Air-to-Ground Traffic Scheduling via Stream Classification
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Solution Overview
Problem
Air-to-Ground communication systems face challenges in managing bandwidth congestion, particularly due to high-bandwidth applications, which disrupt real-time services and overall usability, and existing controls are ineffective in identifying and managing bandwidth-intensive traffic, leading to poor user experience.
Innovation Solution
The Traffic Scheduling System assigns individual IP addresses to passenger wireless devices, enabling unique identification and management of traffic flows to optimize performance, latency, and bandwidth by classifying and controlling bandwidth-intensive streams using dynamic configuration and traffic shaping techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bandwidth-intensive applications are allowed to operate freely, then high data transfer rates are achieved, but network congestion occurs and real-time services are disrupted
Solution Approach 1:
The patent segments traffic into different classes (real-time and non-real-time) and assigns them to separate queues. Real-time traffic (voice, video conferencing) is prioritized in a dedicated queue, while bandwidth-intensive applications are routed to a non-real-time queue. This segmentation ensures that high data transfer rates for bandwidth-intensive applications do not disrupt real-time services, resolving the contradiction between productivity and reliability.
Solution Approach 2:
The patent implements dynamic traffic classification and queue management that adapts to current network conditions. The system dynamically identifies bandwidth-intensive applications and adjusts their resource allocation based on available bandwidth and service requirements. This dynamic approach allows the system to maintain high data transfer rates when capacity is available while ensuring real-time services receive guaranteed bandwidth, resolving the contradiction between productivity and reliability.
2Ease of operation
If static network controls and rate limits are implemented, then network management is simplified, but they cannot adapt to current Air-To-Ground performance and are always in catch-up mode
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors network performance metrics (bandwidth availability, latency, packet loss) and uses this information to dynamically adjust traffic classification and queue management parameters. The system measures actual Air-To-Ground link performance and adapts its traffic scheduling decisions in real-time, moving from static catch-up control to proactive performance optimization. This feedback loop enables the system to maintain simplicity while achieving adaptability.
Solution Approach 2:
The patent transitions from static network controls to dynamic traffic management that adapts to current network conditions. The system dynamically adjusts rate limits, queue priorities, and traffic classification thresholds based on real-time performance measurements. This dynamic approach maintains ease of operation through automated adaptation while achieving versatility in responding to varying network conditions.
3Quantity of substance
If volume controls are activated to reduce high usage, then bandwidth consumption is reduced, but they are ineffective with large subscriber count flights where systemic congestion prevents activation
Solution Approach 1:
The patent segments traffic into real-time and non-real-time queues, allowing volume controls to be applied selectively. Instead of uniformly reducing all traffic when congestion is detected, the system maintains real-time traffic (voice, video conferencing) at guaranteed bandwidth levels while applying volume control only to non-real-time traffic (bandwidth-intensive applications). This segmentation enables bandwidth consumption reduction without compromising service availability for critical applications, even with large subscriber counts.
Solution Approach 2:
The patent applies different quality of service policies to different traffic types. Real-time traffic receives guaranteed bandwidth and priority handling, while non-real-time traffic receives best-effort service with dynamic rate limiting. This local quality approach ensures that volume controls reduce overall bandwidth consumption without preventing service availability for time-sensitive applications, resolving the contradiction between quantity control and productivity maintenance.
Data Source
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AI summary
The Traffic Scheduling System executes a multi-step process first to identify the bandwidth intensive traffic. The identification of the bandwidth intensive traffic is effected at the stream level by measuring the byte volume of the stream over a predetermined period of time and using this data to classify the stream into one of a plurality of usage categories. The classification of bandwidth intensive traffic is network neutral in that all data is classified at the stream level (source IP, destination IP, source port, destination port). Otherwise, the data is not inspected. Once streams have been classified by the Traffic Scheduling System, the Bandwidth Intensive and Near Real Time traffic can be controlled by a simple Traffic Shaping process executed by the Traffic Scheduling System, using a traffic management parameter such as via the Round-Trip Time of the next higher priority queue, in the set of queues.