MDAS Charging Records for Accurate 5G Analytics Billing
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Solution Overview
Problem
Existing communication systems lack efficient methods for charging Management Data Analytics (MDA) services, particularly in the context of 5G networks, which are not capable of handling the complex processing requirements and diverse analytics types, leading to inadequate resource allocation and billing accuracy.
Innovation Solution
Implementing a Management Data Analytics Service (MDAS) apparatus that includes an interface for charging data records, allowing for real-time or near-real-time output indications, AI/ML model usage, and network condition requirements, along with mechanisms for updating, releasing, and monitoring charging data records to manage resource usage and billing accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Management Data Analytics services are implemented in 5G networks, then analytics capabilities and network optimization are improved, but charging accuracy and resource allocation become inadequate due to complex processing requirements
Solution Approach 1:
The patent segments the charging process into distinct phases: pre-charging (before analytics service execution) and post-charging (after service completion). It divides resource allocation into CPU usage tracking, storage requirements monitoring, and memory size measurement. This segmentation enables precise measurement of each resource type separately, resolving the contradiction between providing comprehensive analytics capabilities and maintaining charging accuracy.
Solution Approach 2:
The patent introduces an intermediary charging data record structure that mediates between the complex MDAS processing requirements and the charging function. This intermediary layer collects and organizes information about CPU usage, storage, and memory requirements, then translates them into accurate charging decisions. The intermediary enables both complex analytics processing and precise charging measurement to coexist.
2Speed
If real-time MDAS output is required, then service responsiveness is improved, but resource consumption and processing complexity increase
Solution Approach 1:
The patent applies preliminary action by collecting and preparing charging-related information before the MDAS service executes. The charging data record is opened in advance with pre-configured parameters for CPU usage, storage requirements, and memory size. This preliminary setup eliminates the need for complex real-time calculations during service execution, maintaining responsiveness while reducing processing complexity.
Solution Approach 2:
The patent implements dynamics by making the charging data record adaptable to different service requirements. The record can be opened with different configurations based on the specific MDAS service type, and can be updated dynamically during service execution. This dynamic approach allows real-time output capability while managing processing complexity through flexible resource allocation.
3Adaptability or versatility
If AI/ML models are used for MDAS, then analytics intelligence is improved, but computational resource requirements and billing complexity increase
Solution Approach 1:
The patent uses copying by creating a standardized charging data record template that can be replicated for different AI/ML models and service types. This template captures all necessary information about computational resource usage, model complexity, and billing parameters in a consistent format. The copying approach simplifies billing complexity while maintaining the ability to handle diverse AI/ML analytics intelligence requirements.
Solution Approach 2:
The patent applies parameter changes by adjusting the charging data record parameters according to the specific AI/ML model requirements. Different models have different computational demands, so the system dynamically modifies parameters such as CPU usage thresholds, storage requirements, and memory allocations. This parameter adaptation enables sophisticated analytics intelligence while managing billing complexity through standardized parameter adjustments.
Data Source
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AI summary
An apparatus comprising means for: providing a Management Data Analytics Service, MDAS, to a second apparatus; sending, to a charging function, a request to open a charging data record for the MDAS, wherein the request to open a charging data record for the MDAS comprises at least one information element describing the MDAS; receiving, from the charging function, a response confirming that the charging data record has been opened.