Core Network Task Scheduling for Distributed Sensing Data Processing
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Solution Overview
Problem
Future communication systems require enhanced processing capacity for sensing data to meet high-quality requirements, particularly in emerging vertical applications like smart cities and smart transportation, where communication and sensing technologies need deep integration for efficient and deployable network models.
Innovation Solution
A network architecture that collects sensing data from third-party application functions and distributes computing tasks to computing power nodes for processing, utilizing a scheduler, input, calculation, output, and storage network functions to enhance data processing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensing data is collected from third-party AF entities and processed in the core network, then the processing capacity for sensing data is enhanced, but the device complexity and network architecture complexity increase
Solution Approach 1:
The patent segments the sensing data processing system into distinct functional modules: a sensing data collection module that gathers data from third-party AF entities, a task distribution module that allocates computing tasks to computing power nodes, and a result aggregation module that collects processing results. This segmentation allows each module to perform its function independently, enhancing processing capacity while managing complexity through modular design.
Solution Approach 2:
The patent introduces a core network device as an intermediary between third-party AF entities and computing power nodes. This intermediary manages the collection of sensing data, formulates computing tasks, distributes them to appropriate computing nodes, and aggregates results. The intermediary abstracts the complexity from both data sources and computing nodes, enabling scalable processing capacity without proportionally increasing overall system complexity.
2Productivity
If computing tasks are distributed to multiple computing power nodes, then the processing efficiency is improved, but the coordination and management complexity increases
Solution Approach 1:
The patent implements dynamic task distribution where the core network device adapts computing task allocation based on real-time conditions of computing power nodes. The system can dynamically select which nodes receive tasks, adjust task parameters, and rebalance workloads as nodes become available or unavailable. This dynamic approach improves processing efficiency by utilizing available resources effectively while the centralized coordination logic manages complexity by making decisions based on current system state rather than requiring complex pre-coordination between all nodes.
Data Source
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AI summary
Embodiments of the present disclosure disclose a data processing method and apparatus, a core network device and a computer readable storage medium, relating to the technical field of communications. The method comprises: distributing computing tasks corresponding to a perception dataset to at least one computing power node, the perception data in the perception dataset originating from a third-party application functional entity AF; receiving calculation results fed back by the at least one computing power node. An embodiment of the present disclosure, by means of collecting perception data originating from a third-party AF, distributing corresponding computing tasks to at least one computing power node for calculation, and receiving calculation results fed back by the computing power node, can improve perception data processing capability, and can satisfy high quality requirements for the perception data and computing power in a future communication system.