Network Slicing Engine for Cross-Layer QoS Routing
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Solution Overview
Problem
Managing network traffic in heterogeneous multi-vendor environments is complex due to different wireless communication schemes (e.g., WiFi, 4G, 5G, radar) and interference, leading to QoS and QoE issues.
Innovation Solution
Implementing an Intelligent Network Slicing and Policy-based Routing Engine (INSPiRE) module with a Cross Layer Cognitive Communications Architecture (CLAIRE) to automate interference and congestion mitigation, enabling network slicing and policy-based routing across heterogeneous networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network slicing and policy-based routing is implemented to manage heterogeneous multi-vendor networks, then network resource availability and QoS are improved, but device complexity increases
Solution Approach 1:
The patent divides the heterogeneous network into multiple network slices, each dedicated to specific services or user groups with distinct QoS requirements. This segmentation allows independent management and optimization of each slice while simplifying overall network control through standardized slicing mechanisms.
Solution Approach 2:
The patent introduces a network slicing engine as an intermediary component that mediates between network resources and service requirements. This engine automatically manages slice allocation, routing decisions, and resource allocation, reducing the complexity burden on individual network nodes while ensuring reliable resource availability.
2Ease of operation
If automated interference and congestion mitigation is implemented, then QoE is improved, but device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where network nodes continuously monitor interference and congestion conditions, report status to the slicing engine, and receive dynamic routing adjustments. This closed-loop feedback enables automated QoE optimization through policy-based routing decisions that respond to real-time network conditions.
Solution Approach 2:
The patent enables network nodes to autonomously perform local optimizations by executing pre-configured policies and making self-adjustments based on monitored conditions. The automated mitigation system allows nodes to serve themselves through intelligent decision-making algorithms, reducing the need for complex centralized control while improving QoE.
3Adaptability or versatility
If dynamic network slice assignment and routing is implemented, then adaptability to changing conditions is improved, but network management complexity increases
Solution Approach 1:
The patent implements dynamic network slice assignment where slices can be created, modified, or terminated based on real-time demand and network conditions. The routing paths are dynamically adjusted rather than static, allowing the network to adapt to changing conditions through automated policy-based decisions while maintaining manageable complexity through standardized dynamic protocols.
Data Source
AI summary
One or more aspects of the present disclosure are directed to network optimization solutions provided as software agents (applications) executed on network nodes in a heterogenous multi-vendor environment to provide cross-layer network optimization and ensure availability of network resources to meet associated Quality of Experience (QoE) and Quality of Service (QoS). In one aspect, a network slicing engine is configured to receive at least one request from at least one network endpoint for access to the heterogeneous multi-vendor network for data transmission; receive information on state of operation of a plurality of communication links between the plurality of nodes; determine a set of data transmission routes for the request; assign a network slice for serving the request; determine, from the set of data transmission routes, an end-to-end route for the network slice; and send network traffic associated with the request using the network slice and over the end-to-end route.