Dynamic Wi-Fi Application Prioritization via Intelligent Scheduling
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Solution Overview
Problem
Existing Wi-Fi scheduling technologies face challenges in efficiently managing diverse applications with varying latency and data requirements, leading to complications and overhead costs in prioritization and resource allocation, especially in environments with multiple clients and critical applications.
Innovation Solution
The implementation of intelligent scheduling with enhanced dynamic prioritization techniques, utilizing Air Slice technology to dynamically allocate radio resources based on client and user properties, prioritizing applications through a combination of dedicated queues and 802.11ax MAC scheduling enhancements, allowing for optimized QoS and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional Wi-Fi scheduling is used to support multiple applications, then basic network operations are maintained, but latency-sensitive applications experience poor performance and throughput is not optimized
Solution Approach 1:
The patent segments the wireless medium into multiple virtual queues (e.g., voice queue, video queue, data queue) with different priority levels. Each queue is serviced according to its specific requirements, allowing latency-sensitive traffic to be handled differently from throughput-oriented traffic, thus resolving the contradiction between reliability for real-time applications and productivity for data-intensive applications
Solution Approach 2:
The scheduling mechanism dynamically adjusts queue service orders and transmission parameters based on real-time network conditions and application requirements. The system can switch between different scheduling modes (e.g., strict priority, weighted fair queuing) to optimize performance for current traffic patterns, ensuring both low latency when needed and high throughput when possible
2Reliability
If application prioritization is implemented in IEEE 802.11ax, then latency-sensitive applications improve, but system complexity and scheduling overhead increase
Solution Approach 1:
The system performs preliminary classification of incoming packets into priority queues based on QoS parameters before scheduling decisions are made. This pre-processing organizes traffic in advance, simplifying the actual scheduling task to selecting from pre-categorized queues rather than evaluating all packets individually, thus reducing real-time complexity while maintaining latency guarantees
Solution Approach 2:
The patent changes key scheduling parameters such as queue service weights, transmission power levels, and resource allocation based on application priority. By adjusting these parameters dynamically, the system achieves differentiated service for latency-sensitive applications without requiring completely complex scheduling algorithms, balancing performance improvement with manageable complexity
3Adaptability or versatility
If multiple transmission modes (OFDMA and MU-MIMO) are supported, then flexibility for different applications is improved, but resource allocation complexity increases
Solution Approach 1:
The patent applies different transmission modes and resource allocation strategies locally according to specific application requirements. For example, OFDMA is selectively applied to latency-sensitive small packets while MU-MIMO is used for throughput-intensive streams. This localized optimization provides versatility for different applications while avoiding the complexity of uniformly applying all modes to all traffic
Data Source
AI summary
Systems and techniques are described that are directed to intelligent scheduling of Wi-Fi services for applications, including enhanced dynamic prioritization. A device, such as an access point (AP), can receive data packets from multiple connected devices to dynamically identify an application flow for each data packet, and dynamically identify a user associated with the application flow for each data packet. The AP can generate prioritized candidate lists for selected data packets in queues corresponding to an access category (AC). In response to determining that the identified user associated with the application flow corresponds with a critical user, the AP can select data packets for the prioritized candidate lists based at least in part on priority policies for each of a plurality of applications and based at least in part on dynamic prioritization of applications for each of a plurality of applications; and schedule data packets from the prioritized candidate lists.