Network Slicing for Dynamic Resource Allocation
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Solution Overview
Problem
Next-generation mobility networks, such as 5G, face challenges in efficiently managing diverse services like M2M, IoT, AR/VR, and connected cars due to high resource demands for aggregate bit rates, low latencies, and varying device capabilities, which traditional centralized architectures struggle to meet effectively.
Innovation Solution
The implementation of network slicing technology, which allows for the dynamic reconfiguration of network resources into virtual slices, enabling intelligent pairing of RAN and CN slices to adapt to changing demands and optimize resource allocation in real-time, using AI/ML and network analytics for automated slice design and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional centralized network architecture is used, then network management is simplified, but the system cannot meet diverse service requirements for aggregate bit rates, latency, and device capabilities
Solution Approach 1:
The patent applies segmentation by dividing the network into multiple network slices, each optimized for specific service requirements. The network architecture is segmented into RAN slices and CN slices that can be independently configured and managed, allowing each slice to handle different service types (e.g., eMBB, URLLC, mMTC) with tailored resource allocation, performance parameters, and quality of service settings.
Solution Approach 2:
The patent implements dynamics through automated slice design and management systems that dynamically create, configure, and modify network slices based on real-time service demands. The system continuously monitors network conditions and automatically adjusts slice parameters, resource allocation, and routing policies to adapt to changing traffic patterns and service requirements without manual intervention.
2Reliability
If network slicing is implemented to meet diverse service demands, then service-specific optimization is achieved, but resource allocation complexity increases
Solution Approach 1:
The patent applies self-service through automated slice design systems that autonomously perform resource allocation, slice configuration, and optimization without requiring manual network operator intervention. The system automatically monitors service quality metrics, identifies allocation imbalances, and adjusts resource distribution across slices based on real-time demand, enabling the network to self-optimize and maintain service quality while reducing operational complexity.
Solution Approach 2:
The patent implements feedback mechanisms where the automated management system continuously monitors network performance metrics, service quality indicators, and resource utilization across all slices. This feedback information is used to dynamically adjust resource allocation policies, slice configurations, and routing decisions, creating a closed-loop control system that maintains optimal service quality while adapting to changing conditions.
3Productivity
If manual network design and configuration is used, then implementation control is maintained, but the system cannot respond quickly to changing service demands
Solution Approach 1:
The patent replaces manual mechanical configuration processes with automated electronic systems. The slice design and management platform uses software-based automation to generate slice configurations, allocate resources, and provision network services electronically, eliminating the need for manual network equipment configuration and significantly accelerating the pace of network deployment and service activation.
Solution Approach 2:
The patent applies preliminary action by pre-configuring slice templates and resource allocation policies that can be rapidly instantiated when service demands arise. The system maintains ready-to-deploy slice configurations and resource pools that can be automatically activated and customized based on service requirements, enabling rapid service provisioning without requiring time-consuming manual design and configuration processes.
Data Source
AI summary
Initiation of a network slice event is disclosed. The network slice event can be initiated in response to, and according to a determined a network slice event instruction. The network slice event can result in modification of network slices of a network. The modification of the network slices can correspond to a change in the performance of the network. The modification of the network slices can comprise adding a new slice, removing an existing slice, adapting an existing slice, etc. Artificial intelligence, machine learning, etc., can be employed to provide an inference related to determining the network slice event instruction. The slice event can be implemented via a network controller, for example an ONAP component, based on the network slice event instruction.