Network Traffic Volume Analysis for QoS Saturation Prevention
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Solution Overview
Problem
There is a need for improved methods to analyze and manage forwarded and discarded data volumes in communication networks to prevent saturation and maintain quality of service, particularly in passive optical networks, by identifying critical periods and recommending actions to mitigate potential QoS degradation.
Innovation Solution
An apparatus and method that processes forwarded and discarded traffic volume data using signal processing techniques like wavelet filtering to identify critical periods and determine impact scores, enabling network reconfiguration, capacity adjustments, and service restrictions based on calibrated data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signal processing techniques like wavelet filtering are applied to analyze traffic volume data, then the precision of identifying critical periods is improved, but the computational complexity and processing time increase
Solution Approach 1:
The observation period is divided into discrete time periods, and the traffic volume data is segmented for analysis. Wavelet filtering is applied to segmented data rather than the entire dataset at once, reducing computational complexity while maintaining precision in identifying critical periods.
Solution Approach 2:
The system applies calibration data at multiple calibration levels to enhance the precision of critical period identification. By using partial calibration (multiple levels rather than full calibration), the system achieves improved measurement precision without requiring complete calibration of all parameters.
2Measurement precision
If multiple calibration levels are applied to traffic volume data, then the accuracy of impact score determination is improved, but the processing time and computational resources increase
Solution Approach 1:
The system processes traffic volume data through multiple calibration levels in periodic stages. Each calibration level processes data for a specific period, allowing the system to achieve high accuracy in impact score determination while managing processing time through structured periodic computation.
Solution Approach 2:
Calibration data is prepared and applied at multiple levels before the final impact score determination. This preliminary action ensures that when the actual analysis is performed, the precision is already optimized, reducing the need for repeated processing and minimizing total processing time.
3Reliability
If critical periods are identified and network reconfiguration is implemented, then the quality of service is maintained, but the network complexity and management overhead increase
Solution Approach 1:
The system continuously monitors traffic volume data and provides feedback on critical periods and impact scores. This feedback mechanism enables automatic or semi-automatic network reconfiguration decisions, maintaining quality of service while reducing management complexity through data-driven automation rather than manual intervention.
Solution Approach 2:
The network system performs self-diagnosis by identifying critical periods and calculating impact scores automatically. This self-service capability allows the network to detect and respond to QoS degradation issues without external intervention, maintaining reliability while minimizing management overhead.
Data Source
AI summary
An apparatus, method and computer program is described as obtaining forwarded traffic volume data and discarded traffic volume data of a point-to-multipoint communication network in each of a plurality of discrete time periods over an observation period; generating calibrated forwarded and discarded traffic volume data, wherein calibration data is dependent on a calibration level; filtering, by applying signal processing techniques, multiple instances of said calibrated forwarded and discarded traffic volume data; identifying first sample periods within said observation period; and determining impact scores for the forwarded and discarded traffic volume data for each of said one or more first sample periods.