On-Demand Network Slice Overlay Optimization for Dynamic Load Adjustment
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Solution Overview
Problem
Current network slicing systems in WLAN and WWAN networks lack the ability to dynamically adjust network slices in real-time to meet changing data throughput and quality-of-service requirements, particularly during events like athletic events, due to a lack of real-time visibility and telemetry data from endpoint devices.
Innovation Solution
An on-demand network slice overlay optimization system that collects load profile metrics from endpoint devices and core network capacity data to dynamically adjust network slices and capacity based on client business policies, ensuring flexible resource allocation and cost optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network slices are statically allocated to ensure persistent connectivity, then reliability is improved, but resource utilization efficiency deteriorates due to over-provisioning during low-demand periods
Solution Approach 1:
The patent implements dynamic network slice allocation that automatically adjusts slice capacity and provisioning based on real-time telemetry data from endpoint devices. The system transitions from static to dynamic slice management, using machine learning models to predict demand patterns and adjust resource allocation accordingly, ensuring persistent connectivity while optimizing resource utilization during varying demand conditions
Solution Approach 2:
The system employs continuous feedback loops where telemetry data from endpoint devices is collected, analyzed, and used to adjust network slice configurations in real-time. The feedback mechanism monitors actual usage patterns and connects this information back to the slice management system, enabling automatic adjustments to maintain connectivity reliability while preventing resource waste
2Productivity
If network slices are dynamically adjusted to meet changing demand, then resource allocation efficiency is improved, but system complexity increases due to real-time monitoring and adjustment mechanisms
Solution Approach 1:
The patent implements self-service automation where the network slice management system autonomously monitors telemetry data, predicts demand using machine learning models, and adjusts slice configurations without manual intervention. The system serves itself by automatically detecting when reconfiguration is needed and executing the adjustments, reducing operational complexity while maintaining high resource allocation efficiency
Solution Approach 2:
The system performs preliminary actions by using machine learning models to predict future demand patterns based on historical and current telemetry data. Before actual demand changes occur, the system pre-adjusts network slice configurations to anticipate upcoming needs, simplifying real-time decision-making while optimizing resource allocation efficiency
3Adaptability or versatility
If real-time telemetry data is collected from endpoint devices to enable dynamic slice adjustment, then adaptability is improved, but data collection and processing overhead increases
Solution Approach 1:
The patent extracts only the essential telemetry data parameters needed for network slice management decisions from endpoint devices, rather than collecting comprehensive device information. The system identifies and collects specifically relevant metrics related to network usage patterns, filtering out unnecessary data to reduce collection overhead while maintaining real-time adaptability for slice adjustments
Data Source
AI summary
An information handling system executing an on-demand network slice overlay optimization system includes a processor executing the on-demand network slice overlay optimization system to receive wireless network load profile metrics from a regional edge device and the endpoint load profile metrics; receive core metrics from a radio access network (RAN) service provider descriptive of the load capacity of a core network to provide data throughput at a given time; determine whether the real-time data load demand of the grouped plurality of endpoint devices can be provisioned to the load capacity described in the core metrics; and provide instructions to dynamically adjust network slices at the regional edge device to provide for any changes in the real-time data load demand based on an elastic client business policy enforced at the regional edge device for wireless connectivity data for the grouped plurality of endpoint devices.