Dynamic QoS Adjustment in Mesh Networks via Token Buckets

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Solution Overview

Problem

Existing network technologies fail to dynamically and effectively adjust Quality of Service (QoS) parameters in response to changing bandwidth conditions, particularly in mesh networks, leading to inconsistent service quality for sensitive applications like video streaming and VoIP, due to inadequate handling of bandwidth fluctuations caused by noise, interference, and node changes.

Innovation Solution

A computer-implemented method that dynamically adjusts QoS parameters by monitoring actual bandwidth and changing settings such as token bucket depth and generation rate, and switching to alternate routes, using a hierarchical token bucket system to allocate bandwidth efficiently among subscribers, ensuring seamless service even during bandwidth decreases.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If QoS parameters are set based on expected bandwidth, then service quality can be maintained under normal conditions, but service quality deteriorates when actual bandwidth deviates from expected bandwidth

Engineering Contradiction:

Improveservice qualityVSAvoidbandwidth adaptation

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system dynamically adjusts QoS parameters including token bucket depth, token generation rate, and ceil values based on real-time monitoring of actual bandwidth conditions. This allows the network to adapt to changing bandwidth availability while maintaining service quality for sensitive applications.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system continuously monitors actual bandwidth and uses this feedback to automatically adjust QoS parameters. The feedback loop enables the system to detect bandwidth deviations and respond by modifying token bucket parameters to compensate for the difference between expected and actual bandwidth.

Inventive Principle:

Principle #23Feedback

2Productivity

If bandwidth is increased to support more applications, then network capacity improves, but network stability deteriorates due to noise, interference, and node changes

Engineering Contradiction:

Improvebandwidth capacityVSAvoidnetwork stability

Core Design Contradiction:

ProductivityVSStability of the object's composition

Solution Approach 1:

The system changes QoS parameters such as token bucket depth, token generation rate, and ceil values to optimize network performance under varying bandwidth conditions. These parameter adjustments allow the network to maintain stability while supporting different bandwidth capacities.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The token bucket mechanism provides a buffer that cushions against bandwidth fluctuations. By pre-configuring token buckets with appropriate depth and generation rates, the system can absorb sudden bandwidth changes and prevent them from directly impacting service quality.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

3Reliability

If QoS parameters are manually adjusted to compensate for bandwidth changes, then service quality can be maintained, but operational complexity increases

Engineering Contradiction:

Improveservice qualityVSAvoidparameter adjustment

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system automatically monitors bandwidth conditions and adjusts QoS parameters without requiring manual intervention. The self-service mechanism continuously adapts token bucket parameters based on actual bandwidth, eliminating the need for administrators to manually tune QoS settings.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The automated feedback loop detects bandwidth deviations and triggers appropriate QoS parameter adjustments. This feedback-driven automation maintains service quality while reducing operational complexity by eliminating manual parameter adjustment.

Inventive Principle:

Principle #23Feedback

4Adaptability or versatility

If bandwidth is allocated equally among all subscribers, then fairness is improved, but service quality for sensitive applications deteriorates

Engineering Contradiction:

Improvebandwidth allocationVSAvoidapplication performance

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system applies different QoS treatment to different types of traffic. Sensitive applications such as video streaming and VoIP receive prioritized service through dedicated token buckets with appropriate parameters, while other traffic receives standard service. This local quality differentiation ensures fair allocation while maintaining high performance for sensitive applications.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The bandwidth allocation is segmented into different categories with different QoS parameters. The hierarchical token bucket structure divides bandwidth into parent and child buckets, allowing differentiated allocation where sensitive applications receive guaranteed bandwidth while other applications share remaining capacity.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS10230645B1Dynamic adjustment of quality of service parameters

Publication Date: 2019.03.12 VIVINT INC

AI summary

A computer-implemented method for dynamic adjustment of quality of service parameters is described. In one embodiment, one or more quality of service (QoS) parameters of a client of a mesh network is set based on an expected bandwidth for the mesh network. An actual bandwidth for the mesh network is measured. One or more QoS parameters of the client is automatically changed in response to the actual bandwidth differing from the expected bandwidth. The change in the QoS parameters may be configured to compensate for the difference between the actual bandwidth and the expected bandwidth.