Multi-Access QoS Orchestration Using Predictive Network Selection
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Solution Overview
Problem
Achieving optimal Quality of Service (QoS) in heterogeneous network environments is challenging due to varying network types and dynamic traffic, requiring a solution that balances performance, cost efficiency, and scalability while managing network resources effectively.
Innovation Solution
A method and system for end-to-end QoS in Multi-Access Heterogeneous Networks (MAHN) that includes selecting networks based on QoS requests, using a prediction model, and implementing network-specific interfaces to manage network resources dynamically, ensuring compatibility and optimal utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If QoS management is implemented across heterogeneous networks, then service reliability is improved, but system complexity increases due to varying network types and protocols
Solution Approach 1:
The patent introduces a QoS management entity that acts as an intermediary between applications and heterogeneous networks. This entity translates diverse QoS requirements from different network types (5G, Wi-Fi, Ethernet) into standardized policies, mediating the complexity rather than allowing it to propagate through the entire system. The intermediary handles network-specific protocols and characteristics centrally, simplifying the overall architecture while maintaining reliable QoS across diverse networks.
Solution Approach 2:
The QoS management entity is designed with universal functionality to handle multiple network types and protocols through a single unified interface. It can manage QoS for 5G, Wi-Fi, Ethernet, and other heterogeneous networks using the same core mechanisms (traffic prioritization, bandwidth allocation, latency control), eliminating the need for separate management systems for each network type and reducing overall system complexity.
2Reliability
If dynamic network selection is implemented based on QoS parameters, then service performance is improved, but processing time increases due to real-time analysis requirements
Solution Approach 1:
The system performs preliminary QoS assessments and network evaluations in advance before actual data transmission begins. By pre-analyzing network conditions, predicting performance metrics, and establishing QoS policies beforehand, the system reduces the processing time required during real-time operations. The QoS management entity prepares translation rules and routing decisions in advance, so when data needs to be transmitted, the optimal path is already determined.
Solution Approach 2:
The QoS management system dynamically adjusts its operation mode based on real-time conditions. For time-sensitive applications, it uses pre-established rules and cached network information to make rapid decisions. For less time-critical scenarios, it performs more thorough real-time analysis. This dynamic behavior allows the system to balance processing time and service performance according to actual needs, rather than always operating at maximum analysis depth.
3Productivity
If network resource allocation is optimized for specific applications, then application performance is improved, but network scalability deteriorates due to resource reservation overhead
Solution Approach 1:
The system optimizes network resource allocation by dynamically changing QoS parameters (bandwidth, latency, jitter thresholds) based on application requirements without permanently reserving resources. When an application needs enhanced performance, the QoS management entity adjusts parameters temporarily for that specific flow or session. When the application releases resources or requirements change, parameters are adjusted back, allowing the same network infrastructure to serve multiple different applications with varying needs, thus maintaining scalability while improving performance for active applications.
Data Source
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AI summary
Disclosed is a system and a method for end-to-end QoS over Multi-Access Heterogeneous Networks (MAHN). The system and method implement an Application Enablement Platform (AEP) such that the AEP receives a request from an application of one or more applications, wherein the request is one of, a Quality on Demand (QoD) request or a proactive QoS request. Further, the AEP selects at least one network of a plurality of networks for the application based on the request, wherein when (i) the request is a QoD request, the selection of the at least one network is based on one of, a recommended network or a preferred network and (ii) when the request is a proactive QoS request, the selection of the at least one network is based on one more QoS parameters and a prediction model; and establishes a communication path between the at least one network and the application.