End-to-End Network Slice Management Service for Dynamic Resource Allocation
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Solution Overview
Problem
Current network slice management systems face challenges in dynamically allocating resources to support diverse application services and quality of service requirements due to unpredictable mobile user activities, radio channel quality variations, and limited RAN and core network resources, leading to inefficiencies and potential security issues.
Innovation Solution
An end-to-end network slice management service that includes a network slice profile service, metric predictor service, resource predictor service, mapping service, and resource scheduler service, which uses predictive analytics and scheduling measures to manage network slices across RAN, core, and application layers, enabling dynamic adaptation and isolation between slices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network slices are configured according to a one-to-one basis with dedicated network slices for each application service, then service quality and reliability are improved, but device complexity and network overhead increase significantly
Solution Approach 1:
The patent implements a shared network slice framework where a single physical network infrastructure supports multiple application services simultaneously. The network slice manager dynamically allocates and manages shared network resources (bandwidth, computing power, storage) to serve multiple services including augmented reality, autonomous driving, and telemedicine without requiring separate dedicated networks for each application.
2Adaptability or versatility
If dynamic resource allocation is implemented to adapt to unpredictable mobile user activities and radio channel variations, then adaptability and service quality are improved, but device complexity and control difficulty increase
Solution Approach 1:
The patent implements a feedback mechanism where the network slice manager continuously monitors network conditions including user activities, radio channel quality, and service performance metrics. Based on this real-time feedback, the system dynamically adjusts resource allocation decisions to maintain optimal service quality while adapting to changing conditions without requiring complex manual intervention.
Solution Approach 2:
The network slice manager operates autonomously to make real-time resource allocation decisions based on monitored network conditions and service requirements. The system self-adjusts without external intervention, automatically allocating network resources to different services based on current priorities and conditions, thereby reducing control complexity while maintaining high adaptability.
3Productivity
If predictive analytics and scheduling measures are implemented to manage network slices, then productivity and service level agreement compliance are improved, but use of energy and computational resources increase
Solution Approach 1:
The patent implements predictive analytics that analyze historical network data and user behavior patterns to forecast future network conditions and service requirements. The network slice manager uses these predictions to proactively allocate resources before demand peaks occur, ensuring service level agreements are met while avoiding the need for excessive reactive computational processing during high-demand periods.
Data Source
AI summary
A method, a device, and a non-transitory storage medium are described in which an end-to-end network slice management service is provided. The service may map application services to network slices based on network profiles, and may generate mapping information. The mapping information may indicate network slice portion-to-application service mappings, and end-to-end mappings between a network slice and the application service. The service may calculate metric values pertaining to networks and associated network slices, and calculate prospective metric values for the networks and network slices based on the metric values. The service may estimate resources allocation values associated with resources to support the prospective metric values based on the prospective metric values and the mapping information. The service may calculate a schedule and assignment of the resources, and provide them to the networks.