Charging System Correlation for Application Identification
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Solution Overview
Problem
Current charging systems for over-the-top applications in 3G networks face challenges in accurately identifying and charging for different applications sharing the same IP address, leading to difficulties in application-based charging due to the inability to distinguish between them at the IP layer.
Innovation Solution
A method that involves acquiring and utilizing correlation identifiers for data flows by Traffic Detection Function (TDF) and Policy and Charging Enforcement Function (PCEF) entities, which are then used by the charging system to perform correlation processing on charging information, allowing for accurate charging of both data flows and applications simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IP layer identification is used for application charging, then charging control can be implemented, but different applications sharing the same IP address cannot be distinguished
Solution Approach 1:
The patent segments the identification process into two parts: IP layer identification (performed by PCEF) and application layer identification (performed by TDF). The TDF deep packet inspection capability segments traffic into application-specific flows, allowing differentiation of applications sharing the same IP address. This segmentation enables precise application identification without requiring the PCEF to perform complex deep packet inspection.
Solution Approach 2:
The TDF acts as an intermediary between the PCEF and the charging system. It receives traffic flow information from the PCEF, performs deep packet inspection to identify applications, and returns application-specific charging information. This intermediary approach allows the system to achieve application-level charging precision without burdening the PCEF with complex identification logic.
2Measurement precision
If deep packet inspection is performed by PCEF to identify applications, then application-based charging can be achieved, but system complexity and processing overhead increase
Solution Approach 1:
The patent extracts the deep packet inspection function from the PCEF and places it in the TDF. The PCEF only performs basic IP layer identification and forwards traffic flow information to the TDF. The TDF then performs the computationally intensive deep packet inspection to identify applications. This extraction reduces PCEF complexity while maintaining application identification accuracy.
Solution Approach 2:
The TDF serves as an intermediary that handles the complex deep packet inspection task. It receives traffic information from PCEF, performs application identification, and returns results to the charging system. This intermediary architecture allows the PCEF to remain simple while achieving precise application-based charging through the TDF's inspection capabilities.
3Adaptability or versatility
If separate charging information is collected by TDF and PCEF, then both application and data flow charging can be supported, but correlation processing complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the TDF receives traffic flow information from the PCEF, performs application identification, and returns application-specific charging information that is correlated with the original traffic flow. The charging system uses this feedback loop to match application charging information with data flow charging information through correlation identifiers, enabling flexible charging models while managing complexity through structured information exchange.
Solution Approach 2:
The charging system is designed with universal correlation processing capability that can handle both application-based charging and data flow-based charging through the same correlation identifier mechanism. This multi-functional approach allows the system to support multiple charging models (application charging, data flow charging, or combination) without requiring separate processing paths, thereby reducing overall system complexity.
Data Source
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AI summary
The present invention discloses a charging method, a TDF entity, a PCEF entity, a charging system, a PCRF entity, and a system. The method includes: acquiring, by a TDF entity, a correlation identifier of a data flow of an application borne by the TDF entity; and sending, by the TDF entity, first charging information to a charging system, where the first charging information carries the correlation identifier of the data flow of the application borne by the TDF entity, so that the charging system performs, according to the correlation identifier of the data flow of the application borne by the TDF entity and a correlation identifier of a data flow borne by a PCEF entity, correlation processing for the first charging information and second charging information that is sent by the PCEF entity to the charging system, where the second charging information carries the correlation identifier of the data flow borne by the PCEF entity. In embodiments of the present invention, a correlation identifier is carried in charging information, so that correlation processing may be performed for charging information of a data flow and charging information of an application. Thereby, accurate charging can be performed for the data flow and the application.