Centralized Network Analytics Model for Root Cause Identification
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Solution Overview
Problem
Existing network performance analysis systems are inefficient due to a lack of recognition of root causes, relationships between metrics, and inability to predict issues based on network performance data.
Innovation Solution
A centralized networks analytics model that executes on a cloud-based server or edge device, utilizing analytic accelerators to collect, analyze, and predict network performance issues, identify root causes, and implement remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional network performance analysis methods are used, then system simplicity is maintained, but analysis efficiency and problem identification accuracy deteriorate
Solution Approach 1:
The patent introduces a centralized network analytics model as an intermediary component that sits between network data sources and analysis processes. This model pre-processes and structures network performance data, relationships, and root cause information centrally, making subsequent analysis more efficient without requiring complex distributed processing across all system components.
Solution Approach 2:
The system performs preliminary actions by pre-building a network analytics model that contains pre-computed relationships between network metrics, potential root causes, and their interconnections. This model is constructed in advance and stored for rapid querying during actual network analysis, eliminating the need for complex real-time computations during problem diagnosis.
2Measurement precision
If comprehensive network performance data collection is implemented, then measurement precision improves, but information processing complexity increases
Solution Approach 1:
The centralized network analytics model acts as an intermediary that receives comprehensive network performance data from multiple sources and pre-structures it into standardized formats with established relationships. This model serves as a buffer between raw data collection and analysis processes, managing the complexity of comprehensive data processing centrally while providing simplified interfaces to consuming applications.
3Measurement precision
If root cause analysis capabilities are enhanced, then problem identification accuracy improves, but analysis time increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing the relationships between network metrics, potential root causes, and their correlations in the centralized analytics model. During actual analysis, the system queries this pre-built model rather than performing complex analytical computations in real-time, dramatically reducing analysis time while maintaining high root cause identification accuracy.
Solution Approach 2:
The system implements feedback mechanisms where the network analytics model is continuously refined based on actual network problems and their resolutions. This feedback loop improves the accuracy of root cause relationships stored in the model over time, making subsequent analyses both more accurate and more efficient as the model learns from accumulated experience.
Data Source
AI summary
This disclosure describes systems, methods, and devices related to identification and assessment of security threats to a computer system using zero trust security. A method may include receiving, at a policy enforcement device of a computer network, first data from a first subsystem of the computer network; receiving, at the policy enforcement device, second data from a second subsystem of the computer network, the first subsystem different than the first subsystem; identifying, by the policy enforcement device, based on a comparison of at least one of the first data or the second data to a security policy, a security threat to the computer network; and causing, by the policy enforcement device, a threat intelligence device of the computer network to determine a risk of the security threat