Epoch Comparison for Network Event Detection
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Solution Overview
Problem
Network changes, such as configuration updates, new entities, and policy changes, often lead to unintended consequences and errors, making it difficult for administrators to identify and manage the effects of these changes across network epochs.
Innovation Solution
A system that generates 'smart events' for discrete epochs, allowing for comparisons between network states to identify new, resolved, or persistent events, and performs static network policy analysis by constructing logical models and checking them against predefined rules to detect configuration errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If network changes (configuration updates, new entities, policy changes) are implemented to improve network functionality and adaptability, then network versatility and functionality are enhanced, but it becomes difficult and tedious to identify unintended consequences and errors, worsening network management complexity and reliability
Solution Approach 1:
The system performs preliminary actions by generating epoch comparisons that proactively identify potential issues before they manifest as operational failures. By comparing network states across epochs and identifying new, resolved, and persistent events, the system prepares administrators with advance knowledge of change impacts, allowing preventive rather than reactive management.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring network events across epochs and providing structured feedback about change impacts. The epoch comparison system feeds back information about new events, resolved events, and persistent events, creating a closed-loop system that improves network management reliability through informed decision-making.
2Adaptability or versatility
If multiple sets of changes are implemented to enhance network adaptability, then network versatility improves, but identifying what has changed and how becomes difficult and tedious, worsening the ease of operation
Solution Approach 1:
The system segments the complex task of change identification into distinct categories: new events, resolved events, and persistent events. This segmentation is achieved through epoch comparisons that systematically classify events based on their temporal patterns, making it easier for administrators to understand and manage network changes without being overwhelmed by complexity.
Solution Approach 2:
The system uses visual differentiation (analogous to color changes) to distinguish between different types of events. By presenting epoch comparison results with clear visual distinctions between new, resolved, and persistent events, the system makes change identification intuitive and efficient, directly improving ease of operation.
3Measurement precision
If individual changes are tracked to improve measurement precision of network effects, then event detection accuracy improves, but determining which effects are new versus continuations becomes difficult and tedious, worsening the time required for analysis
Solution Approach 1:
The system performs preliminary classification of events by comparing epochs and automatically categorizing them as new, resolved, or persistent before administrator analysis. This preliminary action eliminates the need for administrators to manually determine event status, significantly reducing analysis time while maintaining high measurement precision.
Solution Approach 2:
The epoch comparison system performs self-service by automatically identifying and categorizing event changes without requiring manual intervention. The system autonomously determines which events are new, which are resolved, and which are persistent, freeing administrators from time-consuming manual analysis while preserving detection precision.
Data Source
AI summary
Systems, methods, and computer-readable media for identifying and categorizing epoch events between a first epoch and a second epoch. Epoch event data for a first epoch and a second epoch is retrieved. The retrieved epoch event data is categorized to determine event category, specific event, and respective object identification. The categorized first and second epoch event data is then labeled to identify new, resolved, and persistent epoch events over multiple epochs.