity, business criticality, and affected components, and provides an enhanced user interface for displaying alerts in a prioritized manner, including historical insights and potential solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually review and prioritize enterprise alerts in cloud computing environments, then they can understand alert severity and business criticality, but the time required for triaging and troubleshooting increases significantly due to the vast amount of data
Solution Approach 1:
The system automatically calculates smart priority scores for alerts using machine learning models and predefined weighting schemes, enabling the system to self-assess and rank alerts without requiring manual user intervention for prioritization, thus reducing triaging time while maintaining accurate alert ranking
Solution Approach 2:
The patent replaces manual mechanical review processes with an automated computational system that uses machine learning algorithms and weighted scoring mechanisms to automatically prioritize alerts, substituting human cognitive effort with automated intelligent processing
2Measurement precision
If the system stores comprehensive metadata for all Configuration Items (CIs) to enable detailed alert analysis, then the precision of alert prioritization improves, but the complexity of data management and processing increases
Solution Approach 1:
The system extracts only the most relevant metadata attributes from the comprehensive CI data store based on predefined weighting schemes and machine learning models, focusing processing on critical attributes such as service level agreements, component criticality, and historical alert patterns, thereby reducing data management complexity while maintaining prioritization precision
Solution Approach 2:
The patent applies different weighting and processing strategies to different metadata attributes based on their relevance to alert prioritization, treating critical attributes with higher computational resources and attention while using simpler processing for less important attributes, optimizing the balance between precision and complexity
3Loss of information
If the system presents all alert details and historical information to users, then the completeness of information for troubleshooting improves, but the ease of operation and user interface clarity deteriorates due to information overload
Solution Approach 1:
The system segments alert information into hierarchical levels of detail, presenting summarized key information prominently in the main interface while organizing comprehensive historical data and detailed metadata into accessible but less prominent sections, allowing users to access complete information when needed while maintaining interface clarity through structured segmentation
Solution Approach 2:
The patent organizes comprehensive alert information across multiple dimensions including temporal (historical vs. current), hierarchical (summary vs. detailed), and categorical (different types of metadata), allowing users to navigate the complete information set through multi-dimensional filtering and drilling down rather than presenting all data in a single overwhelming view
Data Source
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AI summary
Various embodiments are disclosed herein that provide users of a cloud computing system with the ability to display, prioritize, and/or handle enterprise alerts, e.g., in the form of a sorted list. In some embodiments, these alerts may be ranked according to a 'smart priority' calculation. The 'smart priority' calculation may take into account a number of factors related to given alert, e.g.: severity level, business criticality level, role, number of affected system components, types of affected system components, etc. These factors may be combined in the 'smart priority' calculation in a hierarchical fashion, e.g., based on a predetermined (or user-customized ranking) of the importance and/or weighting of the various factors. By seeing the historical and status metadata information relating to the alerts, users may more quickly understand which alerts to address first-and what possible solutions may be employed in order to close out the open alerts in the system.