5G Network Slice Endpoint Selection Using Real-Time Transport Telemetry
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 5G network slicing technologies select endpoints without considering real-time topology information, leading to suboptimal placement that may not meet Service Level Objectives (SLOs) and Service Level Expectations (SLEs), resulting in inaccurate network slice placement.
Innovation Solution
A transport controller evaluates candidate endpoints and pathways using real-time telemetry data and machine learning models to determine optimal placement based on SLO/SLE constraints and intent, dynamically selecting the best endpoints for network slices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If endpoints are selected statically by the end-to-end controller without real-time topology information, then the network slice placement decision is made quickly, but the placement accuracy and ability to meet SLO/SLE constraints deteriorates
Solution Approach 1:
The system divides the endpoint selection function into two parts: the end-to-end controller provides candidate endpoints based on service intent, and the transport controller selects the final endpoint based on real-time topology information. This segmentation allows each controller to focus on its strength while improving overall placement accuracy.
Solution Approach 2:
The transport controller acts as an intermediary between the end-to-end controller and the network slice placement. It receives candidate endpoints from the end-to-end controller, evaluates them against real-time topology information and SLO/SLE constraints, and selects the optimal endpoint. This intermediary role resolves the contradiction by adding intelligence without overcomplicating the end-to-end controller.
2Reliability
If real-time topology information is used to dynamically evaluate candidate endpoints, then the ability to meet SLO/SLE constraints improves, but the system complexity and evaluation time increases
Solution Approach 1:
The end-to-end controller performs preliminary action by identifying and providing candidate endpoints that satisfy service intent before the transport controller evaluates them against real-time topology information. This preliminary filtering reduces the number of endpoints the transport controller must evaluate, maintaining reliability while reducing evaluation time.
Solution Approach 2:
The system implements dynamic endpoint selection where the transport controller continuously monitors topology information and adjusts endpoint selection in real-time. This dynamic approach ensures SLO/SLE constraints are met while the system adapts to changing network conditions, balancing reliability with efficient decision-making.
3Productivity
If the end-to-end controller makes placement decisions without transport network feedback, then the decision-making process is simple, but the selected endpoints may not represent the optimal pathway
Solution Approach 1:
The transport controller provides feedback to the end-to-end controller about the feasibility and quality of candidate endpoints based on real-time topology information and SLO/SLE constraint evaluation. This feedback loop enables the system to maintain simple decision-making at the end-to-end controller while achieving high pathway optimization accuracy through transport controller insights.
Data Source
AI summary
This disclosure describes techniques and mechanisms for enabling an end-to-end controller of a network to offload decisions about placement of network slices to the transport controller. By doing so, the transport controller can intelligently determine optimal placement based on intent (e.g., external intent of the end-to-end controller and internal intent of the transport controller), internal transport network analytics functions, SLO/SLE constraints, real-time telemetry data, and more to provide dynamic and optimal placement of network slices. That is the described techniques dynamically utilize information from within the transport domain to define the placement of network slices. Thus, placements are more accurate, thereby reducing latency and improving functioning of the network, as well as providing an improved user experience by meeting SLO/SLE constraints.