AI-Driven Network Slice Prediction and Dynamic Resource Allocation
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Solution Overview
Problem
Current network slicing methods require manual creation and management by network operators, which is time-consuming and inefficient, especially in dynamically changing network environments.
Innovation Solution
The use of AI machine learning techniques to automatically predict the need for various types of network slices and generate slice blueprints, allowing for dynamic allocation of resources based on predicted usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual creation and management of network slices is performed by network operators, then network slices can be created with operator knowledge of use cases, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables self-service through AI-driven automatic network slice creation and management. The AI model analyzes traffic patterns and automatically generates slice configurations without requiring manual operator intervention, allowing the network to self-optimize while maintaining operator-defined policies and requirements.
Solution Approach 2:
The patent replaces the manual mechanical process of slice creation with an automated AI-based system. Machine learning models analyze network traffic data and automatically generate slice blueprints, substituting human operators' manual work with intelligent algorithms that can process and respond to changing network conditions in real-time.
2Adaptability or versatility
If manual slice management is performed, then network resources can be provisioned according to operator decisions, but operational overhead is high
Solution Approach 1:
The system implements continuous feedback loops where AI models monitor network traffic patterns, slice performance metrics, and resource utilization. Based on this feedback, the system automatically adjusts slice configurations and resource allocation, enabling dynamic adaptation to changing network conditions while reducing manual management complexity.
Solution Approach 2:
The AI-driven platform provides universal management capabilities that handle multiple network slice types, traffic patterns, and service requirements through a single automated system. This multi-functional approach consolidates various management tasks into one intelligent platform, reducing overall operational complexity while maintaining flexibility.
3Reliability
If network slices are created manually based on current needs, then slices can be configured for specific use cases, but the system cannot adapt to dynamically changing network environments
Solution Approach 1:
The system transitions from static manual slice configuration to dynamic automatic adaptation. AI models continuously analyze changing traffic patterns and network conditions, automatically adjusting slice configurations in real-time. This dynamic approach maintains reliable service quality while adapting to evolving network environments without manual intervention.
Solution Approach 2:
The AI system performs preliminary analysis of traffic patterns and predicts future network needs before actual changes occur. By proactively identifying emerging traffic patterns and potential bottlenecks, the system pre-configures or adjusts slices in advance, ensuring reliable service quality while maintaining adaptability to changing conditions.
Data Source
AI summary
Methods and apparatus for using an artificial intelligence engine to automatically predict what network slices will be needed are described. After the network slice prediction is made network slices are automatically generated in accordance with AI machine learned prediction. This new automated approach ensures the network slices are up to date and devices assigned the network slices properly as the new devices get activated on the network. This new approach can also add elasticity to the network slices based on the devices assigned to them by changing the amount of resources allocated to slices of a particular type as predicted resource needs change.