SON QoS Factor Optimization via Dynamic Weighting
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Solution Overview
Problem
Telecommunication networks face congestion, delay, and resource allocation inefficiencies due to static quality-of-service (QoS) weighting schemes, which fail to dynamically adjust resource allocation based on changing network demands and conditions.
Innovation Solution
The implementation of self-organizing network (SON) components that determine and dynamically update QoS factors for different classes of traffic based on performance metrics, network configuration, and fault information, allowing for real-time optimization of resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static QoS weighting schemes are used, then network resource allocation is simplified and easier to manage, but resource utilization efficiency deteriorates during varying demand conditions
Solution Approach 1:
The patent implements dynamic QoS weighting that automatically adjusts weights based on real-time network conditions and traffic patterns. The system transitions from static pre-configured weights to dynamic weights that adapt to changing demands, thereby improving resource utilization efficiency while maintaining manageable complexity through automated adjustment mechanisms.
Solution Approach 2:
The system changes the QoS parameter weights dynamically based on network conditions. Different QoS classes receive different weight assignments depending on current traffic patterns, network load, and service requirements. This parameter adaptation allows optimal resource allocation across varying operational conditions without requiring manual reconfiguration.
2Device complexity
If static QoS weights are assigned to all traffic, then configuration and management is simplified, but network performance deteriorates under varying traffic conditions
Solution Approach 1:
The QoS system performs self-configuration by automatically determining appropriate weights for different QoS classes based on monitored network conditions and traffic characteristics. This self-service capability eliminates the need for manual weight adjustment while maintaining high service reliability through adaptive response to changing network states.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor network performance and traffic patterns, then use this information to adjust QoS weights dynamically. The feedback loop ensures that the system maintains reliable service delivery by adapting to actual network conditions rather than relying on static pre-configured weights.
3Reliability
If lower priority QoS classes receive worse resource allocation, then higher priority traffic is better served, but overall network throughput deteriorates during low demand periods
Solution Approach 1:
The system dynamically adjusts the relative weights of different QoS classes based on current network conditions. During low demand periods, the system can reduce the weight differential between priority classes, allowing lower priority traffic to receive more resources and thereby improving overall throughput while still maintaining adequate service for high priority traffic.
Data Source
AI summary
Described herein are one or more self-organizing network (SON) components configured to determine QoS factors associated with QoS classes for a node of a telecommunication network based at least in part on performance metrics, network configuration, or network fault information. The one or more SON components may receive the performance metrics, network configuration, or network fault information from the node, and the performance metrics, network configuration, or network fault information may be associated with communication of traffic of the QoS classes by the node. Upon determining the QoS factors, the one or more SON components may transmit indications of the QoS factors to the node.