Network-Aware Causation Modeling for Root Cause Analysis
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Solution Overview
Problem
Current network monitoring systems face challenges in accurately performing root cause analysis of network failures due to imprecise timestamp-based methods and lack of network awareness, which can lead to inefficient and inaccurate identification of root causes.
Innovation Solution
The implementation of a network management system that utilizes network-aware causation techniques, including filtering events by temporal proximity, translating events into vector embeddings enriched with network metadata, comparing vector similarities, and processing events through a causation model trained on network hierarchies to accurately perform root cause analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional timestamp-based root cause analysis methods are used, then the system is simpler to implement, but the accuracy of root cause identification deteriorates due to imprecise timestamps and lack of network awareness
Solution Approach 1:
The patent introduces a causation model as an intermediary component that processes network events and determines causal relationships. This mediator layer sits between raw network data and the root cause analysis output, using trained models to accurately identify root causes while managing system complexity through automated processing
Solution Approach 2:
The patent replaces traditional mechanical timestamp-based sorting methods with a machine learning-based causation model. Instead of relying on simple temporal ordering, the system uses neural networks to analyze complex patterns and interdependencies among network events, achieving superior accuracy in root cause identification
2Measurement precision
If network-aware causation techniques with vector embeddings are implemented, then root cause analysis accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-training causation models on historical network data and pre-computing vector embeddings for network entities. This preparation allows the system to make accurate root cause analyses without requiring massive computational resources during actual failure events, as the heavy lifting is done beforehand
3Speed
If real-time root cause analysis is performed across millions of network entities, then network responsiveness improves, but data processing complexity and difficulty of detection increase
Solution Approach 1:
The patent segments the network into hierarchical levels and groups related network entities together. The causation model processes events at different granularities, analyzing local patterns first before considering broader network context. This segmentation allows real-time analysis of millions of entities by breaking down the complex processing into manageable segments
Data Source
AI summary
Systems, apparatuses, and methods for root cause analysis of a computing network are disclosed. A network management system builds a causation model based on causal mappings corresponding to network events. The causal mapping identifies a logical order of occurrence between a given network event and other network events. Network event obtained from the computing network is analyzed using the causation model for performing a root cause analysis for the computing network by generating a network event graph defining a one-to-one relationship between a given network event and one or more other network events. The causation model is built using determined hierarchical relationship of network events with a plurality of network entities as connected within the computing network.