Adaptive Application Scheduler for Dynamic QoS Enforcement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional wireless network resource management mechanisms are static and lack the ability to perform intelligent policy-based Quality of Experience (QoE) enforcement, failing to provide dynamic and adaptive quality of service (QoS) and QoE management, especially in congested conditions.
Innovation Solution
A dynamic and adaptive application scheduler that monitors user plane traffic, configures service parameters based on QoS and QoE parameters, and correlates uplink and downlink traffic to enforce scheduling, providing additional buffering for bottlenecks and implementing congestion control through additive or multiplicative rate adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static QoS parameters are used for traffic flow classification, then network resource management is simplified, but the system cannot adapt to dynamic network conditions and congestion
Solution Approach 1:
The patent implements dynamic QoS parameter adjustment by continuously monitoring network conditions, buffer states, and traffic patterns. The scheduler adapts service parameters in real-time based on current network status, transitioning from static to dynamic resource allocation to resolve the contradiction between adaptability and complexity.
Solution Approach 2:
The system employs feedback mechanisms where the scheduler monitors network congestion, buffer occupancy, and QoS metric performance, then uses this information to dynamically adjust service parameters. This closed-loop control enables adaptive QoS enforcement while managing complexity through systematic feedback processing.
2Reliability
If additional buffering is provided for bottleneck detection, then congestion control capability is improved, but network resource overhead increases
Solution Approach 1:
The patent implements preliminary buffering strategies where buffers are pre-configured with specific parameters based on predicted traffic patterns and QoS requirements. This allows the system to prepare for potential congestion events in advance, improving congestion control reliability while optimizing buffer resource allocation through predictive rather than reactive buffering.
3Measurement precision
If per-application session QoS enforcement is implemented, then QoE management precision is improved, but processing overhead and system complexity increase
Solution Approach 1:
The patent segments QoS enforcement into hierarchical levels: global QoS policies, application-level QoS parameters, and per-flow QoS metrics. This segmentation allows precise QoE measurement for individual applications while managing system complexity through structured hierarchical control, where each level handles specific aspects of QoS enforcement independently.
Data Source
Figure 1
Figure 2
Figure 3A
AI summary
Methods and apparatus, including computer program products, are provided an application scheduler. Related apparatus, systems, methods, and articles are also described.