Adaptive Data Collection Nodes for Heterogeneous Network Fault Detection
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Solution Overview
Problem
Existing communication network technologies face challenges in effectively collecting, aggregating, and analyzing data in dynamic environments, leading to delays in fault detection and service level agreement (SLA) maintenance due to static data collection and aggregation methods, which are inadequate for handling dynamic network and user conditions.
Innovation Solution
A method and system for determining impact characteristics in a distributed heterogeneous communication network to dynamically select collection, aggregation, and analysis nodes, enabling adaptive data collection, aggregation, and analysis based on user, service, slice, and network characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static data collection and aggregation methods are used, then system complexity is reduced, but fault detection timeliness and SLA maintenance effectiveness deteriorate under dynamic network conditions
Solution Approach 1:
The patent implements dynamic data collection and aggregation by enabling the system to adaptively adjust monitoring parameters, data collection frequency, and aggregation rules based on real-time network conditions and user behavior patterns. This allows the system to respond promptly to dynamic changes in network traffic, user mobility, and service requirements, thereby reducing fault detection time while maintaining manageable system complexity through automated adaptation rather than static configuration
Solution Approach 2:
The system dynamically changes monitoring parameters such as data collection intervals, aggregation thresholds, and analysis granularity based on network conditions and user characteristics. By adjusting these parameters in real-time, the system optimizes fault detection speed and accuracy without requiring complete system redesign, thus resolving the contradiction between simplicity and responsiveness
2Measurement precision
If monitoring data is collected from all network sources, then measurement completeness is improved, but network overhead and processing delay increase
Solution Approach 1:
The patent applies local quality by collecting and aggregating data with different levels of detail from different network sources based on their specific characteristics and importance. Critical network elements receive enhanced monitoring with higher data collection frequency and granularity, while less critical elements use reduced monitoring. This selective approach ensures measurement completeness for important parameters while reducing overall network overhead and processing burden
Solution Approach 2:
The system performs partial data collection and aggregation by focusing resources on collecting only the most relevant monitoring data needed for fault detection and SLA maintenance, rather than collecting all possible data from all sources. This selective partial action reduces network overhead and processing delays while maintaining sufficient measurement precision for effective network management
3Stability of the object's composition
If data aggregation is performed at a centralized location, then data processing consistency is improved, but transmission delay and network overhead increase
Solution Approach 1:
The patent segments the data aggregation function into multiple distributed aggregation nodes throughout the network rather than using a single centralized location. Each aggregation node processes and aggregates data locally for its designated network segment, maintaining consistency within that segment while reducing transmission distances and delays. This segmented approach balances processing consistency with improved speed by eliminating the single-point bottleneck
Solution Approach 2:
The system transitions from a single-dimensional centralized aggregation model to a multi-dimensional distributed aggregation architecture where data can be aggregated along multiple dimensions (local segment, regional zone, global network) simultaneously. This dimensional change allows consistent processing to occur at multiple levels, reducing the transmission burden on any single aggregation point while maintaining overall data consistency across the network
Data Source
AI summary
This disclosure relates to methods and systems for effective data collection, aggregation, and analysis in a distributed heterogeneous communication network for effective fault detection or performance anomaly detection. In one embodiment, the method may include determining impact characteristics with respect to a network slice or a service in the distributed heterogeneous communication network. The method may further include determining one or more collection nodes, one or more aggregation nodes, and one or more analysis nodes within the distributed heterogeneous communication network based on the impact characteristics. The method may further include activating the one or more collection nodes for collecting data, the one or more aggregation nodes for aggregating the collected data, and the one or more analysis nodes for analyzing the aggregated data. The impact characteristics may include at least one of: user characteristics, service characteristics, slice characteristics, network characteristics, or performance characteristics.