Local Network Traffic Prioritization Using Device-Aware ML
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Solution Overview
Problem
Existing local networks face challenges in efficiently prioritizing device traffic due to user inability to manage network linking devices, leading to inefficient bandwidth utilization and potential device processing errors, especially with the addition of new devices requiring re-prioritization.
Innovation Solution
A machine-learning based device prioritization model that collects traffic data from multiple local networks to determine device types and priority values, training a model to automatically prioritize network traffic based on device types and usage patterns, reducing the need for manual rule-based approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual traffic prioritization is implemented by users, then device traffic priority can be controlled, but user complexity and difficulty of operation increase significantly
Solution Approach 1:
The system performs self-service by automatically discovering devices on the network, collecting traffic data, determining device types, and generating prioritization schemes without user intervention. The router autonomously monitors network traffic patterns and adjusts QoS parameters based on learned device behaviors and usage patterns.
Solution Approach 2:
The patent replaces manual mechanical configuration operations with automated electronic/machine learning systems. Instead of users manually configuring QoS rules through complex interfaces, a machine learning model automatically analyzes traffic data and generates prioritization schemes, substituting human操作 with automated intelligent systems.
2Reliability
If multiple prioritization schemes are implemented through trial and error, then optimized QoS can be achieved for particular devices, but time consumption and network inefficiency increase
Solution Approach 1:
The system performs preliminary actions by proactively collecting and analyzing traffic data from all devices, pre-determining device types and usage patterns before QoS optimization is needed. The machine learning model continuously learns network behavior patterns in advance, so when prioritization is required, optimized schemes are already prepared based on pre-analyzed data.
Solution Approach 2:
The system implements continuous feedback loops where traffic data is constantly monitored, analyzed, and used to refine prioritization schemes. QoS parameters are adjusted based on feedback from actual network performance and traffic patterns, enabling dynamic optimization without manual trial and error iterations.
3Adaptability or versatility
If network devices are added to the local network, then network functionality is enhanced, but re-prioritization is required leading to bandwidth utilization inefficiency
Solution Approach 1:
The patent implements dynamic prioritization where QoS parameters are not static but continuously adapt to changing network conditions and device additions. When new devices join the network, the system dynamically discovers them, analyzes their traffic patterns, and automatically integrates them into the prioritization scheme, maintaining optimal bandwidth utilization without manual reconfiguration.
Solution Approach 2:
The machine learning model serves multiple functions: device discovery, device type determination, traffic pattern analysis, prioritization scheme generation, and continuous optimization. This multi-functional system handles device additions, removals, and priority changes universally through a single automated framework, eliminating the need for separate re-prioritization processes.
4Productivity
If machine learning model is used for auto-prioritization, then bandwidth efficiency is improved, but device complexity and training requirements increase
Solution Approach 1:
The patent introduces an intermediary layer between raw network traffic and QoS control decisions. The machine learning model acts as a mediator that automatically processes traffic data, determines device types, and translates complex traffic patterns into actionable prioritization rules, shielding users from the complexity of model training while enabling sophisticated bandwidth management.
Data Source
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AI summary
A device prioritization model (130) receives information about a set of traffic data for devices (204) in a local network as input and outputs at least one prioritization scheme (210). Each prioritization scheme (210) includes optimized device bandwidth allocation parameters, comprising at least one or more traffic priority values PN for each network connected device DN. The prioritization scheme is applied to a local network with the following devices: a coffeemaker, a credit card reader, and a security system. The prioritization scheme could include multiple traffic priority values for each of the devices. For a security system having two types of alerts, one "normal operation" and one "emergency situation," the traffic from the "normal operation" may be assigned a lower traffic priority value by the prioritization scheme, while the "emergency situation" may be assigned a higher traffic priority value by the prioritization scheme. The lower and higher traffic priority values assigned to the security system are relative to the traffic priority values of the other devices (e.g., the coffee maker and the credit card reader) which each have at least one traffic priority value assigned by the prioritization scheme. Training of the device prioritization model (130) may incorporate one or more features (206) for producing a prioritization scheme function, PS (208).