Autonomous Scaling of Network Data Resolution for Performance Management
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Solution Overview
Problem
Legacy systems provide semi-static configurations for performance management data collection in telecommunications networks, leading to inefficiencies in resource utilization and sub-optimal network performance due to inflexible data collection granularity and aggregation, which fail to adapt to dynamic network requirements.
Innovation Solution
A monitoring system autonomously scales data resolution and aggregation levels to optimize network performance by dynamically adjusting data collection based on triggers and root cause analysis, enabling finer data collection where needed, and performing actions such as modifying RAN parameters or providing emergency services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If semi-static configuration with fixed granularity and aggregation is used for data collection, then system complexity is reduced and ease of operation is improved, but network performance optimization is limited and resource utilization is inefficient
Solution Approach 1:
The system dynamically adjusts data collection granularity and aggregation levels based on real-time network conditions and events. The monitoring system transitions from semi-static configuration to dynamic adaptation by automatically scaling resolution and aggregation factors in response to detected network events, ensuring optimal performance without manual reconfiguration.
Solution Approach 2:
The invention changes key parameters including resolution factor, aggregation factor, and data collection frequency based on network conditions. By modifying these parameters dynamically, the system optimizes data collection efficiency and network performance, moving away from fixed semi-static configurations to adaptive parameter adjustment.
2Measurement precision
If high resolution and frequent data collection is applied across the entire network, then measurement precision and problem detection capability are improved, but resource consumption and system complexity increase
Solution Approach 1:
The system applies different data collection resolutions and aggregation levels to different network portions based on local conditions. High-resolution monitoring is applied only to affected network portions where events are detected, while other portions maintain lower resolution, optimizing resource usage while preserving measurement precision where needed.
Solution Approach 2:
The monitoring system segments the network into different portions and applies differentiated data collection strategies to each segment. Instead of uniform high-resolution monitoring across the entire network, the system divides monitoring intensity based on event location and network conditions, reducing overall resource consumption while maintaining precision in critical areas.
3Reliability
If data collection frequency and resolution are increased, then reliability and problem detection capability are improved, but loss of time for data processing and system response increases
Solution Approach 1:
The system implements periodic data collection with variable frequencies based on network events. Instead of continuous high-frequency monitoring, the system uses event-triggered periodic collection, adjusting the period between measurements based on detected conditions, thereby maintaining reliability while reducing unnecessary processing time.
4Ease of manufacture
If semi-static aggregation configuration is used, then device complexity is reduced and ease of manufacture is improved, but adaptability to dynamic network requirements deteriorates
Solution Approach 1:
The monitoring system performs self-configuration by automatically adjusting aggregation and resolution parameters based on detected network events. Instead of requiring manual reconfiguration for different network conditions, the system autonomously adapts its data collection strategy, improving versatility while maintaining ease of initial deployment.
Data Source
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AI summary
A device may determine a first resolution and aggregation for data collection from a network and may receive first PM data associated with the network, at the first resolution and aggregation. The device may calculate, based on the first PM data, a first parameter characteristic of a UE and may determine a trigger based on the first parameter characteristic and based on one or more of a root cause analysis, an application input, or a KPI. The device may identify a portion of the network that is associated with the trigger based on the first PM data and may determine a second resolution and aggregation for data collection from the portion of the network. The device may receive second PM data associated with the portion of the network, at the second resolution and aggregation and may calculate a second parameter characteristic of the UE based on the second PM data.