Dynamic QoS Scheduling for Mixed Delay-Sensitive and Best Effort Flows
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing Quality of Service (QoS) for a mix of best effort and delay sensitive flows due to limited bandwidth and high error rates, leading to obsolete packet scheduling techniques that fail to meet delay requirements effectively.
Innovation Solution
The system dynamically adjusts scheduling priorities and allocates bandwidth for each data packet to meet delay requirements of delay sensitive flows while assigning remaining bandwidth to best effort flows, using a metric-based approach to optimize resource allocation and ensure fairness between flow types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strict ordering is used for QoS management, then delay requirements for delay sensitive flows are met, but bandwidth utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic priority adjustment where the scheduler continuously monitors delay metrics and bandwidth utilization, adjusting the priority of delay sensitive flows in real-time based on current network conditions. This allows the system to meet delay requirements when necessary while optimizing bandwidth utilization when delay pressure is low, resolving the contradiction between reliability and productivity.
Solution Approach 2:
The system changes the scheduling parameter (priority level) of flows dynamically based on measured delay performance and available bandwidth. By adjusting priority levels rather than maintaining fixed strict ordering, the system can adapt to varying network conditions and achieve both delay satisfaction and efficient bandwidth utilization under different operating scenarios.
2Reliability
If dynamic bandwidth reallocation is implemented, then QoS optimization is improved, but scheduling complexity increases
Solution Approach 1:
The patent employs feedback mechanisms where the scheduler monitors delay metrics and bandwidth utilization, then uses this feedback to adjust priority assignments and bandwidth allocation. This closed-loop control enables effective QoS management through dynamic adaptation while keeping the scheduling logic relatively simple by relying on measured performance rather than complex predictive models.
Data Source
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AI summary
Systems and methodologies are described that facilitate dynamically adjusting scheduling priorities in relation to a combination of delay sensitive flows with delay requirements and best effort flows. The systems and methodologies provide optimal and efficient techniques to enable real time adjustment and assignment of bandwidth for a combination of best effort flows and delay sensitive flows. In particular, the bandwidth allocation is adjusted for each data packet such that delay requirements are met and the remaining bandwidth can be assigned to best effort flows.