Network Health Mapping for Scalable Issue Detection
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Solution Overview
Problem
Health management systems in large networks face scalability issues due to the overwhelming quantity of metrics to be monitored, leading to delayed issue detection and increased resource consumption, which can result in communication failures.
Innovation Solution
Implementing a health-based network management system that utilizes multiple databases with different schemas to efficiently store and manage metrics and network topology information, employing a timeseries schema for metrics and relational, graph-based, or document-based schemas for topology, and dynamically adjusting sampling rates based on network issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a health management system monitors all metrics in large networks, then comprehensive health monitoring is achieved, but the system experiences delayed issue detection and increased resource consumption
Solution Approach 1:
The patent segments the monolithic health management system into multiple specialized databases with different schemas: a timeseries database for metrics, a relational database for topology information, and a graph database for network relationships. This segmentation allows each database to optimize its schema for specific data types, improving overall system efficiency and issue detection speed while maintaining comprehensive monitoring capabilities
Solution Approach 2:
The patent changes the parameter of data storage by implementing dynamic schema selection based on data type. Instead of using a single fixed schema, the system dynamically chooses appropriate schemas (timeseries, relational, graph) for different data categories, enabling efficient querying and analysis that accelerates issue detection without compromising monitoring completeness
2Productivity
If multiple databases with different schemas are implemented, then efficient storage and management of metrics and topology information is achieved, but system complexity increases
Solution Approach 1:
The patent divides the data management system into specialized databases, each handling specific data types with optimized schemas. This segmentation improves data management efficiency for metrics and topology information while containing complexity within each individual database component rather than overwhelming a single monolithic system
Solution Approach 2:
The patent introduces an intermediary layer that manages the multiple databases with different schemas. This intermediary handles the complexity of coordinating between timeseries, relational, and graph databases, allowing efficient data management while shielding users from the underlying system complexity
3Measurement precision
If metrics are collected at a high sampling rate continuously, then detailed network analysis is available, but resource consumption increases significantly
Solution Approach 1:
The patent implements dynamic sampling rate adjustment based on network conditions and detected issues. The system transitions from lower sampling rates during normal operation to higher sampling rates when issues are detected, providing detailed network analysis only when necessary and significantly reducing overall resource consumption
Solution Approach 2:
The patent changes the sampling rate parameter dynamically based on network health status and issue severity. This parameter adjustment allows the system to maintain high measurement precision for detailed network analysis when needed while reducing resource consumption during normal operating conditions
Data Source
AI summary
In some examples, a system stores, in a first database having a first schema, metrics received from a network comprising communication nodes, the metrics relating to operations of the communication nodes, and the first database associating the metrics with metadata corresponding to hierarchical components in a topology of the network. In response to an alert relating to an issue in the network, the system computes a health measure based on the metrics, the health measure indicating a health status of a first component, performs a dynamic runtime mapping of the metadata associated with the metrics in the first database having the first schema with corresponding metadata in a second database having a second schema different from the first schema, where the second database contains information of a topology of the network, and initiates a management action to address the health status based on the health measure and the dynamic runtime mapping.