Dynamic ECN Threshold Control for Queue Depth and Delay
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Solution Overview
Problem
Current network congestion control methods lack flexibility due to statically configured ECN thresholds, leading to either excessive queue depths or low network resource utilization.
Innovation Solution
Implement a dynamic ECN configuration using an ECN inference model trained by an analysis device based on network status information, allowing for real-time adjustment of ECN parameters such as thresholds and marking probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the ECN threshold is set to a high value, then the queue depth of the egress queue increases, but the transmission delay of data packets increases
Solution Approach 1:
The patent applies dynamics by transitioning from static ECN threshold configuration to dynamic adjustment. The network device continuously monitors queue depth and transmission delay, adjusting the ECN threshold in real-time based on current network conditions. This allows the system to adaptively balance queue depth and transmission delay rather than being constrained by fixed threshold values.
Solution Approach 2:
The patent implements parameter changes by modifying the ECN threshold parameter based on network status. The system changes the threshold value dynamically according to monitored metrics such as queue depth and transmission delay, enabling optimization of the trade-off between maintaining sufficient queue depth for buffer capacity and minimizing transmission delay for performance.
2Speed
If the ECN threshold is set to a low value, then the sending rate of data packets decreases, but network resource utilization decreases
Solution Approach 1:
The system dynamically adjusts the ECN threshold based on real-time network conditions rather than using a fixed low value. This allows the sending rate to be optimized adaptively - increasing it when network resources are available and decreasing it when congestion is detected - thereby maintaining high network resource utilization while controlling congestion.
Solution Approach 2:
The patent implements feedback mechanisms where the network device monitors network status including queue depth and transmission delay, then uses this feedback to adjust the ECN threshold. This closed-loop control enables the system to respond to changing network conditions and optimize both sending rate and resource utilization based on actual performance metrics.
3Ease of operation
If static ECN configuration parameters are used, then configuration simplicity is maintained, but congestion control flexibility decreases
Solution Approach 1:
The patent applies self-service by enabling the network device to automatically monitor its own performance metrics and adjust ECN thresholds without external intervention. The system self-manages the congestion control parameters based on real-time network status, eliminating the need for manual reconfiguration while maintaining flexibility. This resolves the contradiction by providing both ease of operation (automatic adjustment) and adaptability (dynamic response to conditions).
Solution Approach 2:
The system transitions from static configuration to dynamic self-adjustment. The ECN threshold becomes a dynamic parameter that automatically adapts to network conditions through continuous monitoring and adjustment, providing both operational simplicity (no manual configuration needed) and flexibility (adaptive response to changing conditions).
Data Source
AI summary
A network device inputs first network status information of the network device in a first time period to an ECN inference model, to obtain an inference result that is output by the ECN inference model based on the first network status information. Then, the network device sends an ECN parameter sample to an analysis device that manages the network device, where the ECN parameter sample includes the first network status information and a target ECN configuration parameter corresponding to the first network status information, and the target ECN configuration parameter is obtained based on the inference result. The network device receives an updated ECN inference model sent by the analysis device.