Dynamic Queue Latency Control via Token Bucket Shaping
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Solution Overview
Problem
Network devices face inefficiencies due to dynamic queuing latency, which can lead to high latency and overconsumption of bandwidth, especially during congestion or oversubscription, as static queue lengths fail to adapt to changing packet volumes and transmit rates.
Innovation Solution
A queuing system that dynamically adjusts queue length by using a transmit rate computation engine to determine a target queue latency and adjust parameters such as packet drop probabilities, ensuring actual queue latency approximates the target, thereby optimizing latency and minimizing bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static queue length is used, then the device complexity is reduced, but the queue latency becomes uncontrolled and increases during network congestion
Solution Approach 1:
The patent applies dynamics by making the queue length adjustable rather than fixed. The system dynamically modifies the queue length parameter based on current network conditions and target latency requirements, allowing the queue to adapt its size in response to changing traffic patterns and congestion levels, thereby resolving the contradiction between simplicity and latency control.
Solution Approach 2:
The patent changes the parameter of queue length from a static value to a dynamic variable. By implementing mechanisms that adjust the queue length parameter in response to network conditions, the system achieves better latency control without requiring overly complex architectural changes, thus resolving the contradiction between device complexity and time loss.
2Productivity
If the queue length is increased to handle peak traffic, then the bandwidth capacity is improved, but the queue latency increases and network flooding occurs
Solution Approach 1:
The patent implements feedback mechanisms that monitor network conditions and queue status in real-time. Based on this feedback, the system adjusts the queue length dynamically to match actual traffic demands, preventing both underutilization and excessive latency. This feedback-driven approach allows the system to maintain optimal bandwidth capacity while controlling queue latency, resolving the contradiction between productivity and time loss.
Solution Approach 2:
The system dynamically adjusts queue length based on real-time network conditions rather than using a fixed large queue size. This dynamic adaptation allows the queue to expand when needed to handle peak traffic and contract when traffic decreases, maintaining high bandwidth capacity while preventing excessive latency and network flooding.
3Loss of time
If the queue length is decreased to reduce latency, then the queue latency is improved, but the bandwidth capacity is reduced and packets are dropped
Solution Approach 1:
The patent applies dynamics by making the queue length adjustable rather than fixed. The system dynamically modifies the queue length parameter based on current network conditions and target latency requirements, allowing the queue to adapt its size in response to changing traffic patterns and congestion levels, thereby resolving the contradiction between simplicity and latency control.
Solution Approach 2:
The patent changes the parameter of queue length from a static value to a dynamic variable. By implementing mechanisms that adjust the queue length parameter in response to network conditions, the system achieves better latency control without requiring overly complex architectural changes, thus resolving the contradiction between device complexity and time loss.
4Object-affected harmful factors
If a token bucket shaper is used to regulate packet flow, then the network flooding is prevented, but the packet transmit rate is reduced and queue latency increases
Solution Approach 1:
The patent implements feedback mechanisms that monitor network conditions and queue status in real-time. Based on this feedback, the system adjusts the queue length dynamically to match actual traffic demands, preventing both underutilization and excessive latency. This feedback-driven approach allows the system to maintain optimal bandwidth capacity while controlling queue latency, resolving the contradiction between productivity and time loss.
Data Source
AI summary
Techniques are disclosed for a queuing system for network devices. In one example, a network device determines a transmit rate of packets from the queue. In one example the network device determines the transmit rate by determining a number of tokens used over a unit of time by a token bucket shaper for the queue to dequeue the packets from the queue, wherein each of the tokens represents a given number of bytes to be dequeued from the queue. The network device determines a temporal queue length of the queue based on a target queue latency and the determined transmit rate. Further, the network device adjusts at least one parameter of the queue based on the determined temporal queue length such that an actual queue latency of the queue at the determined transmit rate is equal to the target queue latency.